Metabolic landscape of the tumor microenvironment at single cell resolution

Metabolism score and machine learning models for the prediction of esophageal squamous cell carcinoma progression

The incomplete prediction of prognosis in esophageal squamous cell carcinoma (ESCC) patients is attributed to various therapeutic interventions and complex prognostic factors. Consequently, there is a pressing demand for enhanced predictive biomarkers that can facilitate clinical management and treatment decisions. This study recruited 491 ESCC patients who underwent surgical treatment at Huashan Hospital, Fudan University. We incorporated 14 blood metabolic indicators and identified independent prognostic indicators for overall survival through univariate and multivariate analyses. Subsequently, a metabolism score formula was established based on the biochemical markers. We constructed a nomogram and machine learning models utilizing the metabolism score and clinically significant prognostic features, followed by an evaluation of their predictive accuracy and performance. We identified alkaline phosphatase, free fatty acids, homocysteine, lactate dehydrogenase, and triglycerides as independent prognostic indicators for ESCC. Subsequently, based on these five indicators, we established a metabolism score that serves as an independent prognostic factor in ESCC patients. By utilizing this metabolism score in conjunction with clinical features, a nomogram can precisely predict the prognosis of ESCC patients, achieving an area under the curve (AUC) of 0.89. The random forest (RF) model showed superior predictive ability (AUC = 0.90, accuracy = 86%, Matthews correlation coefficient = 0.55). Finally, we used an RF model with optimal performance to establish an online predictive tool. The metabolism score developed in this study serves as an independent prognostic indicator for ESCC patients.

The relationship between Stroma AReactive Invasion Front Areas (SARIFA), Warburg-subtype and survival: results from a large prospective series of colorectal cancer patients

BACKGROUND

Stroma AReactive Invasion Front Areas (SARIFA) is a recently identified haematoxylin & eosin (H&E)based histopathologic biomarker in gastrointestinal cancers, including colorectal cancer (CRC), defined as direct contact between tumour cells and adipocytes at the tumour invasion front. The current study aimed at validating the prognostic relevance of SARIFA in a large population-based CRC series as well as at investigating the relationship between SARIFA-status and previously established Warburg-subtypes, both surrogates of the metabolic state of the tumour cells.

METHODS

SARIFA-status (positive versus negative) was determined on H&E slides of 1,727 CRC specimens. Warburg-subtype (high versus moderate versus low) data was available from our previous study. The associations between SARIFA-status, Warburg-subtype, clinicopathological characteristics and CRC-specific as well as overall survival were investigated.

RESULTS

28.7% (n=496) CRC were SARIFA-positive. SARIFA-positivity was associated with more advanced disease stage, higher pT category, and more frequent lymph node involvement (all p<0.001). SARIFA-positivity was more common in Warburg-high CRC. 44.2% (n=219) of SARIFA-positive CRCs were Warburg-high compared to 22.8% (n=113) being Warburg-low and 33.1% (n=164) being Warburg-moderate (p<0.001). In multivariable-adjusted analysis, patients with SARIFA-positive CRCs had significantly poorer CRC-specific (HRCRC-specific 1.65; 95% CI 1.41-1.93) and overall survival (HRoverall survival 1.46; 95% CI 1.28-1.67) independent of clinically known risk factors and independent of Warburg-subtype. Combining the SARIFA-status and the Warburg-subtype to a combination score (SARIFA-negative/Warburg-high versus SARIFA-positive/Warburg-low versus SARIFA-positive/Warburg-high, and so on) did not improve the survival prediction compared to the use of SARIFA-status alone (SARIFA-negative + Warburg-high: HRCRC-specific 1.08; 95% CI 0.84-1.38; SARIFA-positive + Warburg-low: HRCRC-specific 1.79; 95% CI 1.32-2.41; SARIFA-positive + Warburg-high: HRCRC-specific 1.58; 95% CI 1.23-2.04).

CONCLUSIONS

Our current study is the by far largest external validation of SARIFA-positivity as a novel independent negative prognostic H&E-based biomarker in CRC. In addition, our study shows that SARIFA-positivity is associated with the Warburg-high subtype. Further research is warranted to provide a more mechanistic understanding of the underlying tumour biology. Based on our data, we conclude SARIFA-status should be implemented in pathologic routine practice to stratify CRC patients.

Targeting metabolic pathways to counter cancer immunotherapy resistance

Immunotherapies have revolutionized the treatment of certain cancers, but challenges remain in overcoming immunotherapy resistance. Research shows that metabolic modulation of the tumor microenvironment can enhance antitumor immunity. Here, we discuss recent preclinical and clinical evidence for the efficacy of combining metabolic modifiers with immunotherapies. While this combination holds great promise, a few key areas must be addressed, which include identifying the effects of metabolic modifiers on immune cell metabolism, the putative biomarkers of therapeutic efficacy, the efficacy of modifiers on tumors harboring metabolic heterogeneity, and the potential development of resistance due to tumor reliance on alternative metabolic pathways. We propose solutions to these problems and posit that assessing these parameters is crucial for considering the potential of metabolic modifiers in sensitizing tumors to immunotherapies.

Metabolic dialogues: regulators of chimeric antigen receptor T cell function in the tumor microenvironment

Tumor-infiltrating lymphocytes (TILs) and chimeric antigen receptor (CAR) T cells have demonstrated remarkable success in the treatment of relapsed/refractory melanoma and hematological malignancies, respectively. These treatments have marked a pivotal shift in cancer management. However, as "living drugs," their effectiveness is dependent on their ability to proliferate and persist in patients. Recent studies indicate that the mechanisms regulating these crucial functions, as well as the T cell's differentiation state, are conditioned by metabolic shifts and the distinct utilization of metabolic pathways. These metabolic shifts, conditioned by nutrient availability as well as cell surface expression of metabolite transporters, are coupled to signaling pathways and the epigenetic landscape of the cell, modulating transcriptional, translational, and post-translational profiles. In this review, we discuss the processes underlying the metabolic remodeling of activated T cells, the impact of a tumor metabolic environment on T cell function, and potential metabolic-based strategies to enhance T cell immunotherapy.

Single-cell transcriptomic analysis identifies downregulated phosphodiesterase 8B as a novel oncogene in IDH-mutant glioma

Introduction

Glioma, a prevalent and deadly brain tumor, is marked by significant cellular heterogeneity and metabolic alterations. However, the comprehensive cell-of-origin and metabolic landscape in high-grade (Glioblastoma Multiforme, WHO grade IV) and low-grade (Oligoastrocytoma, WHO grade II) gliomas remains elusive.

Methods

In this study, we undertook single-cell transcriptome sequencing of these glioma grades to elucidate their cellular and metabolic distinctions. Following the identification of cell types, we compared metabolic pathway activities and gene expressions between high-grade and low-grade gliomas.

Results

Notably, astrocytes and oligodendrocyte progenitor cells (OPCs) exhibited the most substantial differences in both metabolic pathways and gene expression, indicative of their distinct origins. The comprehensive analysis identified the most altered metabolic pathways (MCPs) and genes across all cell types, which were further validated against TCGA and CGGA datasets for clinical relevance.

Discussion

Crucially, the metabolic enzyme phosphodiesterase 8B (PDE8B) was found to be exclusively expressed and progressively downregulated in astrocytes and OPCs in higher-grade gliomas. This decreased expression identifies PDE8B as a metabolism-related oncogene in IDH-mutant glioma, marking its dual role as both a protective marker for glioma grading and prognosis and as a facilitator in glioma progression.

