Unlocking the tumor-immune microenvironment in osteosarcoma: insights into the immune landscape and mechanisms.

Osteosarcoma has a unique tumor microenvironment (TME), which is characterized as a complex microenvironment comprising of bone cells, immune cells, stromal cells, and heterogeneous vascular structures. These elements are intricately embedded in a mineralized extracellular matrix, setting it apart from other primary TMEs. In a state of normal physiological function, these cell types collaborate in a coordinated manner to maintain the homeostasis of the bone and hematopoietic systems. However, in the pathological condition, i.e., neoplastic malignancies, the tumor-immune microenvironment (TIME) has been shown to promote cancer cells proliferation, migration, apoptosis and drug resistance, as well as immune escape. The intricate and dynamic system of the TIME in osteosarcoma involves crucial roles played by various infiltrating cells, the complement system, and exosomes. This complexity is closely associated with tumor cells evading immune surveillance, experiencing uncontrolled proliferation, and facilitating metastasis. In this review, we elucidate the intricate interplay between diverse cell populations in the osteosarcoma TIME, each contributing uniquely to tumor progression. From chondroblastic and osteoblastic osteosarcoma cells to osteoclasts, stromal cells, and various myeloid and lymphoid cell subsets, the comprehensive single-cell analysis provides a detailed roadmap of the complex osteosarcoma ecosystem. Furthermore, we summarize the mutations, epigenetic mechanisms, and extracellular vesicles that dictate the immunologic landscape and modulate the TIME of osteosarcoma. The perspectives of the clinical implementation of immunotherapy and therapeutic approaches for targeting immune cells are also intensively discussed.

Development, study, and comparison of models of cross-immunity to the influenza virus using statistical methods and machine learning.

INTRODUCTION: The World Health Organization considers the values of antibody titers in the hemagglutination inhibition assay as one of the most important criteria for assessing successful vaccination. Mathematical modeling of cross-immunity allows for identification on a real-time basis of new antigenic variants, which is of paramount importance for human health. MATERIALS AND METHODS: This study uses statistical methods and machine learning techniques from simple to complex: logistic regression model, random forest method, and gradient boosting. The calculations used the AAindex matrices in parallel to the Hamming distance. The calculations were carried out with different types and values of antigenic escape thresholds, on four data sets. The results were compared using common binary classification metrics. RESULTS: Significant differentiation is shown depending on the data sets used. The best results were demonstrated by all three models for the forecast autumn season of 2022, which were preliminary trained on the February season of the same year (Auroc 0.934; 0.958; 0.956, respectively). The lowest results were obtained for the entire forecast year 2023, they were set up on data from two seasons of 2022 (Aucroc 0.614; 0.658; 0.775). The dependence of the results on the types of thresholds used and their values turned out to be insignificant. The additional use of AAindex matrices did not significantly improve the results of the models without introducing significant deterioration. CONCLUSION: More complex models show better results. When developing cross-immunity models, testing on a variety of data sets is important to make strong claims about their prognostic robustness.

Neutrophil-related Signature Characterizes Immune Landscape and Predicts Prognosis of Invasive Breast Cancer.

As a leading prevalent malignancy, breast cancer remains a significant worldwide health issue. Recent research indicates that neutrophils play a crucial role in breast cancer development. The prognostic significance of neutrophil-related genes (NRGs) or the immune landscape of the neutrophil-related signature in invasive breast cancer (IBC) is, nevertheless, unknown. To uncover innovative therapy alternatives, the significance of the neutrophil-related signatures in IBC was evaluated here. Briefly, a prediction model based on neutrophil-related core prognostic genes and The Cancer Genome Atlas data was created (TCGA). The model may assess IBC patients' prognosis. The IBC data from the Gene Expression Omnibus (GEO) confirmed the prognostic accuracy of the model. The overall survival (OS) of patients was worse in the group with a high NRGs score compared to the group with a low NRGs score. In addition, patients with low NRGs scores were considerably more sensitive to vinorelbine, cyclophosphamide, epirubicin, gemcitabine, paclitaxel, 5-fluorouracil, docetaxel, and cisplatin. Patients with low NRGs scores responded better to CTLA-4 and PD-1 treatments. Additionally, the immune microenvironment components were more abundant in patients with low NRGs scores. Moreover, qRT-PCR results confirmed that LEF1 had a higher expression level in tumor samples compared to normal samples, whereas NRG1 and STX11 exhibited lower expression levels in tumor samples than in normal samples. These results suggest that NRGs might be utilized as biomarkers to predict the prognosis of individuals with IBC, thereby paving the way for the creation of customized therapies for IBC.

