Altered Gene Expression along the Glycolysis-Cholesterol Synthesis Axis Is Associated with Outcome in Pancreatic Cancer

High glucose levels promote glycolysis and cholesterol synthesis via ERRα and suppress the autophagy-lysosomal pathway in endometrial cancer

Endometrial cancer (EC) patients with Diabetes Mellitus (DM) always have a poor prognosis. Estrogen-related receptor α (ERRα) is known as the metabolic-related prognostic factor for EC. However, the mechanism linking glycolipid metabolism dysfunction mediated by ERRα to poor prognosis of EC with DM is still unclear. In vitro, high-glucose (HG) levels showed enhancement of ERRα expression, cell proliferation, and inhibition of the autophagic lysosomes and apoptosis by flow cytometry analysis, transmission electron microscopy, and CCK-8 assays. Mechanistically, lose-and-gain function assay, DNA sequencing, and CO-IP revealed HG increased ERRα expression to promote the transcription of HK2 and HMGCS1, which were the key rate-limiting enzyme of glycolysis-cholesterol synthesis and their metabolites suppressed the autophagy-lysosomal pathway in an ERRα-dependent manner. Furthermore, CO-IP and molecular dynamics simulation uncovered the protein residues (ARG 769HK2 vs. ARG 313HMGCS1) of HK2 and HMGCS1 could bind to p62 to form stable protein complexes involved in the autophagy-lysosomal pathway. In EC tissue from patients with comorbid DM, ERRα was significantly higher expressed compared to EC tissue from patients without evidence for DM (p < 0.05). The 3D EC organoid model with HG stimulation showed that the cell viability of XCT790 + carboplatin treatment was similar to that of metformin+carboplatin treatment, while the obviously bigger volume of organoids was more visible in the metformin+carboplatin group, indicating the therapy of XCT790 + carboplatin had the better inhibition of EC organoids with the same carboplatin dose. Besides insights into the interaction of HG and the autophagy-lysosomal pathway via ERRα, our present study points out the potential benefit of targeting ERRα in patients with EC with dysregulation of glucose and cholesterol metabolism.

Cancer-intrinsic Cxcl5 orchestrates a global metabolic reprogramming for resistance to oxidative cell death in 3D

Pancreatic ductal adenocarcinoma is characterized by a three-dimensional (3D) tumor microenvironment devoid of oxygen and nutrients but enriched in extracellular matrix, which acts as a physical and chemical barrier. In 3D, cancer cells reprogram their metabolic pathways in ways that help them survive hostile conditions. However, little is known about the metabolic phenotypes of cancer cells in 3D and the intrinsic cues that modulate them. We found that Cxcl5 deletion restricted pancreatic tumor growth in a 3D spheroid-in-Matrigel culture system without affecting cancer cell growth in 2D culture. Cxcl5 deletion impaired 3D-specific global metabolic reprogramming, resistance to hypoxia-induced cell death, and upregulation of Hif1α and Myc. Overexpression of Hif1α and Myc, however, effectively restored 3D culture-induced metabolic reconfiguration, growth, redox homeostasis, and mitochondrial function in Cxcl5-/- cells, reducing ferroptosis. We also found that pancreatic cancer patients with higher expression of hypoxia and metabolism-related genes whose expression is well-correlated with CXCL5 generally have poorer prognosis. Together, our findings identify an unanticipated role of Cxcl5 in orchestrating the cancer metabolic reprogramming in 3D culture that is required for energy and biomass maintenance and that restricts oxidative cell death. Thus, our results provide a rationale for targeting CXCL5 as a promising therapeutic strategy.

Tumor metabolism as a factor affecting diversity in cancer cachexia

Cancer cachexia is a multifaceted metabolic syndrome characterized by muscle wasting, fat redistribution, and metabolic dysregulation, commonly associated with advanced cancer but sometimes also evident in early-stage disease. More subtle body composition changes have also been reported in association with cancer, including sarcopenia, myosteatosis, and increased fat radiodensity. Emerging evidence reveals that body composition changes including sarcopenia, myosteatosis, and increased fat radiodensity, arise from distinct biological mechanisms and significantly impact survival outcomes. Importantly, these features often occur independently, with their combined presence exacerbating poor prognoses. Tumor plays a pivotal role in driving these host changes, either by acting as a metabolic parasite or by releasing mediators that disrupt normal tissue function. This review explores the diversity of tumor metabolism. It highlights the potential for tumor-specific metabolic phenotypes to influence systemic effects, including fat redistribution and sarcopenia. Addressing this tumor-host metabolic interplay requires personalized approaches that disrupt tumor metabolism while preserving host health. Promising strategies include targeted pharmacological interventions and anticachexia agents like growth differentiation factor 15 (GDF-15) inhibitors. Nutritional modifications such as ketogenic diets and omega-3 fatty acid supplementation also merit further investigation. In addition to preserving muscle, these therapies will need to be evaluated for their capability to improve survival and quality of life. This review underscores the need for further research into tumor-driven metabolic effects on the host and the development of integrative treatment strategies to address the interconnected challenges of cancer progression and cachexia.