Genetically programmable cell membrane-camouflaged nanoparticles for targeted combination therapy of colorectal cancer

Cell membrane-camouflaged nanoparticles possess inherent advantages derived from their membrane structure and surface antigens, including prolonged circulation in the bloodstream, specific cell recognition and targeting capabilities, and potential for immunotherapy. Herein, we introduce a cell membrane biomimetic nanodrug platform termed MPB-3BP@CM NPs. Comprising microporous Prussian blue nanoparticles (MPB NPs) serving as both a photothermal sensitizer and carrier for 3-bromopyruvate (3BP), these nanoparticles are cloaked in a genetically programmable cell membrane displaying variants of signal regulatory protein α (SIRPα) with enhanced affinity to CD47. As a result, MPB-3BP@CM NPs inherit the characteristics of the original cell membrane, exhibiting an extended circulation time in the bloodstream and effectively targeting CD47 on the cytomembrane of colorectal cancer (CRC) cells. Notably, blocking CD47 with MPB-3BP@CM NPs enhances the phagocytosis of CRC cells by macrophages. Additionally, 3BP, an inhibitor of hexokinase II (HK2), suppresses glycolysis, leading to a reduction in adenosine triphosphate (ATP) levels and lactate production. Besides, it promotes the polarization of tumor-associated macrophages (TAMs) towards an anti-tumor M1 phenotype. Furthermore, integration with MPB NPs-mediated photothermal therapy (PTT) enhances the therapeutic efficacy against tumors. These advantages make MPB-3BP@CM NPs an attractive platform for the future development of innovative therapeutic approaches for CRC. Concurrently, it introduces a universal approach for engineering disease-tailored cell membranes for tumor therapy.

New insights into immune cells in cancer immunotherapy: from epigenetic modification, metabolic modulation to cell communication

Cancer is one of the leading causes of death worldwide, and more effective ways of attacking cancer are being sought. Cancer immunotherapy is a new and effective therapeutic method after surgery, radiotherapy, chemotherapy, and targeted therapy. Cancer immunotherapy aims to kill tumor cells by stimulating or rebuilding the body's immune system, with specific efficiency and high safety. However, only few tumor patients respond to immunotherapy and due to the complex and variable characters of cancer immune escape, the behavior and regulatory mechanisms of immune cells need to be deeply explored from more dimensions. Epigenetic modifications, metabolic modulation, and cell-to-cell communication are key factors in immune cell adaptation and response to the complex tumor microenvironment. They collectively determine the state and function of immune cells through modulating gene expression, changing in energy and nutrient demands. In addition, immune cells engage in complex communication networks with other immune components, which are mediated by exosomes, cytokines, and chemokines, and are pivotal in shaping the tumor progression and therapeutic response. Understanding the interactions and combined effects of such multidimensions mechanisms in immune cell modulation is important for revealing the mechanisms of immunotherapy failure and developing new therapeutic targets and strategies.

A systematic review on the culture methods and applications of 3D tumoroids for cancer research and personalized medicine

Cancer is a highly heterogeneous disease, and thus treatment responses vary greatly between patients. To improve therapy efficacy and outcome for cancer patients, more representative and patient-specific preclinical models are needed. Organoids and tumoroids are 3D cell culture models that typically retain the genetic and epigenetic characteristics, as well as the morphology, of their tissue of origin. Thus, they can be used to understand the underlying mechanisms of cancer initiation, progression, and metastasis in a more physiological setting. Additionally, co-culture methods of tumoroids and cancer-associated cells can help to understand the interplay between a tumor and its tumor microenvironment. In recent years, tumoroids have already helped to refine treatments and to identify new targets for cancer therapy. Advanced culturing systems such as chip-based fluidic devices and bioprinting methods in combination with tumoroids have been used for high-throughput applications for personalized medicine. Even though organoid and tumoroid models are complex in vitro systems, validation of results in vivo is still the common practice. Here, we describe how both animal- and human-derived tumoroids have helped to identify novel vulnerabilities for cancer treatment in recent years, and how they are currently used for precision medicine.

In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level

Comprehensive single-cell metabolic profiling is critical for revealing phenotypic heterogeneity and elucidating the molecular mechanisms underlying biological processes. However, single-cell metabolomics remains challenging because of the limited metabolite coverage and inability to discriminate isomers. Herein, we establish a single-cell metabolomics platform for in-depth organic mass cytometry. Extended single-cell analysis time guarantees sufficient MS/MS acquisition for metabolite identification and the isomers discrimination while online sampling ensures the high-throughput of the method. The largest number of identified metabolites (approximately 600) are achieved in single cells and fine subtyping of MCF-7 cells is first demonstrated by an investigation on the differential levels of 3-hydroxybutanoic acid among clusters. Single-cell transcriptome analysis reveals differences in the expression of 3-hydroxybutanoic acid downstream antioxidative stress genes, such as metallothionein 2 (MT2A), while a fluorescence-activated cell sorting assay confirms the positive relationship between 3-hydroxybutanoic acid and target proteins; these results suggest that the heterogeneity of 3-hydroxybutanoic acid provides cancer cells with different ability to resist surrounding oxidative stress. Our method paves the way for deep single-cell metabolome profiling and investigations on the physiological and pathological processes that occur during cancer.

Unraveling metabolic characteristics and clinical implications in gastric cancer through single-cell resolution analysis

Background: Gastric cancer is a highly prevalent malignant neoplasm. Metabolic reprogramming is intricately linked to both tumorigenesis and cancer immune evasion. The advent of single-cell RNA sequencing technology provides a novel perspective for evaluating cellular metabolism. This study aims to comprehensively investigate the metabolic pathways of various cell types in tumor and normal samples at high resolution and delve into the intricate regulatory mechanisms governing the metabolic activity of malignant cells in gastric cancer. Methods: Utilizing single-cell RNA sequencing data from gastric cancer, we constructed metabolic landscape maps for different cell types in tumor and normal samples. Employing unsupervised clustering, we categorized malignant cells in tumor samples into high and low metabolic subclusters and further explored the characteristics of these subclusters. Results: Our research findings indicate that epithelial cells in tumor samples exhibit significantly higher activity in most KEGG metabolic pathways compared to other cell types. Unsupervised clustering, based on the scores of metabolic pathways, classified malignant cells into high and low metabolic subclusters. In the high metabolic subcluster, it demonstrated the potential to induce a stronger immune response, correlating with a relatively favorable prognosis. In the low metabolic subcluster, a subset of cells resembling cancer stem cells (CSCs) was identified, and its prognosis was less favorable. Furthermore, a set of risk genes associated with this subcluster was discovered. Conclusion: This study reveals the intricate regulatory mechanisms governing the metabolic activity of malignant cells in gastric cancer, offering new perspectives for improving prognosis and treatment strategies.

Single-cell transcriptomic landscape and the microenvironment of normal adjacent tissues in hypopharyngeal carcinoma

BACKGROUND

The cellular origin of hypopharyngeal diseases is crucial for further diagnosis and treatment, and the microenvironment in tissues may also be associated with specific cell types at the same time. Normal adjacent tissues (NATs) of hypopharyngeal carcinoma differ from non-tumor-bearing tissues, and can influenced by the tumor. However, the heterogeneity in kinds of disease samples remains little known, and the transcriptomic profile about biological information associated with disease occurrence and clinical outcome contained in it has yet to be fully evaluated. For these reasons, we should quickly investigate the taxonomic and transcriptomic information of NATs in human hypopharynx.

RESULTS

Single-cell suspensions of normal adjacent tissues (NATs) of hypopharyngeal carcinoma were obtained and single-cell RNA sequencing (scRNA-seq) was performed. We present scRNA-seq data from 39,315 high-quality cells in the hypopharyngeal from five human donors, nine clusters of normal adjacent human hypopharyngeal cells were presented, including epithelial cells, endothelial cells (ECs), mononuclear phagocyte system cells (MPs), fibroblasts, T cells, plasma cells, B cells, mural cells and mast cells. Nonimmune components in the microenvironment, including epithelial cells, endothelial cells, fibroblasts and the subpopulations of them were performed.

CONCLUSIONS

Our data provide a solid basis for the study of single-cell landscape in human normal adjacent hypopharyngeal tissues biology and related diseases.

Metabolic rewiring and communication in cancer immunity

The immune system shapes tumor development and progression. Although immunotherapy has transformed cancer treatment, its overall efficacy remains limited, underscoring the need to uncover mechanisms to improve therapeutic effects. Metabolism-associated processes, including intracellular metabolic reprogramming and intercellular metabolic crosstalk, are emerging as instructive signals for anti-tumor immunity. Here, we first summarize the roles of intracellular metabolic pathways in controlling immune cell function in the tumor microenvironment. How intercellular metabolic communication regulates anti-tumor immunity, and the impact of metabolites or nutrients on signaling events, are also discussed. We then describe how targeting metabolic pathways in tumor cells or intratumoral immune cells or via nutrient-based interventions may boost cancer immunotherapies. Finally, we conclude with discussions on profiling and functional perturbation methods of metabolic activity in intratumoral immune cells, and perspectives on future directions. Uncovering the mechanisms for metabolic rewiring and communication in the tumor microenvironment may enable development of novel cancer immunotherapies.