Integrative multi-omics analysis unveils the connection between transcriptomic characteristics associated with mitochondria and the tumor immune microenvironment in lower-grade gliomas.

Lower-grade gliomas (LGGs) exhibit diverse clinical behaviors and varying immune infiltration levels. Mitochondria have been implicated in numerous cancer pathogenesis and development, including LGGs. However, the precise biological functions of mitochondrial genes in shaping the immune landscape and the prognostic significance of LGGs remain elusive. Utilizing the Mito-Carta3.0 database, we curated a total of 1136 genes implicated in mitochondrial functions. By leveraging the expression profiles of 1136 genes related to mitochondria, we successfully categorized LGGs into four distinctive mitochondria-related transcriptome (MRT) subtypes. Our thorough analysis conclusively demonstrated that these subtypes exhibited marked disparities. To enable a personalized and integrated evaluation of LGG patients, we developed a prognostic signature known as MRT-related prognostic signature (MTRS). MTRS demonstrated correlation with mitochondria-related transcriptome (MRT) subtypes, allowing the assessment of patients' prognosis and immune microenvironment. We conducted a detailed exploration of the single-cell distribution of MTRS in lower-grade gliomas and verified the core genes of MTRS within the spatial transcriptome of these tumors. Furthermore, our study pinpointed MGME1 as the pivotal gene in the model, functioning as an oncogene that exerts influence on cell proliferation and migration capabilities. Our research highlights the importance of mitochondrial transcriptomic features in LGGs, offering paths for tailored therapies.

Immunological dynamics in MASH: from landscape analysis to therapeutic intervention.

Metabolic dysfunction-associated steatohepatitis (MASH), previously known as nonalcoholic steatohepatitis (NASH), is a multifaceted liver disease characterized by inflammation and fibrosis that develops from simple steatosis. Immune and inflammatory pathways have a central role in the pathogenesis of MASH, yet, how to target immune pathways to treat MASH remains perplexed. This review emphasizes the intricate role that immune cells play in the etiology and pathophysiology of MASH and highlights their significance as targets for therapeutic approaches. It discusses both current strategies and novel therapies aimed at modulating the immune response in MASH. It also highlights challenges in liver-specific drug delivery, potential off-target effects, and difficulties in targeting diverse immune cell populations within the liver. This review is a comprehensive resource that integrates current knowledge with future perspectives in the evolving field of MASH, with the goal of driving forward progress in medical therapies designed to treat this complex liver disease.

Potential tumor-specific antigens and immune landscapes identification for mRNA vaccine in thyroid cancer.

Background: Tumor mRNA vaccines have been identified as a promising technology for cancer therapy in multiple cancer types, while their efficacy in thyroid cancer (THCA) is unclear. Immunotyping is strongly associated with the immune microenvironment and immune status in cancer, thus it is important in vaccination and therapeutic response. This study is to identify potential valuable antigens and novel immune subtypes of THCA for immune landscape construction, thus screening patients suitable for mRNA vaccination. Methods: The clinical information and gene expression files of 568 THCA cases were obtained from the TCGA dataset. The DNA copy number variation and the somatic mutation of THCA were visualized by the cBioPortal database. TIMER was used to investigate the immune infiltrating correlation with candidate antigens. Consensus clustering analysis was conducted to cluster data using the ConsensusClusterPlus package. The immune landscapes of THCA patients were visualized using the Monocle package. The critical hub genes for THCA mRNA vaccines were identified by WGCNA package. To validate, the immunohistochemistry and real-time quantitative PCR (RT-qPCR) were performed to detect the expression level of potential antigen for mRNA vaccine in tissue and cell lines in THCA. Results: Thymidine kinase 1 (TK1) was identified as a potential biomarker of mRNA vaccine against THCA. It was confirmed to be significantly upregulated in THCA tissues and cells lines. Moreover, three novel immune subtypes of THCA were obtained based on the expression consistency of immune-associated genes. The S2 subtype was characterized as an immunological "cold" phenotype with a high expression of immunogenic cell death modulators. S1 and S3 subtypes were immunological "hot" phenotypes with immune checkpoints upregulation. Further, the immune landscape of THCA patients was visualized and ten hub genes for mRNA vaccines were identified. Conclusion: TK1 was a tumor-specific antigen of mRNA vaccines. The patients belonging to the S2 subtype ("cold" tumor) were suitable for mRNA vaccine therapy in THCA. Notably, ten hub genes were conducted as potential biomarkers for identifying suitable patients for mRNA vaccination. These findings provided novel insights into mRNA vaccine development against THCA.