Trends in the Global Incidence of Pancreatic Cancer and a Brief Review of its Histologic and Molecular Subtypes

The global burden of pancreatic cancer has more than doubled in recent decades. It is now the sixth leading cause of cancer-related death worldwide, with an estimated 510,922 new cases and 467,409 deaths in 2022. The incidence of the disease continues to rise annually, with projections indicating a 95.4% increase in new cases by 2050, potentially reaching a total of 998,663 new cases globally. The overall five-year survival rate for pancreatic cancer is 10% worldwide, showing only a modest improvement compared to the past decade. The rising trends in the incidence rates are likely to continue as the global population ages and access to healthcare improves. The relatively low survival rate is primarily attributed to late-stage diagnoses and the lack of an effective screening method. Currently, population-based screening for asymptomatic individuals is not recommended, highlighting the importance of identifying and monitoring individuals at high risk for pancreatic cancer. Numerous studies have highlighted the differences in the molecular pathology of pancreatic cancer, underscoring the need for continued research to better understand these differences. The silent progression of the disease, poor prognosis, lack of screening options, and the necessity to improve our comprehension of its molecular characteristics emphasize the critical need for ongoing monitoring of disease trends at the population level. This review article analyses trends in the incidence of pancreatic cancer and its histological subtypes and provides an update on its molecular subtypes.

Cystic Fluid Total Proteins, Low-Density Lipoprotein Cholesterol, Lipid Metabolites, and Lymphocytes: Worrisome Biomarkers for Intraductal Papillary Mucinous Neoplasms

Objectives: Pancreatic cystic neoplasms (PCNs), particularly intraductal papillary mucinous neoplasms (IPMNs), present a challenge for their potential malignancy. Despite promising biomarkers like CEA, amylase, and glucose, our study investigates whether metabolic indices in blood and cystic fluids (CFs), in addition to lymphocyte subsets and hematopoietic stem/progenitor cells (HSPCs), can effectively differentiate between high- and low-risk PCNs. Materials and Methods: A total of 26 patients (11 males, mean age 69.5 ± 9 years) undergoing Endoscopic Ultrasound-guided Fine Needle Aspiration were consecutively enrolled. Analyses included blood, serum, and CF, assessing glucose, CEA, cholesterol (total, HDL, and LDL), and total proteins. Flow cytometry examined immunophenotyping in peripheral blood and cystic fluids. Mass spectrometry was used for the metabolomic analysis of CF. Sensitivity, specificity, and ROC analyses evaluated discriminatory power. Results: A total of 25 out of 26 patients had IPMN. Patients were categorized as low or high risk based on multidisciplinary evaluation of clinical, radiological, and endoscopic data. High-risk patients showed lower CF total proteins and LDL cholesterol (p = 0.005 and p = 0.031), with a marked reduction in CF lymphocytes (p = 0.005). HSCPs were absent in CF. In blood, high-risk patients showed increased non-MHC-restricted cytotoxic T cells (p = 0.019). The metabolomic analysis revealed significantly reduced middle and long-chain acyl carnitines (AcCa) and tryptophan metabolites in high-risk patients. ROC curves indicated comparable discriminant abilities for CF lymphocytes (AUC 0.868), CF total proteins (AUC 0.859), and CF LDL cholesterol (AUC 0.795). The highest performance was achieved by the AcCa 14:2 and 16:0 (AUC: 0.9221 and 0.8857, respectively). Conclusions: CF levels of glucose, CEA, LDL cholesterol, and total proteins together with lymphocyte counts are easy translational biomarkers that may support risk stratification of PCNs in IPMN patients and might be endorsed by metabolomic analysis. Further studies are required for potential clinical integration.