Deciphering metabolic heterogeneity in retinoblastoma unravels the role of monocarboxylate transporter 1 in tumor progression

BACKGROUND

Tumors exhibit metabolic heterogeneity, influencing cancer progression. However, understanding metabolic diversity in retinoblastoma (RB), the primary intraocular malignancy in children, remains limited.

METHODS

The metabolic landscape of RB was constructed based on single-cell transcriptomic sequencing from 11 RB and 5 retina samples. Various analyses were conducted, including assessing overall metabolic activity, metabolic heterogeneity, and the correlation between hypoxia and metabolic pathways. Additionally, the expression pattern of the monocarboxylate transporter (MCT) family in different cell clusters was examined. Validation assays of MCT1 expression and function in RB cell lines were performed. The therapeutic potential of targeting MCT1 was evaluated using an orthotopic xenograft model. A cohort of 47 RB patients was analyzed to evaluate the relationship between MCT1 expression and tumor invasion.

RESULTS

Distinct metabolic patterns in RB cells, notably increased glycolysis, were identified. This metabolic heterogeneity correlated closely with hypoxia. MCT1 emerged as the primary monocarboxylate transporter in RB cells. Disrupting MCT1 altered cell viability and energy metabolism. In vivo studies using the MCT1 inhibitor AZD3965 effectively suppressed RB tumor growth. Additionally, a correlation between MCT1 expression and optic nerve invasion in RB samples suggested prognostic implications.

CONCLUSIONS

This study enhances our understanding of RB metabolic characteristics at the single-cell level, highlighting the significance of MCT1 in RB pathogenesis. Targeting MCT1 holds promise as a therapeutic strategy for combating RB, with potential prognostic implications.

Investigation of ENO2 as a promising novel marker for the progression of colorectal cancer with microsatellite instability-high

BACKGROUND

Microsatellite instability-high (MSI-H) has emerged as a significant biological characteristic of colorectal cancer (CRC). Studies reported that MSI-H CRC generally had a better prognosis than microsatellite stable (MSS)/microsatellite instability-low (MSI-L) CRC, but some MSI-H CRC patients exhibited distinctive molecular characteristics and experienced a less favorable prognosis. In this study, our objective was to explore the metabolic transcript-related subtypes of MSI-H CRC and identify a biomarker for predicting survival outcomes.

METHODS

Single-cell RNA sequencing (scRNA-seq) data of MSI-H CRC patients were obtained from the Gene Expression Omnibus (GEO) database. By utilizing the copy number variation (CNV) score, a malignant cell subpopulation was identified at the single-cell level. The metabolic landscape of various cell types was examined using metabolic pathway gene sets. Subsequently, functional experiments were conducted to investigate the biological significance of the hub gene in MSI-H CRC. Finally, the predictive potential of the hub gene was assessed using a nomogram.

RESULTS

This study revealed a malignant tumor cell subpopulation from the single-cell RNA sequencing (scRNA-seq) data. MSI-H CRC was clustered into two subtypes based on the expression profiles of metabolism-related genes, and ENO2 was identified as a hub gene. Functional experiments with ENO2 knockdown and overexpression demonstrated its role in promoting CRC cell migration, invasion, glycolysis, and epithelial-mesenchymal transition (EMT) in vitro. High expression of ENO2 in MSI-H CRC patients was associated with worse clinical outcomes, including increased tumor invasion depth (p = 0.007) and greater likelihood of perineural invasion (p = 0.015). Furthermore, the nomogram and calibration curves based on ENO2 showed potential prognosis predictive performance.

CONCLUSION

Our findings suggest that ENO2 serves as a novel prognostic biomarker and is associated with the progression of MSI-H CRC.

Longitudinal molecular profiling elucidates immunometabolism dynamics in breast cancer

Although metabolic reprogramming within tumor cells and tumor microenvironment (TME) is well described in breast cancer, little is known about how the interplay of immune state and cancer metabolism evolves during treatment. Here, we characterize the immunometabolic profiles of tumor tissue samples longitudinally collected from individuals with breast cancer before, during and after neoadjuvant chemotherapy (NAC) using proteomics, genomics and histopathology. We show that the pre-, on-treatment and dynamic changes of the immune state, tumor metabolic proteins and tumor cell gene expression profiling-based metabolic phenotype are associated with treatment response. Single-cell/nucleus RNA sequencing revealed distinct tumor and immune cell states in metabolism between cold and hot tumors. Potential drivers of NAC based on above analyses were validated in vitro. In summary, the study shows that the interaction of tumor-intrinsic metabolic states and TME is associated with treatment outcome, supporting the concept of targeting tumor metabolism for immunoregulation.

Photosensitive Hydrogel with Temperature-Controlled Reversible Nano-Apertures for Single-Cell Protein Analysis

Single cell western blot (scWB) is one of the most important methods for cellular heterogeneity profiling. However, current scWB based on conventional photoactive polyacrylamide hydrogel material suffers from the tradeoff between in-gel probing and separation resolution. Here, a highly sensitive temperature-controlled single-cell western blotting (tc-scWB) method is introduced, which is based on a thermo/photo-dualistic-sensitive polyacrylamide hydrogel, namely acrylic acid-functionalized graphene oxide (AFGO) assisted, N-isopropylacrylamide modified polyacrylamide (ANP) hydrogel. The ANP hydrogel is contracted at high-temperature to constrain protein band diffusion during microchip electrophoretic separation, while the gel aperture is expanded under low-temperature for better antibody penetration into the hydrogel. The tc-scWB method enables the separation and profiling of small-molecule-weight proteins with highly crosslinked gel (12% T) in SDS-PAGE. The tc-scWB is demonstrated on three metabolic and ER stress-specific proteins (CHOP, MDH2 and FH) in four pancreatic cell subtypes, revealing the expression of key enzymes in the Krebs cycle is upregulated with enhanced ER stress. It is found that ER stress can regulate crucial enzyme (MDH2 and FH) activities of metabolic cascade in cancer cells, boosting aerobic respiration to attenuate the Warburg effect and promote cell apoptosis. The tc-scWB is a general toolbox for the analysis of low-abundance small-molecular functional proteins at the single-cell level.

High expression of SLC7A1 in high-grade serous ovarian cancer promotes tumor progression and is involved in MAPK/ERK pathway and EMT

Our previous studies have shown that upregulation of SLC7A1 in epithelial ovarian cancer (EOC) tumor cells significantly increases cancer cell proliferation, migration, and cisplatin resistance; however, the molecular mechanism by which SLC7A1 functions in EOC remains unknown. In later studies, we found that SLC7A1 is also highly expressed in the interstitial portion of high-grade serous ovarian cancer (HGSOC), but the significance of this high expression in the interstitial remains unclear. Here, we showed the Interstitial high expression of SLC7A1 in HGSOC by immunohistochemistry. SLC7A1 enriched in cancer-associated fibroblasts (CAFs) was upregulated by TGF-β1. Transwell assay, scratch assay, cck8 assay and cell adhesion assay showed that SLC7A1 highly expressed in CAFs promoted tumor cells invasion, migration and metastasis in vitro. The effect of SLC7A1 on MAPK and EMT pathway proteins in ovarian cancer (OC) was verified by RNA sequencing and western blotting. Overexpression of SLC7A1 in OC is involved in MAPK/ ERK pathway and EMT. In general, in HGSOC, CAFs overexpressing SLC7A1 supported the migration and invasion of tumor cells; SLC7A1 is highly expressed in ovarian cancer and is involved in ERK phosphorylation and EMT signaling in MAPK signaling pathway. This suggests that SLC7A1 may be a potential therapeutic target for OC metastasis.