Unraveling the immune landscape of lung adenocarcinoma: insights for tailoring therapeutic approaches.

Lung adenocarcinoma (LUAD), a prevalent type of non-small cell lung cancer (NSCLC), was known for its diversity and intricate tumor microenvironment (TME). Comprehending the interaction among human immune-related genes (IRGs) and the TME is vital in the creation of accurate predictive models and specific treatments. We created a risk score based on IRGs and designed a nomogram to predict the prognosis of LUAD accurately. This involved a thorough examination of TME and the infiltration of immune cells in both high-risk and low-risk LUAD groups. Furthermore, the examination of the association between characteristic genes (BIRC5 and BMP5) and immune cells, along with immune checkpoints in the TME, was also conducted. The findings of our research unveiled unique immune profiles and interactions among individuals in the high- and low-risk categories, which contribute to variations in prognosis. LUAD demonstrated significant associations between BIRC5, BMP5, immune cells, and checkpoints, suggesting their involvement in disease advancement and resistance to medication. Furthermore, by correlating our findings with a multidrug database, we identified specific LUAD patient subsets that might benefit from tailored treatments. Our study establishes a groundbreaking prognostic model for LUAD, which not only underscores the importance of the immune context in LUAD but also paves the way for advancing precision medicine strategies in this complex malignancy.

A novel molecular classification based on efferocytosis-related genes for predicting clinical outcome and treatment response in acute myeloid leukemia.

BACKGROUND: Previous studies have shown that macrophage-mediated efferocytosis is involved in immunosuppression in acute myeloid leukemia (AML). However, the regulatory role of efferocytosis in AML remains unclear and needs further elucidation. METHODS: We first identified the key efferocytosis-related genes (ERGs) based on the expression matrix. Efferocytosis-related molecular subtypes were obtained by consensus clustering algorithm. Differences in immune landscape and biological processes among molecular subtypes were further evaluated. The efferocytosis score model was constructed to quantify molecular subtypes and evaluate its value in prognosis prediction and treatment decision-making in AML. RESULTS: Three distinct efferocytosis-related molecular subtypes were identified and divided into immune activation, immune desert, and immunosuppression subtypes based on the characteristics of the immune landscape. We evaluated the differences in clinical and biological features among different molecular subtypes, and the construction of an efferocytosis score model can effectively quantify the subtypes. A low efferocytosis score is associated with immune activation and reduced mutation frequency, and patients have a better prognosis. A high efferocytosis score reflects immune exhaustion, increased activity of tumor marker pathways, and poor prognosis. The prognostic predictive value of the efferocytosis score model was confirmed in six AML cohorts. Patients exhibiting high efferocytosis scores may derive therapeutic benefits from anti-PD-1 immunotherapy, whereas those with low efferocytosis scores tend to exhibit greater sensitivity towards chemotherapy. Analysis of treatment data in ex vivo AML cells revealed a group of drugs with significant differences in sensitivity between different efferocytosis score groups. Finally, we validated model gene expression in a clinical cohort. CONCLUSIONS: This study reveals that efferocytosis plays a non-negligible role in shaping the diversity and complexity of the AML immune microenvironment. Assessing the individual efferocytosis-related molecular subtype in individuals will help to enhance our understanding of the characterization of the AML immune landscape and guide the establishment of more effective clinical treatment strategies.

Investigating immune dysregulation and hub genes in septic cardiomyopathy development.