Real-World outcomes of Non-Small cell lung cancer patients harbouring KRAS G12C and KRAS G12D mutations

BACKGROUND

KRAS G12D and G12C mutations have distinct biological traits influencing treatment response. This study examines real-world demographics, clinical characteristics, and first-line treatment outcomes in metastatic non-small-cell lung cancer (NSCLC) patients with these mutations.

METHODS

This retrospective, multi-institution observational study used data from the AURORA database. Patients aged 18 years or older, diagnosed with metastatic KRAS G12D or G12C NSCLC between January 1, 2010, and April 30, 2024, were included. Descriptive statistics compared patient characteristics, and time-to-event outcomes were assessed using Cox proportional hazards regression.

RESULTS

A total of 298 (216 KRAS G12C and 82 KRAS G12D) patients were included. The KRAS G12D group had a higher proportion of never smokers (15 % vs. 1 %, p < 0.01) and PD-L1 < 1 % (36 % vs. 21 %, p = 0.06). No significant differences were observed in overall survival (OS) (HR 1.09, 95 % CI 0.80-1.48, p = 0.60) or real-world progression-free survival (rwPFS) (HR 1.21, 95 % CI 0.92-1.59, p = 0.18) between mutation groups. In KRAS G12C, monotherapy immunotherapy (HR 0.61, 95 % CI 0.39-0.97, p = 0.04) and chemo-immunotherapy (HR 0.59, 95 % CI 0.37-0.94, p = 0.03) improved OS compared to chemotherapy. For KRAS G12D, neither immunotherapy (HR 0.74, 95 % CI 0.29-1.89, p = 0.53) nor chemo-immunotherapy (HR 0.73, 95 % CI 0.34-1.57, p = 0.42) improved OS compared to chemotherapy alone.

CONCLUSION

KRAS G12C and G12D mutations demonstrate distinct clinical characteristics and treatment responses, with poorer immunotherapy outcomes in KRAS G12D patients. Prospective studies are needed to validate these findings.

Minimally invasive determination of pancreatic ductal adenocarcinoma (PDAC) subtype by means of circulating cell-free RNA

Pancreatic ductal adenocarcinoma (PDAC) comprises two clinically relevant molecular subtypes that are currently determined using tissue biopsies, which are spatially biased and highly invasive. We used whole transcriptome sequencing of 10 plasma samples with tumor-informed subtypes, complemented by proteomic analysis for minimally invasive identification of PDAC subtype markers. Data were validated in independent large cohorts and correlated with treatment response and patient outcome. Differential transcript abundance analyses revealed 32 subtype-specific, protein-coding cell-free RNA (cfRNA) transcripts. The subtype specificity of these transcripts was validated in two independent tissue cohorts comprising 195 and 250 cases, respectively. Three disease-relevant cfRNA-defined subtype markers (DEGS1, KDELC1, and RPL23AP7) that consistently associated with basal-like tumors across all cohorts were identified. In both tumor and liquid biopsies, the overexpression of these markers correlated with poor survival. Moreover, elevated levels of the identified markers were linked to a poor response to systemic therapy and early relapse in resected patients. Our data indicate clinical applicability of cfRNA markers in determining tumor subtypes and monitoring disease recurrence.

Bulk and single-cell transcriptome revealed the metabolic heterogeneity in human glioma

Background

Emerging perspectives on tumor metabolism reveal its heterogeneity, a characteristic yet to be fully explored in gliomas. To advance therapies targeting metabolic processes, it is crucial to uncover metabolic differences and identify distinct metabolic subtypes. Therefore, we aimed to develop a classification system for gliomas based on the enrichment levels of four key metabolic pathways: glutaminolysis, glycolysis, the pentose phosphate pathway, and fatty acid oxidation.

Methods

Energy-related features of glioma were characterized through integrative analyses of multiple datasets, including bulk, single-cell, and spatial transcriptome profiling. The glioma energy metabolic subtypes were constructed using the R package ConsensusClusterPlus. Kaplan-Meier analysis was conducted to compare clinical outcomes between different metabolic groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed to elucidate the biological functions of genes of interest. Cell-cell communication analysis was performed at single-cell resolution using the R package CellChat and at spatial resolution using the standard stLearn pipeline.

Results

Glioma samples were stratified into two prognostic subtypes. Group 1, enriched in the glutaminolysis pathway, had better clinical outcomes. In contrast, Group 2 exhibited high activities in glycolysis, the pentose phosphate pathway, and fatty acid oxidation, correlating with decreased survival time. Group 1 samples were predominantly located in the peripheral region and had a high composition of neuron cells. Group 2, however, had increased infiltration of tumor-promoting immune cells, such as M2 macrophages, and was characterized by traits of invasion, hypoxia, and immunity. Lastly, cell-cell communications were compared across different tumor regions, and the CX3CL1/CX3CR1 ligand-receptor pair was validated using spatial transcriptomic data.