Single-cell analysis revealing the metabolic landscape of prostate cancer

Tumor metabolic reprogramming is a hallmark of cancer development, and targeting metabolic vulnerabilities has been proven to be an effective approach for castration-resistant prostate cancer (CRPC) treatment. Nevertheless, treatment failure inevitably occurs, largely due to cellular heterogeneity, which cannot be deciphered by traditional bulk sequencing techniques. By employing computational pipelines for single-cell RNA sequencing, we demonstrated that epithelial cells within the prostate are more metabolically active and plastic than stromal cells. Moreover, we identified that neuroendocrine (NE) cells tend to have high metabolic rates, which might explain the high demand for nutrients and energy exhibited by neuroendocrine prostate cancer (NEPC), one of the most lethal variants of prostate cancer (PCa). Additionally, we demonstrated through computational and experimental approaches that variation in mitochondrial activity is the greatest contributor to metabolic heterogeneity among both tumor cells and nontumor cells. These results establish a detailed metabolic landscape of PCa, highlight a potential mechanism of disease progression, and emphasize the importance of future studies on tumor heterogeneity and the tumor microenvironment from a metabolic perspective.

Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization

Recent studies have increasingly revealed the connection between metabolic reprogramming and tumor progression. However, the specific impact of metabolic reprogramming on inter-patient heterogeneity and prognosis in lung adenocarcinoma (LUAD) still requires further exploration. Here, we introduced a cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), to reanalyze 178,739 single-cell reference profiles. Furthermore, we proposed a three-stage ensemble learning pipeline, aided by genetic algorithm (GA), for survival prediction across 9 LUAD cohorts (n = 2066). Throughout the pipeline of developing the three stage-MMR (3 S-MMR) score, double training sets were implemented to avoid over-fitting; the gene-pairing method was utilized to remove batch effect; GA was harnessed to pinpoint the optimal basic learner combination. The novel 3 S-MMR score reflects various aspects of LUAD biology, provides new insights into precision medicine for patients, and may serve as a generalizable predictor of prognosis and immunotherapy response. To facilitate the clinical adoption of the 3 S-MMR score, we developed an easy-to-use web tool for risk scoring as well as therapy stratification in LUAD patients. In summary, we have proposed and validated an ensemble learning model pipeline within the framework of metabolic reprogramming, offering potential insights for LUAD treatment and an effective approach for developing prognostic models for other diseases.

Sugar transporters spatially organize microbiota colonization along the longitudinal root axis of Arabidopsis

Plant roots are functionally heterogeneous in cellular architecture, transcriptome profile, metabolic state, and microbial immunity. We hypothesized that axial differentiation may also impact spatial colonization by root microbiota along the root axis. We developed two growth systems, ArtSoil and CD-Rhizotron, to grow and then dissect Arabidopsis thaliana roots into three segments. We demonstrate that distinct endospheric and rhizosphere bacterial communities colonize the segments, supporting the hypothesis of microbiota differentiation along the axis. Root metabolite profiling of each segment reveals differential metabolite enrichment and specificity. Bioinformatic analyses and GUS histochemistry indicate microbe-induced accumulation of SWEET2, 4, and 12 sugar uniporters. Profiling of root segments from sweet mutants shows altered spatial metabolic profiles and reorganization of endospheric root microbiota. This work reveals the interdependency between root metabolites and microbial colonization and the contribution of SWEETs to spatial diversity and stability of microbial ecosystem.

KHDRBS1 as a novel prognostic signaling biomarker influencing hepatocellular carcinoma cell proliferation, migration, immune microenvironment, and drug sensitivity

Background

Human tumors pose significant challenges, with targeted therapy against specific molecular targets or signaling pathways being a mainstay alongside surgical resection. Previous studies have implicated KHDRBS1 in the oncogenesis of certain human tumors such as colorectal and prostate cancers, underscoring its potential as a therapeutic target. However, the comprehensive expression pattern of KHDRBS1 in hepatocellular carcinoma (HCC) warrants further exploration.

Methods

Integrating and analyzing multi-omics, multi-cohort data from public databases, coupled with clinical samples and molecular biology validation, we elucidate the oncogenic role of KHDRBS1 in HCC progression. Additionally, leveraging HCC single-cell sequencing data, we segregate malignant cells into KHDRBS1-positive and negative subsets, uncovering significant differences in their expression profiles and functional roles.

Results

Our study identifies KHDRBS1 as a tumor-promoting factor in HCC, with its positivity correlating with tumor progression. Furthermore, we highlight the clinical significance of KHDRBS1-positive malignant cells, aiming to further propel its clinical utility.

Conclusion

KHDRBS1 plays a key role in HCC development. This study provides crucial insights for further investigation into KHDRBS1 as a therapeutic target in HCC.

A novel mitochondria-related core gene signature to predict the prognosis and evaluate tumour microenvironment in CESC single-cell validation

Mitochondria and their related genes (MTRGs) are pivotal in the tumour microenvironment (TME) of cervical cancer, influencing prognosis and treatment response. This study developed a prognostic model using MTRGs to predict overall survival (OS) in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), aiming for personalized therapy. Analysing 14 MTRGs like ISCU and NDUFA11 through techniques such as univariate Cox regression, we found that a low mitochondrial (MT) score is associated with better survival, while a high MT score predicts poorer outcomes. The TME score, particularly influenced by CD8 T cells, also correlates with prognosis, with a high score indicating favourable outcomes. The interplay between MT and TME subtypes revealed that the best prognosis is seen in patients with a low MT and high TME score. Our findings highlight the role of MTRGs as potential biomarkers and therapeutic targets in cervical cancer, offering a novel approach to improving patient outcomes through a more nuanced understanding of mitochondrial function and immune interactions within the TME. This model presents a promising avenue for enhancing the precision of prognostic assessments in CESC.

The value of intratumoral microbiota in the diagnosis and prognosis of tumors

Intratumoral microbiota (ITM) are microorganisms present in tumor cells. ITM participate in tumor development by affecting tumor cells directly and the tumor microenvironment (TME), indirectly. Alterations in ITM instigate changes in tumor DNA, activate oncogenic pathways, induce tumor inflammatory responses, disrupt normal immune activity, and facilitate the secretion of effectors leading to tumor progression, metastasis, or diminished therapeutic effects. ITM varies significantly in different types of cancer cells and disease states. The presence of certain ITM serves as a predictor of various disease states. Thus, ITM predicts tumorigenesis, tumor grade, treatment efficacy, and prognosis, making it a potential tumor biomarker. The present study aimed to determine the mechanisms by which ITM affects tumor development, especially through the TME; highlight the significant potential of ITM in enhancing tumor diagnosis and prognosis; and outline future directions for ITM research, with a focus on the development of innovative tumor markers.

ATF4 in cellular stress, ferroptosis, and cancer

Activating transcription factor 4 (ATF4), a member of the ATF/cAMP response element-binding (CREB) family, plays a critical role as a stress-induced transcription factor. It orchestrates cellular responses, particularly in the management of endoplasmic reticulum stress, amino acid deprivation, and oxidative challenges. ATF4's primary function lies in regulating gene expression to ensure cell survival during stressful conditions. However, when considering its involvement in ferroptosis, characterized by severe lipid peroxidation and pronounced endoplasmic reticulum stress, the ATF4 pathway can either inhibit or promote ferroptosis. This intricate relationship underscores the complexity of cellular responses to varying stress levels. Understanding the connections between ATF4, ferroptosis, and endoplasmic reticulum stress holds promise for innovative cancer therapies, especially in addressing apoptosis-resistant cells. In this review, we provide an overview of ATF4, including its structure, modifications, and functions, and delve into its dual role in both ferroptosis and cancer.

RNA modifications in cellular metabolism: implications for metabolism-targeted therapy and immunotherapy

Cellular metabolism is an intricate network satisfying bioenergetic and biosynthesis requirements of cells. Relevant studies have been constantly making inroads in our understanding of pathophysiology, and inspiring development of therapeutics. As a crucial component of epigenetics at post-transcription level, RNA modification significantly determines RNA fates, further affecting various biological processes and cellular phenotypes. To be noted, immunometabolism defines the metabolic alterations occur on immune cells in different stages and immunological contexts. In this review, we characterize the distribution features, modifying mechanisms and biological functions of 8 RNA modifications, including N6-methyladenosine (m6A), N6,2'-O-dimethyladenosine (m6Am), N1-methyladenosine (m1A), 5-methylcytosine (m5C), N4-acetylcytosine (ac4C), N7-methylguanosine (m7G), Pseudouridine (Ψ), adenosine-to-inosine (A-to-I) editing, which are relatively the most studied types. Then regulatory roles of these RNA modification on metabolism in diverse health and disease contexts are comprehensively described, categorized as glucose, lipid, amino acid, and mitochondrial metabolism. And we highlight the regulation of RNA modifications on immunometabolism, further influencing immune responses. Above all, we provide a thorough discussion about clinical implications of RNA modification in metabolism-targeted therapy and immunotherapy, progression of RNA modification-targeted agents, and its potential in RNA-targeted therapeutics. Eventually, we give legitimate perspectives for future researches in this field from methodological requirements, mechanistic insights, to therapeutic applications.