Septic cardiomyopathy is a life-threatening heart dysfunction caused by severe infection. Considering the complexity of pathogenesis and high mortality, the identification of efficient biomarkers are needed to guide clinical practice. Based on multimicroarray analysis, this study aimed to explore the pathogenesis of septic cardiomyopathy and the related immune landscape. The results showed that septic cardiomyopathy resulted in organ dysfunction due to extreme pro- and anti-inflammatory effects. In this process, KLRG1, PRF1, BCL6, GAB2, MMP9, IL1R1, JAK3, IL6ST, and SERPINE1 were identified as the hub genes regulating the immune landscape of septic cardiomyopathy. Nine transcription factors regulated the expression of these genes: SRF, STAT1, SP1, RELA, PPARG, NFKB1, PPARA, SMAD3, and STAT3. The hub genes activated the Th17 cell differentiation pathway, JAK-STAT signaling pathway, and cytokine‒cytokine receptor interaction pathway. These pathways were mainly involved in regulating the inflammatory response, adaptive immune response, leukocyte-mediated immunity, cytokine-mediated immunity, immune effector processes, myeloid cell differentiation, and T-helper cell differentiation. These nine hub genes could be considered biomarkers for the early prediction of septic cardiomyopathy.

Single-cell RNA sequencing in endometrial cancer: exploring the epithelial cells and the microenvironment landscape.

Single-cell RNA sequencing (scRNA-seq) technology has emerged as a powerful tool for dissecting cellular heterogeneity and understanding the intricate biology of diseases, including cancer. Endometrial cancer (EC) stands out as the most prevalent gynecological malignancy in Europe and the second most diagnosed worldwide, yet its cellular complexity remains poorly understood. In this review, we explore the contributions of scRNA-seq studies to shed light on the tumor cells and cellular landscape of EC. We discuss the diverse tumoral and microenvironmental populations identified through scRNA-seq, highlighting the implications for understanding disease progression. Furthermore, we address potential limitations inherent in scRNA-seq studies, such as technical biases and sample size constraints, emphasizing the need for larger-scale research encompassing a broader spectrum of EC histological subtypes. Notably, a significant proportion of scRNA-seq analyses have focused on primary endometrioid carcinoma tumors, underscoring the need to incorporate additional histological and aggressive types to comprehensively capture the heterogeneity of EC. By critically evaluating the current state of scRNA-seq research in EC, this review underscores the importance of advancing towards more comprehensive studies to accelerate our understanding of this complex disease.

AI-powered genomic mutation signature for predicting immune checkpoint inhibitor therapy outcomes in gastroesophageal cancer: a multi-cohort analysis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have significantly transformed the treatment of gastroesophageal cancer (GEC). However, the lack of reliable prognostic biomarkers hinders the ability to predict patient response to ICI therapy. METHODS: In this study, we engineered and validated a genomic mutation signature (GMS) utilizing an innovative artificial intelligence (AI) algorithm to forecast ICI therapy outcomes in GEC patients. We further explored immune profiles across subtypes through comprehensive multiomics analysis. Our investigation of drug sensitivity data from the Genomics of Drug Sensitivity in Cancer (GDSC) database led to the identification of trametinib as a potential therapeutic agent. We subsequently evaluated trametinib's efficacy in AGS and MKN45 cell lines using Cell Counting Kit-8 (CCK8) assays and clonogenic experiments. RESULTS: We developed a GMS by integrating 297 algorithms, enabling autonomous prognosis prediction for GEC patients. The GMS demonstrated consistent performance across three public cohorts, exhibiting high sensitivity and specificity for overall survival (OS) at 6, 12, and 18 months, as shown by Receiver Operator Characteristic Curve (ROC) analysis. Notably, the GMS surpassed traditional clinical and molecular features, including tumor mutational burden (TMB), programmed death-ligand 1 (PD-L1) expression, and microsatellite instability (MSI), in predictive accuracy. Low-risk samples exhibited elevated levels of cytolytic immune cells and heightened immunogenic potential compared to high-risk samples. Our investigation identified trametinib as a potential therapeutic agent. An inverse correlation was observed between GMS and trametinib IC50. Moreover, the high-risk-derived AGS cell line showed increased sensitivity to trametinib compared to the low-risk-derived MKN45 cell line. CONCLUSION: The GMS utilized in this study successfully demonstrated the ability to reliably predict the survival advantage for patients with GECs undergoing ICI therapy.