Conclusions

Our work revealed the metabolic heterogeneity in glioma by developing a new classification system with significant prognostic and therapeutic value. Single-cell transcriptional profiles offer novel insights into tumor metabolic reprogramming, which could enhance therapies tailored to cell- or patient-specific metabolic patterns.

Single-Cell and Transcriptome Analysis of Periodontitis: Molecular Subtypes and Biomarkers Linked to Mitochondrial Dysfunction and Immunity

Background

Periodontitis represents an inflammatory disease with multiple contributing factors, affecting both oral and systemic health. The mechanisms linking mitochondrial dysfunction to immune responses in periodontitis remain unclear, limiting the development of individualized diagnostic and therapeutic approaches.

Objective

 This study aims to elucidate the roles of mitochondrial dysfunction and immune responses in the pathogenesis of periodontitis, identify distinct molecular subtypes, and discover robust diagnostic biomarkers to support precision medicine approaches.

Methods

Single-cell RNA sequencing and transcriptome data from periodontitis patients were analyzed to identify gene signatures linked to macrophages and mitochondria. Consensus clustering was applied to classify molecular subtypes. Potential biomarkers were identified using five machine learning algorithms and validated in clinical samples through qPCR and IHC.

Results

Four molecular subtypes were identified: quiescent, macrophage-dominant, mitochondria-dominant, and mixed, each exhibiting unique gene expression patterns. From 13 potential biomarkers, eight were shortlisted using machine learning, and five (BNIP3, FAHD1, UNG, CBR3, and SLC25A43) were validated in clinical samples. Among them, BNIP3, FAHD1, and UNG were significantly downregulated (p < 0.05).

Conclusion

This study identifies novel molecular subtypes and biomarkers that elucidate the interplay between immune responses and mitochondrial dysfunction in periodontitis. These findings provide insights into the disease's heterogeneity and lay the foundation for developing non-invasive diagnostic tools and personalized therapeutic strategies.

Plasticity and Tumor Microenvironment in Pancreatic Cancer: Genetic, Metabolic, and Immune Perspectives

Cancer has long been believed to be a genetic disease caused by the accumulation of mutations in key genes involved in cellular processes. However, recent advances in sequencing technology have demonstrated that cells with cancer driver mutations are also present in normal tissues in response to aging, environmental damage, and chronic inflammation, suggesting that not only intrinsic factors within cancer cells, but also environmental alterations are important key factors in cancer development and progression. Pancreatic cancer tissue is mostly comprised of stromal cells and immune cells. The desmoplasmic microenvironment characteristic of pancreatic cancer is hypoxic and hypotrophic. Pancreatic cancer cells may adapt to this environment by rewiring their metabolism through epigenomic changes, enhancing intrinsic plasticity, creating an acidic and immunosuppressive tumor microenvironment, and inducing noncancerous cells to become tumor-promoting. In addition, pancreatic cancer has often metastasized to local and distant sites by the time of diagnosis, suggesting that a similar mechanism is operating from the precancerous stage. Here, we review key recent findings on how pancreatic cancers acquire plasticity, undergo metabolic reprogramming, and promote immunosuppressive microenvironment formation during their evolution. Furthermore, we present the following two signaling pathways that we have identified: one based on the small G-protein ARF6 driven by KRAS/TP53 mutations, and the other based on the RNA-binding protein Arid5a mediated by inflammatory cytokines, which promote both metabolic reprogramming and immune evasion in pancreatic cancer. Finally, the striking diversity among pancreatic cancers in the relative importance of mutational burden and the tumor microenvironment, their clinical relevance, and the potential for novel therapeutic strategies will be discussed.

Simvastatin induces ferroptosis and activates anti-tumor immunity to sensitize anti-PD-1 immunotherapy in microsatellite stable gastric cancer

BACKGROUND

Gastric cancer (GC), especially the case with microsatellite stability (MSS) phenotype, has limited efficacy for immune checkpoint blockade (ICB) therapy. Metabolism reprogramming is newly recognized to affect tumor immune microenvironment (TIME). However, the relationship between metabolism reprogramming and immunotherapy for MSS GC has not been reported.