Engineering Tumor Stroma Morphogenesis Using Dynamic Cell-Matrix Spheroid Assembly

The tumor microenvironment consists of resident tumor cells organized within a compositionally diverse, three-dimensional (3D) extracellular matrix (ECM) network that cannot be replicated in vitro using bottom-up synthesis. We report a new self-assembly system to engineer ECM-rich 3D MatriSpheres wherein tumor cells actively organize and concentrate microgram quantities of decellularized ECM dispersions which modulate cell phenotype. 3D colorectal cancer (CRC) MatriSpheres were created using decellularized small intestine submucosa (SIS) as an orthotopic ECM source that had greater proteomic homology to CRC tumor ECM than traditional ECM formulations such as Matrigel. SIS ECM was rapidly concentrated from its environment and assembled into ECM-rich 3D stroma-like regions by mouse and human CRC cell lines within 4-5 days via a mechanism that was rheologically distinct from bulk hydrogel formation. Both ECM organization and transcriptional regulation by 3D ECM cues affected programs of malignancy, lipid metabolism, and immunoregulation that corresponded with an in vivo MC38 tumor cell subpopulation identified via single cell RNA sequencing. This 3D modeling approach stimulates tumor specific tissue morphogenesis that incorporates the complexities of both cancer cell and ECM compartments in a scalable, spontaneous assembly process that may further facilitate precision medicine.

[Advances in Research on Cell Metabolic Interactions in the Tumor Microenvironment]

Metabolic reprogramming plays a critical role in tumorigenesis and tumor progression. The metabolism and the proliferation of tumors are regulated by both intrinsic factors within the tumor and the availability of metabolites in the tumor microenvironment (TME). The metabolic niche within the TME is primarily orchestrated at 3 levels: 1) the regulation of tumor metabolism by factors intrinsic to the tumors, 2) the interaction between tumor cells and T cells, macrophages, and stromal cells, and 3) the metabolic heterogeneity of tumor cells within the tissue space. Herein, we provided a concise overview of the various metabolic regulatory modes observed in tumor cells. Additionally, we extensively analyzed the interaction between tumor cells and other cells within the TME, as well as the metabolic characteristics and functions of different types of cells. Ultimately, this review provides a theoretical basis and novel insights for the precision treatment of tumors.

Predicting the prognosis of glioma patients with TERT promoter mutations and guiding the specific immune profile of immune checkpoint blockade therapy

The telomerase reverse transcriptase promoter (TERTp) is frequently mutated in gliomas. This study sought to identify immune biomarkers of gliomas with TERTp mutations. Data from TCGA were used to identify and validate survival-associated gene signatures, and immune and stromal scores were calculated using the ESTIMATE algorithm. High stromal or immune scores in patients with TERTp-mutant gliomas correlated with shorter overall survival compared to cases with low stromal or immune scores. Among TERTp-mutant gliomas with both high immune and high stromal scores, 213 commonly shared DEGs were identified. Among 71 interacting DEGs representing candidate hub genes in a PPI network, HOXC6, WT1, CD70, and OTP showed significant ability in establishing subgroups of high- and low-risk patients. A risk model based on these 4 genes showed strong prognostic potential for gliomas with mutated TERTp, but was inapplicable for TERTp-wild-type gliomas. TERTp-mutant gliomas with high-risk scores displayed a greater percentage of naïve B cells, plasma cells, naïve CD4 T cells, and activated mast cells than low-risk score gliomas. TIDE analysis indicated that immune checkpoint blockade (ICB) therapy may benefit glioma patients with TERTp mutations. The present risk model can help predict prognosis of glioma patients with TERTp mutations and aid ICB treatment options.

Identification of a novel inflammatory-related gene signature to evaluate the prognosis of gastric cancer patients

BACKGROUND

Gastric cancer (GC) is a highly aggressive malignancy with a heterogeneous nature, which makes prognosis prediction and treatment determination difficult. Inflammation is now recognized as one of the hallmarks of cancer and plays an important role in the aetiology and continued growth of tumours. Inflammation also affects the prognosis of GC patients. Recent reports suggest that a number of inflammatory-related biomarkers are useful for predicting tumour prognosis. However, the importance of inflammatory-related biomarkers in predicting the prognosis of GC patients is still unclear.

AIM

To investigate inflammatory-related biomarkers in predicting the prognosis of GC patients.

METHODS

In this study, the mRNA expression profiles and corresponding clinical information of GC patients were obtained from the Gene Expression Omnibus (GEO) database (GSE66229). An inflammatory-related gene prognostic signature model was constructed using the least absolute shrinkage and selection operator Cox regression model based on the GEO database. GC patients from the GSE26253 cohort were used for validation. Univariate and multivariate Cox analyses were used to determine the independent prognostic factors, and a prognostic nomogram was established. The calibration curve and the area under the curve based on receiver operating characteristic analysis were utilized to evaluate the predictive value of the nomogram. The decision curve analysis results were plotted to quantify and assess the clinical value of the nomogram. Gene set enrichment analysis was performed to explore the potential regulatory pathways involved. The relationship between tumour immune infiltration status and risk score was analysed via Tumour Immune Estimation Resource and CIBERSORT. Finally, we analysed the association between risk score and patient sensitivity to commonly used chemotherapy and targeted therapy agents.

RESULTS

A prognostic model consisting of three inflammatory-related genes (MRPS17, GUF1, and PDK4) was constructed. Independent prognostic analysis revealed that the risk score was a separate prognostic factor in GC patients. According to the risk score, GC patients were stratified into high- and low-risk groups, and patients in the high-risk group had significantly worse prognoses according to age, sex, TNM stage and Lauren type. Consensus clustering identified three subtypes of inflammation that could predict GC prognosis more accurately than traditional grading and staging. Finally, the study revealed that patients in the low-risk group were more sensitive to certain drugs than were those in the high-risk group, indicating a link between inflammation-related genes and drug sensitivity.

CONCLUSION

In conclusion, we established a novel three-gene prognostic signature that may be useful for predicting the prognosis and personalizing treatment decisions of GC patients.

Identification and functional activity of Nik related kinase (NRK) in benign hyperplastic prostate

OBJECTIVE

Benign prostatic hyperplasia (BPH) is common in elder men. The current study aims to identify differentially expressed genes (DEGs) in hyperplastic prostate and to explore the role of Nik related kinase (NRK) in BPH.

METHODS

Four datasets including three bulk and one single cell RNA-seq (scRNA-seq) were obtained to perform integrated bioinformatics. Cell clusters and specific metabolism pathways were analyzed. The localization, expression and functional activity of NRK was investigated via RT-PCR, western-blot, immunohistochemical staining, flow cytometry, wound healing assay, transwell assay and CCK-8 assay.

RESULTS

A total of 17 DEGs were identified by merging three bulk RNA-seq datasets. The findings of integrated single-cell analysis showed that NRK remarkably upregulated in fibroblasts and SM cells of hyperplasia prostate. Meanwhile, NRK was upregulated in BPH samples and localized almost in stroma. The expression level of NRK was significantly correlated with IPSS and Qmax of BPH patients. Silencing of NRK inhibited stromal cell proliferation, migration, fibrosis and EMT process, promoted apoptosis and induced cell cycle arrest, while overexpression of NRK in prostate epithelial cells showed opposite results. Meanwhile, induced fibrosis and EMT process were rescued by knockdown of NRK. Furthermore, expression level of NRK was positively correlated with that of α-SMA, collagen-I and N-cadherin, negatively correlated with that of E-cadherin.

CONCLUSION

Our novel data identified NRK was upregulated in hyperplastic prostate and associated with prostatic stromal cell proliferation, apoptosis, cell cycle, migration, fibrosis and EMT process. NRK may play important roles in the development of BPH and may be a promising therapeutic target for BPH/LUTS.