Significance of novel PANoptosis genes to predict prognosis and therapy effect in the lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the dominant histotype of non-small cell lung cancer. Panoptosis, a comprehensive form of programmed cell death, is central to carcinogenesis. In this study, the expression of PANoptosis-related genes (PRGs) and their impact on the development, prognosis, tumor microenvironment, and treatment response of patients with lung adenocarcinoma (LUAD) were systematically evaluated. PRGs were selected from The Cancer Genome Atlas database and Genecards dataset using differential expression analysis. The signature of included PRGs was identified using univariate Cox regression analysis and LASSO regression analysis. Additionally, a nomogram was developed that includes signature and clinical information. Kaplan-Meier survival analysis and receiver operating characteristic curves were used to assess the predictive validity of these risk models. Finally, functional analysis of the selected PRGs in signature and analysis of immune landscape were also performed. Preliminary identification of 10 genes related to PANoptosis has significant implications for prognosis. Subsequently, seven related genes were integrated to classify LUAD patients into different survival risk groups. The prognostic risk score generated from the signature and the TNM stage were as independent prognostic factors and were utilized in creating a nomogram plot. Both the features and the nomogram plot showed accurate performance in predicting the overall survival of LUAD patients. The PRGs were enriched in several biological functions and pathways, and stratified studies were conducted on the differences in immune landscape between high-risk and low-risk groups based on their characteristics. Ultimately, our evaluation focused on the differences in drug treatment efficacy between the high-risk and low-risk groups, providing a foundation for future research directions. Potential associations between PRGs and patient prognosis in LUAD have been identified in this study. Potential biomarkers for clinical application could be considered for the prognostic predictors identified in this study.

Elucidating the immune landscape and potential prognostic model in acute myeloid leukemia with TP53 mutation.

OBJECTIVES: The TP53 mutation, a prevalent tumor suppressor gene alteration, is linked to chemotherapy resistance, increased relapse rates and diminished overall survival (OS) in acute myeloid leukemia (AML) patients. METHODS: In this study, we characterize the TP53 mutation phenotypes across various AML cohorts utilizing The Cancer Genome Atlas (TCGA) data. We devised a TP53-related prognostic signature derived from differentially expressed genes between mutated and wild-type TP53 AML specimens. In-depth analyses were conducted, encompassing genetic variation, immune cell infiltration and prognostic stratification. RESULTS: A six-gene TP53-related signature was established using least absolute shrinkage and selection operator (LASSO)-Cox regression, demonstrating robust prognostic predictability. This signature exhibited strong performance in both the OHSU validation cohorts, an independent Gene Expression Omnibus (GEO) validation cohort (GSE71014) and proved by results of the in vivo experiment. Finally, we used single cell database (GSE198681) to observe the characteristics of these six genes. DISCUSSION: Our study may facilitate the development of efficacious therapeutic approaches and provide a novel idea for future research. Conclusion: The TP53-related signature and pattern hold the potential to refine prognostic stratification and underscore emerging targeted therapies.

Leveraging programmed cell death patterns to predict prognosis and therapeutic sensitivity in OSCC.

OBJECTIVES: Intricate associations between programmed cell death (PCD) and cancer development and treatment outcomes have been increasingly appreciated. Here, we integrated 12 PCD patterns to construct a novel biomarker, cell death index (CDI), for oral squamous cell carcinoma (OSCC) prognostication and therapeutic prediction. MATERIALS AND METHODS: Univariate Cox regression, Kaplan-Meier survival, and LASSO analyses were performed to construct the CDI. A nomogram combining CDI and selected clinicopathological parameters was established by multivariate Cox regression. The associations between CDI and immune landscape and therapeutic sensitivity were estimated. Single-cell RNA-seq data of OSCC was used to infer CDI genes in selected cell types and determine their expression along cell differentiation trajectory. RESULTS: Ten selected PCD genes derived a novel prognostic signature for OSCC. The predictive prognostic performance of CDI and nomogram was robust and superior across multiple independent patient cohorts. CDI was negatively associated with tumor-infiltrating immune cell abundance and immunotherapeutic outcomes. Moreover, scRNA-seq data reanalysis revealed that GSDMB, IL-1A, PRKAA2, and SFRP1 from this signature were primarily expressed in cancer cells and involved in cell differentiation. CONCLUSIONS: Our findings established CDI as a novel powerful predictor for prognosis and therapeutic response for OSCC and suggested its potential involvement in cancer cell differentiation.

Identification of immunity- and ferroptosis-related signature genes as potential design targets for mRNA vaccines in AML patients.