METHODS

A metabolic stratification for GC was developed based on the glycolysis/cholesterol synthesis axis using the R package "ConsensusClusterPlus". The T cell inflamed score was used to define "immune-hot" and "immune-cold" phenotypes in MSS GC. The anti-tumor and immunological effects of simvastatin were explored using in vitro and in vivo experiments.

RESULTS

Three metabolic subtypes were identified in GC patients, including cholesterol, glycolysis and quiescent subtypes. The cholesterol subtype was associated with poorer clinical features and higher tumor purity. Correspondingly, we demonstrated that simvastatin, a specific inhibitor of cholesterol synthesis, significantly inhibited the proliferation, migration, and induced ferroptosis in GC cells. Interestingly, simvastatin markedly inhibited tumor growth in immunocompetent mice, while no significant effect in immunodeficient mice. Upregulation of chemokines and increased recruitment of CD8+ T cells were observed after simvastatin treatment. Consistently, the cholesterol subtype exhibited a less inflamed TIME and coincided significantly with the "immune-cold" phenotype of MSS GC. Finally, we confirmed simvastatin enhanced PD-1 blockade efficacy via modulating the TIME and activating anti-tumor immunity in tumor-bearing mice.

CONCLUSION

Our data revealed the significance of cholesterol synthesis in GC and demonstrated simvastatin served as a promising sensitizer for ICB therapy by inducing ferroptosis and anti-tumor immunity in MSS GC patients.

High RRM2 Correlates with Mitochondrial and Immune Responses in the Eosinophilic Subtype of Clear Cell Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC), the predominant subtype of RCC, is distinguished by unique biological characteristics and heterogeneity, including eosinophilic and clear subtypes. Notwithstanding progress in therapy, immune checkpoint inhibitors (ICIs), and tyrosine kinase inhibitors (TKIs), the prognosis for individuals with metastatic ccRCC remains poor, presumably owing to metabolic alterations leading to mitochondrial dysfunction, which affects treatment response variability.

Methods

We analyzed histological and immunohistochemical data from a cohort at Dalian Medical University's First Affiliated Hospital alongside RNA-sequencing transcriptome data from the TCGA database. Histologically, eosinophilic and clear ccRCC subtypes were evaluated using Kaplan-Meier and Cox proportional hazards models for survival analysis and prognosis. Differential gene expression (DEG) analysis and Gene Set Enrichment Analysis were performed to explore transcriptomic differences and relevant pathways.

Results

The study discovered substantial histological and molecular differences between the eosinophilic and clear cell subtypes of ccRCC. The eosinophilic subtype linked with frequent high-grade tumors (69.05% eosinophil vs 35.35% clear) and a poorer prognosis (HR=2.659, 95% CI:1.437-4.919, P=0.002). DEG analysis revealed distinct expression patterns among subtypes and identified a risk score signature that remained significant even after adjusting for clinical variables (HR=3.967, 95% CI: 1.665-9.449, P=0.002), showing less favorable survival in the high-risk group (P < 0.0001). RRM2 emerged as the most prognostic gene from this risk score, particularly in the eosinophilic subtype, alongside other clinical variables. By IHC, RRM2 shows high IHC score in eosinophilic compared to clear subtype (P=0.019). In addition, highly expressed RRM2 correlates with poor outcomes and is linked to mitochondrial genes, immunological pathways, and ICIs treatment.

Conclusion

These findings show significant differences in prognosis between subtypes. RRM2 was the most prognostic gene from the discovered novel risk score signature associated with subtypes. Future research is essential to validate these insights and their therapeutic implications for ccRCC management.

Metabolite Biomarkers for Early Detection of Pancreatic Ductal Adenocarcinoma: A Systematic Review

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a poor prognosis. This poor prognosis is largely attributed to a late-stage diagnosis. Recent advancements in metabolomics have emerged as a promising avenue for biomarker discovery in PDAC. This systematic review evaluates the potential of metabolite biomarkers for early detection of PDAC. Four studies meeting the inclusion criteria were analyzed, encompassing experimental, case-control, and prospective cohort designs. Key findings include the identification of distinct metabolic subtypes in PDAC with varying sensitivities to metabolic inhibitors. A biomarker signature comprising nine metabolites plus CA19-9 showed high accuracy in distinguishing PDAC from chronic pancreatitis, outperforming CA19-9 alone. Another study identified a five-metabolite signature demonstrating high diagnostic accuracy for pancreatic cancer, differentiating it from type 2 diabetes mellitus. A two-metabolite model (isoleucine and adrenic acid) showed superior performance in detecting stage-I PDAC compared to CA19-9. These studies consistently demonstrate altered metabolic pathways in PDAC patients compared to healthy controls and those with benign pancreatic conditions. Integrating metabolomic data with other molecular profiling approaches has become a powerful strategy for improving diagnostic accuracy. However, challenges remain, including the influence of confounding factors, the need for large-scale validation studies, and the standardization of metabolomic methods. The potential of artificial intelligence in interpreting complex metabolomic data offers promising avenues for future research. This review highlights the significant potential of metabolite biomarkers in early PDAC detection while emphasizing the need for further validation and refinement of these approaches.