Construction of a Diagnostic Model for Small Cell Lung Cancer Combining Metabolomics and Integrated Machine Learning

BACKGROUND

To date, no study has systematically explored the potential role of serum metabolites and lipids in the diagnosis of small cell lung cancer (SCLC). Therefore, we aimed to conduct a case-cohort study that included 191 cases of SCLC, 91 patients with lung adenocarcinoma, 82 patients with squamous cell carcinoma, and 97 healthy controls.

METHODS

Metabolomics and lipidomics were applied to analyze different metabolites and lipids in the serum of these patients. The SCLC diagnosis model (d-model) was constructed using an integrated machine learning technology and a training cohort (n = 323) and was validated in a testing cohort (n=138).

RESULTS

Eight metabolites, including 1-mristoyl-sn-glycero-3-phosphocholine, 16b-hydroxyestradiol, 3-phosphoserine, cholesteryl sulfate, D-lyxose, dioctyl phthalate, DL-lactate and Leu-Phe, were successfully selected to distinguish SCLC from controls. The d-model was constructed based on these 8 metabolites and showed improved diagnostic performance for SCLC, with the area under curve (AUC) of 0.933 in the training cohort and 0.922 in the testing cohort. Importantly, the d-model still had an excellent diagnostic performance after adjusting the stage and related clinical variables and, combined with the progastrin-releasing peptide (ProGRP), showed the best diagnostic performance with 0.975 of AUC for limited-stage patients.

CONCLUSION

This study is the first to analyze the difference between metabolomics and lipidomics and to construct a d-model to detect SCLC using integrated machine learning. This study may be of great significance for the screening and early diagnosis of SCLC patients.

AMPKα2 promotes tumor immune escape by inducing CD8+ T-cell exhaustion and CD4+ Treg cell formation in liver hepatocellular carcinoma

BACKGROUND

Adenosine monophosphate-activated protein kinase (AMPK) is associated with the development of liver hepatocellular carcinoma (LIHC). AMPKα2, an α2 subunit of AMPK, is encoded by PRKAA2, and functions as the catalytic core of AMPK. However, the role of AMPKα2 in the LIHC tumor immune environment is unclear.

METHODS

RNA-seq data were obtained from the Cancer Genome Atlas and Genotype-Tissue Expression databases. Using the single-cell RNA-sequencing dataset for LIHC obtained from the China National Genebank Database, the communication between malignant cells and T cells in response to different PRKAA2 expression patterns was evaluated. In addition, the association between PRKAA2 expression and T-cell evolution during tumor progression was explored using Pseudotime analysis, and the role of PRKAA2 in metabolic reprogramming was explored using the R "scMetabolis" package. Functional experiments were performed in LIHC HepG2 cells.

RESULTS

AMPK subunits were expressed in tissue-specific and substrate-specific patterns. PRKAA2 was highly expressed in LIHC tissues and was associated with poor patient prognosis. Tumors with high PRKAA2 expression displayed an immune cold phenotype. High PRKAA2 expression significantly promoted LIHC immune escape. This result is supported by the following evidence: 1) the inhibition of major histocompatibility complex class I (MHC-I) expression through the regulation of interferon-gamma activity in malignant cells; 2) the promotion of CD8+ T-cell exhaustion and the formation of CD4+ Treg cells in T cells; 3) altered interactions between malignant cells and T cells in the tumor immune environment; and 4) induction of metabolic reprogramming in malignant cells.

CONCLUSIONS

Our study indicate that PRKAA2 may contribute to LIHC progression by promoting metabolic reprogramming and tumor immune escape through theoretical analysis, which offers a theoretical foundation for developing PRKAA2-based strategies for personalized LIHC treatment.

Deciphering cellular plasticity in pancreatic cancer for effective treatments

Cellular plasticity and therapy resistance are critical features of pancreatic cancer, a highly aggressive and fatal disease. The pancreas, a vital organ that produces digestive enzymes and hormones, is often affected by two main types of cancer: the pre-dominant ductal adenocarcinoma and the less common neuroendocrine tumors. These cancers are difficult to treat due to their complex biology characterized by cellular plasticity leading to therapy resistance. Cellular plasticity refers to the capability of cancer cells to change and adapt to different microenvironments within the body which includes acinar-ductal metaplasia, epithelial to mesenchymal/epigenetic/metabolic plasticity, as well as stemness. This plasticity allows heterogeneity of cancer cells, metastasis, and evasion of host's immune system and develops resistance to radiation, chemotherapy, and targeted therapy. To overcome this resistance, extensive research is ongoing exploring the intrinsic and extrinsic factors through cellular reprogramming, chemosensitization, targeting metabolic, key survival pathways, etc. In this review, we discussed the mechanisms of cellular plasticity involving cellular adaptation and tumor microenvironment and provided a comprehensive understanding of its role in therapy resistance and ways to overcome it.

Uncovering immune checkpoint heterogeneity in oral squamous cell carcinoma using single cell RNA-sequencing data highlights three subgroups of patients with distinct immune phenotypes

OBJECTIVES

In head and neck squamous cell carcinoma (HNSCC), PD-1/PD-L1 inhibitors remain inefficient in most patients, which points to the need for better characterization of immune checkpoint (ICP) molecule expression.

MATERIAL AND METHODS

We evaluated the expression of 22 ICP ligands (ICPL) in 2,176 malignant cells from 10 patients in a public single-cell RNA-sequencing dataset and in two cohorts of HNSCC patients for which gene expression data are available.

RESULTS

Based on ICPL expression, malignant cells formed three distinct clusters characterized either by a strong expression of ICPL together with an immune phenotype linked to IFN-γ response (cluster 1) or by a weak ICPL expression and little response to IFN-γ (clusters 2 and 3). Malignant cells from cluster 3 showed a high PD-L1 expression associated with NRF2 signature. The relevance of 3 groups of patients, i.e "high ICPL/high IFN-γ", "low ICPL/low IFN-γ" or "low ICPL/low IFN-γ/high PD-L1" was confirmed in a cohort of 259 OSCC whole tumor samples from TCGA and in the CLB-IHN cohort including patients treated with PD1/PD-L1 inhibitors. The heterogeneous expression of ICPL among patients' malignant cells was associated with immunologically distinct microenvironments, evaluated with the "hot/cold" and the Tumor microenvironment (TME) classification. Finally, the "low ICPL/low IFN-γ/high PD-L1" group 3 displayed a poor prognosis in the TCGA cohort.

CONCLUSION

Hence, the global picture of ICPL gene expression in malignant cells from HNSCC patients may contribute to the broader issue of improving immunotherapy strategies though a better stratification of patients and the design of new treatment combinations.

Single-cell RNA-seq reveals the metabolic status of immune cells response to immunotherapy in triple-negative breast cancer

Immune checkpoint blockade (ICB) therapy offers promise in the treatment of triple-negative breast cancer (TNBC); however, its limited efficacy in certain TNBC patients poses a challenge. In this study, we elucidated the metabolic mechanism at 'sub-subtype' resolution underlying the non-response to ICB therapy in TNBC. Here, an analytic pipeline was developed to reveal the metabolic heterogeneity, which is correlated with the ICB outcomes, within each immune cell subtype. First, we identified metabolic 'sub-subtypes' within certain cell subtypes, predominantly T cell subsets, which are enriched in ICB non-responders and named as non-responder-enriched (NR-E) clusters. Notably, most of NR-E T metabolic cells exhibit globally higher metabolic activities compared to other cells within the same individual subtype. Further, we investigated the extra-cellular signals that trigger the metabolic status of NR-E T cells. In detail, the prediction of cell-to-cell communication indicated that NR-E T cells are regulated by plasmatic dendritic cells (pDCs) through TNFSF9, as well as by macrophages expressing SIGLEC9. In addition, we also validate the communication between TNFSF9+ pDCs and NR-E T cells utilizing deconvolution of spatial transcriptomics analysis. In summary, our research identified specific metabolic 'sub-subtypes' associated with ICB non-response and uncovered the mechanisms of their regulation in TNBC. And the proposed analytical pipeline can be used to examine metabolic heterogeneity within cell types that correlate with diverse phenotypes.