Immune-associated ferroptosis plays an important role in the progression of acute myeloid leukemia (AML); however, the targets that play key roles in this process are currently unknown. This limits the development of mRNA vaccines based on immune-associated ferroptosis for clinical therapeutic applications. In this study, based on the rich data resources of the TCGA-LAML cohort, we analyzed the tumor mutational burden (TMB), gene mutation status, and associations between immune and ferroptosis genes to reveal the disease characteristics of AML patients. To gain a deeper understanding of differentially expressed genes, we applied the Limma package for differential expression analysis and integrated data sources such as ImmPort Shared Data and FerrDb V2. Moreover, we established gene modules related to TMB according to weighted gene coexpression network analysis (WGCNA) and explored the functions of these modules in AML and their relationships with TMB. We focused on the top 30 most frequent genes through a detailed survey of missense mutations and single nucleotide polymorphisms (SNPs) and selected potentially critical gene targets for subsequent analysis. Based on the expression of these genes, we successfully subgrouped AML patients and found that the subgroups associated with TMB (C1 and C2) exhibited significant differences in survival. The differences in the tumor microenvironment and immune cells between C1 and C2 patients were investigated with the ESTIMATE and MCP-counter algorithms. A predictive model of TMB-related genes (TMBRGs) was constructed, and the validity of the model was demonstrated by categorizing patients into high-risk and low-risk groups. The differences in survival between the high-risk patients and high-TMB patients were further investigated, and potential vaccine targets were identified via immune cell-level analysis. The identification of immunity- and ferroptosis-associated signature genes is an independent predictor of survival in AML patients and provides new information on immunotherapy for AML.

Integrating Multi-omics to Identify Age-Related Macular Degeneration Subtypes and Biomarkers.

Age-related macular degeneration (AMD) is one of the most common causes of irreversible vision loss in the elderly. Its pathogenesis is likely multifactorial, involving a complex interaction of metabolic and environmental factors, and remains poorly understood. Previous studies have shown that mitochondrial dysfunction and oxidative stress play a crucial role in the development of AMD. Oxidative damage to the retinal pigment epithelium (RPE) has been identified as one of the major mediators in the pathogenesis of age-related macular degeneration (AMD). Therefore, this article combines transcriptome sequencing (RNA-seq) and single-cell sequencing (scRNA-seq) data to explore the role of mitochondria-related genes (MRGs) in AMD. Firstly, differential expression analysis was performed on the raw RNA-seq data. The intersection of differentially expressed genes (DEGs) and MRGs was performed. This paper proposes a deep subspace nonnegative matrix factorization (DS-NMF) algorithm to perform a multi-layer nonlinear transformation on the intersection of gene expression profiles corresponding to AMD samples. The age of AMD patients is used as prior information at the network's top level to change the data distribution. The classification is based on reconstructed data with altered distribution. The types obtained significantly differ in scores of multiple immune-related pathways and immune cell infiltration abundance. Secondly, an optimal AMD diagnosis model was constructed using multiple machine learning algorithms for external and qRT-PCR verification. Finally, ten potential therapeutic drugs for AMD were identified based on cMAP analysis. The AMD subtypes identified in this article and the diagnostic model constructed can provide a reference for treating AMD and discovering new drug targets.

A novel M2-like tumor associated macrophages-related gene signature for predicting the prognosis and immunotherapy efficacy in gastric cancer.

BACKGROUND: M2-like tumor-associated macrophages (M2-like TAMs) play key roles in tumor progression and the immune response. However, the clinical significance and prognostic value of M2-like TAMs-associated regulatory genes in gastric cancer (GC) have not been clarified. METHODS: Herein, we identified M2-like TAM-related genes by weighted gene coexpression network analysis of TCGA-STAD and GSE84437 cohort. Lasso-Cox regression analyses were then performed to screen for signature genes, and a novel signature was constructed to quantify the risk score for each patient. Tumor mutation burden (TMB), survival outcomes, immune cells, and immune function were analyzed in the risk groups to further reveal the immune status of GC patients. A gene-drug correlation analysis and sensitivity analysis of anticancer drugs were used to identify potential therapeutic agents. Finally, we verified the mRNA expression of signature genes in patient tissues by qRT-PCR, and analyzed the expression distribution of these genes by IHC. RESULTS: A 4-gene (SERPINE1, MATN3, CD36, and CNTN1) signature was developed and validated, and the risk score was shown to be an independent prognostic factor for GC patients. Further analyses revealed that GC patients in the high-risk group had a worse prognosis than those in the low-risk group, with significant differences in TMB, clinical features, enriched pathways, TIDE score, and tumor microenvironment features. Finally, we used qRT-PCR and IHC analysis to verify mRNA and protein level expression of signature genes. CONCLUSION: These findings highlight the importance of M2-like TAMs, provide a new perspective on individualized immunotherapy for GC patients.