Multi-omics analysis reveals NNMT as a master metabolic regulator of metastasis in esophageal squamous cell carcinoma

Metabolic reprogramming has been observed in cancer metastasis, whereas metabolic changes required for malignant cells during lymph node metastasis of esophageal squamous cell carcinoma (ESCC) are still poorly understood. Here, we performed single-cell RNA sequencing (scRNA-seq) of paired ESCC tumor tissues and lymph nodes to uncover the reprogramming of tumor microenvironment (TME) and metabolic pathways. By integrating analyses of scRNA-seq data with metabolomics of ESCC tumor tissues and plasma samples, we found nicotinate and nicotinamide metabolism pathway was dysregulated in ESCC patients with lymph node metastasis (LN+), exhibiting as significantly increased 1-methylnicotinamide (MNA) in both tumors and plasma. Further data indicated high expression of N-methyltransferase (NNMT), which converts active methyl groups from the universal methyl donor, S-adenosylmethionine (SAM), to stable MNA, contributed to the increased MNA in LN+ ESCC. NNMT promotes epithelial-mesenchymal transition (EMT) and metastasis of ESCC in vitro and in vivo by inhibiting E-cadherin expression. Mechanically, high NNMT expression consumed too much active methyl group and decreased H3K4me3 modification at E-cadherin promoter and inhibited m6A modification of E-cadherin mRNA, therefore inhibiting E-cadherin expression at both transcriptional and post-transcriptional level. Finally, a detection method of lymph node metastasis was build based on the dysregulated metabolites, which showed good performance among ESCC patients. For lymph node metastasis of ESCC, this work supports NNMT is a master regulator of the cross-talk between cellular metabolism and epigenetic modifications, which may be a therapeutic target.

Metabolic regulation of tumor-associated macrophage heterogeneity: insights into the tumor microenvironment and immunotherapeutic opportunities

Tumor-associated macrophages (TAMs) are a heterogeneous population that play diverse functions in tumors. Their identity is determined not only by intrinsic factors, such as origins and transcription factors, but also by external signals from the tumor microenvironment (TME), such as inflammatory signals and metabolic reprogramming. Metabolic reprogramming has rendered TAM to exhibit a spectrum of activities ranging from pro-tumorigenic to anti-tumorigenic, closely associated with tumor progression and clinical prognosis. This review implicates the diversity of TAM phenotypes and functions, how this heterogeneity has been re-evaluated with the advent of single-cell technologies, and the impact of TME metabolic reprogramming on TAMs. We also review current therapies targeting TAM metabolism and offer new insights for TAM-dependent anti-tumor immunotherapy by focusing on the critical role of different metabolic programs in TAMs.

Identification of Proteome-Based Immune Subtypes of Early Hepatocellular Carcinoma and Analysis of Potential Metabolic Drivers

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, ranking fourth in frequency. The relationship between metabolic reprogramming and immune infiltration has been identified as having a crucial impact on HCC progression. However, a deeper understanding of the interplay between the immune system and metabolism in the HCC microenvironment is required. In this study, we used a proteomic dataset to identify three immune subtypes (IM1-IM3) in HCC, each of which has distinctive clinical, immune, and metabolic characteristics. Among these subtypes, IM3 was found to have the poorest prognosis, with the highest levels of immune infiltration and T-cell exhaustion. Furthermore, IM3 showed elevated glycolysis and reduced bile acid metabolism, which was strongly correlated with CD8 T cell exhaustion and regulatory T cell accumulation. Our study presents the proteomic immune stratification of HCC, revealing the possible link between immune cells and reprogramming of HCC glycolysis and bile acid metabolism, which may be a viable therapeutic strategy to improve HCC immunotherapy.

Metabolic heterogeneity in cancer

Cancer cells rewire their metabolism to survive during cancer progression. In this context, tumour metabolic heterogeneity arises and develops in response to diverse environmental factors. This metabolic heterogeneity contributes to cancer aggressiveness and impacts therapeutic opportunities. In recent years, technical advances allowed direct characterisation of metabolic heterogeneity in tumours. In addition to the metabolic heterogeneity observed in primary tumours, metabolic heterogeneity temporally evolves along with tumour progression. In this Review, we summarize the mechanisms of environment-induced metabolic heterogeneity. In addition, we discuss how cancer metabolism and the key metabolites and enzymes temporally and functionally evolve during the metastatic cascade and treatment.

Single-cell sequencing reveals the evolution of immune molecules across multiple vertebrate species

INTRODUCTION

Both innate and adaptive immune system undergo evolution from low to high vertebrates. Due to the limitation of conventional approaches in identifying broader spectrum of immune cells and molecules from various vertebrates, it remains unclear how immune molecules evolve among vertebrates.

OBJECTIVES

Here, we utilized carry out comparative transcriptome analysis in various immune cells across seven vertebrate species.

METHODS

Single-cell RNA sequencing (scRNA-seq).

RESULTS

We uncovered both conserved and species-specific profiling of gene expression in innate and adaptive immunity. Macrophages exhibited highly-diversified genes and developed sophisticated molecular signaling networks along with evolution, indicating effective and versatile functions in higher species. In contrast, B cells conservatively evolved with less differentially-expressed genes in analyzed species. Interestingly, T cells represented a dominant immune cell populations in all species and unique T cell populations were identified in zebrafish and pig. We also revealed compensatory TCR cascade components utilized by different species. Inter-species comparison of core gene programs demonstrated mouse species has the highest similarity in immune transcriptomes to human.

CONCLUSIONS

Therefore, our comparative study reveals gene transcription characteristics across multiple vertebrate species during the evolution of immune system, providing insights for species-specific immunity as well as the translation of animal studies to human physiology and disease.

Mitochondrial response of glioma cells to temozolomide

Metabolic adaptations are central for carcinogenesis and response to therapy, but little is known about the contribution of mitochondrial dynamics to the response of glioma cells to the standard treatment with temozolomide (TMZ). Glioma cells responded to TMZ with mitochondrial mass increased and the production of round structures of dysfunctional mitochondria. At single-cell level, asymmetric mitosis contributed to the heterogeneity of mitochondrial levels. It affected the fitness of cells in control and treated condition, indicating that the mitochondrial levels are relevant for glioma cell fitness in the presence of TMZ.

Regulatory mechanisms of one-carbon metabolism enzymes

One-carbon metabolism is a central metabolic pathway critical for the biosynthesis of several amino acids, methyl group donors, and nucleotides. The pathway mostly relies on the transfer of a carbon unit from the amino acid serine, through the cofactor folate (in its several forms), and to the ultimate carbon acceptors that include nucleotides and methyl groups used for methylation of proteins, RNA, and DNA. Nucleotides are required for DNA replication, DNA repair, gene expression, and protein translation, through ribosomal RNA. Therefore, the one-carbon metabolism pathway is essential for cell growth and function in all cells, but is specifically important for rapidly proliferating cells. The regulation of one-carbon metabolism is a critical aspect of the normal and pathological function of the pathway, such as in cancer, where hijacking these regulatory mechanisms feeds an increased need for nucleotides. One-carbon metabolism is regulated at several levels: via gene expression, posttranslational modification, subcellular compartmentalization, allosteric inhibition, and feedback regulation. In this review, we aim to inform the readers of relevant one-carbon metabolism regulation mechanisms and to bring forward the need to further study this aspect of one-carbon metabolism. The review aims to integrate two major aspects of cancer metabolism-signaling downstream of nutrient sensing and one-carbon metabolism, because while each of these is critical for the proliferation of cancerous cells, their integration is critical for comprehensive understating of cellular metabolism in transformed cells and can lead to clinically relevant insights.

Genomic subtypes of cutaneous melanoma have distinct metabolic profiles: A single-cell transcriptomic analysis

OBJECTIVE

Genomic profiling previously classified melanoma into distinct subtypes based on the presence or absence of mutations in driver genes, but metabolic differences between and within these groups have yet to be thoroughly analyzed. Thus, the objective of the present study is to provide the first effort to holistically characterize the metabolic landscape of qualified melanoma genomic subtypes at single-cell resolution.

METHODS

Expression data for a total of 1145 malignant cells sourced from NRAS(Q61L), BRAF(V600E), and NRAS/BRAF WT melanomas were retrieved from the Broad Single Cell Portal. Metabolic activity was interrogated by pathway scoring and gene set enrichment analysis.