The cross-talk between the macro and micro-environment in precursor lesions of pancreatic cancer leads to new and promising circulating biomarkers.

Pancreatic cancer (PC) is a clinically challenging tumor to combat due to its advanced stage at diagnosis as well as its resistance to currently available therapies. The absence of early symptoms and known detectable biomarkers renders this disease incredibly difficult to detect/manage. Recent advances in the understanding of PC biology have highlighted the importance of cancer-immune cell interactions, not only in the tumor micro-environment but also in distant systemic sites, like the bone marrow, spleen and circulating immune cells, the so-called macro-environment. The response of the macro-environment is emerging as a determining factor in tumor development by contributing to the formation of an increasingly immunogenic micro-environment promoting tumor homeostasis and progression. We will summarize the key events associated with the feedback loop between the tumor immune micro-environment (TIME) and the tumor immune macroenvironment (TIMaE) in pancreatic precancerous lesions along with how it regulates disease development and progression. In addition, liquid biopsy biomarkers capable of diagnosing PC at an early stage of onset will also be discussed. A clearer understanding of the early crosstalk between micro-environment and macro-environment could contribute to identifying new molecular therapeutic targets and biomarkers, consequently improving early PC diagnosis and treatment.

Single-cell immune landscape of measurable residual disease in acute myeloid leukemia.

Measurable residual disease (MRD) is a powerful prognostic factor of relapse in acute myeloid leukemia (AML). We applied the single-cell RNA sequencing to bone marrow (BM) samples from patients with (n=20) and without (n=12) MRD after allogeneic hematopoietic stem cell transplantation. A comprehensive immune landscape with 184,231 cells was created. Compared with CD8+ T cells enriched in the MRD-negative group (MRD-_CD8), those enriched in the MRD-positive group (MRD+_CD8) showed lower expression levels of cytotoxicity-related genes. Three monocyte clusters (i.e., MRD+_M) and three B-cell clusters (i.e., MRD+_B) were enriched in the MRD-positive group. Conversion from an MRD-positive state to an MRD-negative state was accompanied by an increase in MRD-_CD8 clusters and vice versa. MRD-enriched cell clusters employed the macrophage migration inhibitory factor pathway to regulate MRD-_CD8 clusters. These findings revealed the characteristics of the immune cell landscape in MRD positivity, which will allow for a better understanding of the immune mechanisms for MRD conversion.

A novel artificial intelligence network to assess the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized gastrointestinal cancer treatment, yet the absence of reliable biomarkers hampers precise patient response prediction. Methods: We developed and validated a genomic mutation signature (GMS) employing a novel artificial intelligence network to forecast the prognosis of gastrointestinal cancer patients undergoing ICIs therapy. Subsequently, we explored the underlying immune landscapes across different subtypes using multiomics data. Finally, UMI-77 was pinpointed through the analysis of drug sensitization data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. The sensitivity of UMI-77 to the AGS and MKN45 cell lines was evaluated using the cell counting kit-8 (CCK8) assay and the plate clone formation assay. Results: Using the artificial intelligence network, we developed the GMS that independently predicts the prognosis of gastrointestinal cancer patients. The GMS demonstrated consistent performance across three public cohorts and exhibited high sensitivity and specificity for 6, 12, and 24-month overall survival (OS) in receiver operating characteristic (ROC) curve analysis. It outperformed conventional clinical and molecular features. Low-risk samples showed a higher presence of cytolytic immune cells and enhanced immunogenic potential compared to high-risk samples. Additionally, we identified the small molecule compound UMI-77. The half-maximal inhibitory concentration (IC50) of UMI-77 was inversely related to the GMS. Notably, the AGS cell line, classified as high-risk, displayed greater sensitivity to UMI-77, whereas the MKN45 cell line, classified as low-risk, showed less sensitivity. Conclusion: The GMS developed here can reliably predict survival benefit for gastrointestinal cancer patients on ICIs therapy.