RESULTS

A total of 53 metabolic pathways were differentially regulated in at least one melanoma genomic subtype. Some notable findings include: BRAF/NRAS WT cells were enriched for fatty acid biosynthesis and depleted for metabolism of alanine, aspartate, and glutamate; BRAF(V600E) melanoma cells were enriched for beta-alanine metabolism and depleted for phenylalanine metabolism; NRAS(Q61L) melanoma cells were enriched for steroid biosynthesis and depleted for linoleic acid metabolism.

CONCLUSION

Primary limitations include the total quantity of single cells and breadth of available genomic subtypes plus inherent noisiness of the applied methodologies. Nonetheless, these findings nominate novel, testable therapeutic targets.

Multivariate modeling of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations

Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent nature of cancer cell metabolism, however, poses challenges in identifying effective therapeutic interventions. Here, we utilize various unsupervised and supervised multivariate modeling approaches to systematically pinpoint recurrent metabolic states within hundreds of cancer cell lines, elucidate their association with tissue lineage and growth environments, and uncover vulnerabilities linked to their metabolic states across diverse genetic and tissue contexts. We validate key findings using data from an independent set of cell lines, pharmacological screens, and via single-cell analysis of patient-derived tumors. Our analysis uncovers new synthetically lethal associations between the tumor metabolic state (e.g., oxidative phosphorylation), driver mutations (e.g., loss of tumor suppressor PTEN), and actionable biological targets (e.g., mitochondrial electron transport chain). Investigating these relationships could inform the development of more precise and context-specific, metabolism-targeted cancer therapies.

MMP3C: an in-silico framework to depict cancer metabolic plasticity using gene expression profiles

Metabolic plasticity enables cancer cells to meet divergent demands for tumorigenesis, metastasis and drug resistance. Landscape analysis of tumor metabolic plasticity spanning different cancer types, in particular, metabolic crosstalk within cell subpopulations, remains scarce. Therefore, we proposed a new in-silico framework, termed as MMP3C (Modeling Metabolic Plasticity by Pathway Pairwise Comparison), to depict tumor metabolic plasticity based on transcriptome data. Next, we performed an extensive metabo-plastic analysis of over 6000 tumors comprising 13 cancer types. The metabolic plasticity within distinct cell subpopulations, particularly interplay with tumor microenvironment, were explored at single-cell resolution. Ultimately, the metabo-plastic events were screened out for multiple clinical applications via machine learning methods. The pilot research indicated that 6 out of 13 cancer types exhibited signs of the Warburg effect, implying its high reliability and robustness. Across 13 cancer types, high metabolic organized heterogeneity was found, and four metabo-plastic subtypes were determined, which link to distinct immune and metabolism patterns impacting prognosis. Moreover, MMP3C analysis of approximately 60 000 single cells of eight breast cancer patients unveiled several metabo-plastic events correlated to tumorigenesis, metastasis and immunosuppression. Notably, the metabolic features screened out by MMP3C are potential biomarkers for diagnosis, tumor classification and prognosis. MMP3C is a practical cross-platform tool to capture tumor metabolic plasticity, and our study unveiled a core set of metabo-plastic pairs among diverse cancer types, which provides bases toward improving response and overcoming resistance in cancer therapy.

Longitudinal multi-functional analysis identified responses of T cells, B cells, and monocytes as hallmarks of immunotherapy tolerance in patients with merkel cell carcinoma

PURPOSE

Merkel cell carcinoma (MCC) is a neuroendocrine carcinoma originating in the skin. Studies are needed to determine the mechanisms of immune escape in patients with MCC, and malignant cell conditions that promote immune evasion.

METHODS

We used Single-cell RNA sequencing (scRNA-seq) to determine cellular features associated with MCC disease trajectory. A longitudinal multi-omics study was performed using scRNA-seq data of peripheral blood harvested from four-time points. Six major cell types and fifteen cell subgroups were identified and confirmed their presence by expression of characteristic markers. The expression patterns and specific changes of different cells at different time points were investigated. Subsequently, bulk RNA data was used to validate key findings.

RESULTS

The dynamic characteristics of the cells were identified during the critical period between benign improvement and acquisition of resistance. Combined with the results of the validation cohort, the resistance program expressed in the relapse stage is mainly associated with T cell exhaustion and immune cell crosstalk disorder. Coinciding with immune escape, we also identified a decrease non-classical monocytes and an expansion of classical monocytes with features of high inflammation and immune deficiency.

CONCLUSION

Changes in cellular status, such as depletion of T cells and dysregulation of B cell proliferation and differentiation, may lead to drug resistance in MCC patients. Meanwhile, the widespread decreased antigen presentation ability and immune disorders caused by deletion of MHC class II gene expression should not be ignored.

Progesterone Activates the Histone Lactylation-Hif1α-glycolysis Feedback Loop to Promote Decidualization

Decidualization is a progesterone-dependent cellular differentiation process that is essential for establishing pregnancy. Robust activation of glycolysis and lactate synthesis during decidualization is remarkable, but their developmental functions remain largely unknown. Herein, we identify that endometrial lactate production plays a critical role in establishing local histone lactylation, a newly identified histone modification, and is important for ensuring normal decidualization. Enhanced endometrial glycolysis is the hallmark metabolic change and is tightly coupled with H4K12la during decidualization. Inhibition of histone lactylation impaired decidualization, in either physiological conception or in vivo and in vitro induced decidualization models. Mechanistic study based on CUT&Tag and ATAC-seq revealed that a transcriptional factor hypoxia-inducible factor 1 α (Hif1α) is the critical regulatory target of H4K12la, and in turn forms an H4K12la-Hif1α-glycolysis feedback loop to drive decidualization. Moreover, we demonstrate that the loop is directly activated by progesterone during decidualization. Our study not only advances the current knowledge of the role of lactate in regulating uterine function, but also establishes a novel functional link among the major endocrine factors, endometrial metabolic change, and epigenetic modification during endometrial remodeling. These findings present valuable clues to develop clinical intervention strategies to improve pregnancy outcomes following both natural conception and assisted reproduction.

Metabolic predictors of response to immune checkpoint blockade therapy

Metabolism of immune cells in the tumor microenvironment (TME) plays a critical role in cancer patient response to immune checkpoint inhibitors (ICI). Yet, a metabolic characterization of immune cells in the TME of patients treated with ICI is lacking. To bridge this gap we performed a semi-supervised analysis of ∼1700 metabolic genes using single-cell RNA-seq data of > 1 million immune cells from ∼230 samples of cancer patients treated with ICI. When clustering cells based on their metabolic gene expression, we found that similar immunological cellular states are found in different metabolic states. Most importantly, we found metabolic states that are significantly associated with patient response. We then built a metabolic predictor based on a dozen gene signature, which significantly differentiates between responding and non-responding patients across different cancer types (AUC = 0.8-0.92). Taken together, our results demonstrate the power of metabolism in predicting patient response to ICI.

New perspectives on biology, disease progression, and therapy response of head and neck cancer gained from single cell RNA sequencing and spatial transcriptomics

Head and neck squamous cell carcinoma (HNSCC) is one of the most frequent cancers worldwide. The main risk factors are consumption of tobacco products and alcohol, as well as infection with human papilloma virus. Approved therapeutic options comprise surgery, radiation, chemotherapy, targeted therapy through epidermal growth factor receptor inhibition, and immunotherapy, but outcome has remained unsatisfactory due to recurrence rates of ~50% and the frequent occurrence of second primaries. The availability of the human genome sequence at the beginning of the millennium heralded the omics era, in which rapid technological progress has advanced our knowledge of the molecular biology of malignant diseases, including HNSCC, at an unprecedented pace. Initially, microarray-based methods, followed by approaches based on next-generation sequencing, were applied to study the genetics, epigenetics, and gene expression patterns of bulk tumors. More recently, the advent of single-cell RNA sequencing (scRNAseq) and spatial transcriptomics methods has facilitated the investigation of the heterogeneity between and within different cell populations in the tumor microenvironment (e.g., cancer cells, fibroblasts, immune cells, endothelial cells), led to the discovery of novel cell types, and advanced the discovery of cell-cell communication within tumors. This review provides an overview of scRNAseq, spatial transcriptomics, and the associated bioinformatics methods, and summarizes how their application has promoted our understanding of the emergence, composition, progression, and therapy responsiveness of, and intercellular signaling within, HNSCC.