Devimistat (CPI-613) With Modified Fluorouarcil, Oxaliplatin, Irinotecan, and Leucovorin (FFX) Versus FFX for Patients With Metastatic Adenocarcinoma of the Pancreas: The Phase III AVENGER 500 Study

PURPOSE

Metastatic pancreatic adenocarcinoma (mPC) remains a difficult-to-treat disease. Fluorouarcil, oxaliplatin, irinotecan, and leucovorin (FFX) is a standard first-line therapy for mPC for patients with a favorable performance status and good organ function. In a phase I study, devimistat (CPI-613) in combination with modified FFX (mFFX) was deemed safe and exhibited promising efficacy in mPC.

METHODS

The AVENGER 500 trial (ClinicalTrials.gov identifier: NCT03504423) is a global, randomized phase III trial conducted at 74 sites across six countries to investigate the efficacy and safety of devimistat in combination with mFFX (experimental arm) compared with standard-dose FFX (control arm) in treatment-naïve patients with mPC. Treatment, administered in once-every-2-weeks cycles until disease progression or intolerable toxicity, included intravenous devimistat at 500 mg/m2 total per day on days 1 and 3 in the experimental arm. The primary end point of the study was overall survival (OS).

RESULTS

Five hundred and twenty-eight patients were randomly assigned (266 in the experimental arm and 262 in the control arm). The median OS was 11.10 months for devimistat plus mFFX versus 11.73 months for FFX (hazard ratio [HR], 0.95 [95% CI, 0.77 to 1.18]; P = .655) and median progression-free survival was 7.8 months versus 8.0 months, respectively (HR, 0.99 [95% CI, 0.76 to 1.29]; P = .94). Grade ≥3 treatment-emergent adverse events with >10% frequency in the devimistat plus mFFX arm versus the FFX arm were neutropenia (29.0% v 34.5%), diarrhea (11.2% v 19.6%), hypokalemia (13.1% v 14.9%), anemia (13.9% v 13.6%), thrombocytopenia (11.6% v 13.6%), and fatigue (10.8% v 11.5%), respectively.

CONCLUSION

Devimistat in combination with mFFX did not improve long- and short-term mPC patient outcomes compared with standard FFX. There were no new toxicity signals with the addition of devimistat.

Comprehensive multi-omics profiling identifies novel molecular subtypes of pancreatic ductal adenocarcinoma

Pancreatic cancer, a highly fatal malignancy, is predicted to rank as the second leading cause of cancer-related death in the next decade. This highlights the urgent need for new insights into personalized diagnosis and treatment. Although molecular subtypes of pancreatic cancer were well established in genomics and transcriptomics, few known molecular classifications are translated to guide clinical strategies and require a paradigm shift. Notably, chronically developing and continuously improving high-throughput technologies and systems serve as an important driving force to further portray the molecular landscape of pancreatic cancer in terms of epigenomics, proteomics, metabonomics, and metagenomics. Therefore, a more comprehensive understanding of molecular classifications at multiple levels using an integrated multi-omics approach holds great promise to exploit more potential therapeutic options. In this review, we recapitulated the molecular spectrum from different omics levels, discussed various subtypes on multi-omics means to move one step forward towards bench-to-beside translation of pancreatic cancer with clinical impact, and proposed some methodological and scientific challenges in store.

Pancreatic cancer tumor organoids exhibit subtype-specific differences in metabolic profiles

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease characterized by complex metabolic rewiring that enables growth in changing nutrient availability and oxygen conditions. Transcriptome-based prognostic PDAC tumor subtypes, known as 'basal-like' and 'classical' subtypes are associated with differences in metabolic gene expression including genes involved in glycolysis. Tumor subtype-specific metabolism phenotypes may provide new targets for treatment development in PDAC, but their functional relevance has not been fully elucidated. We aimed to investigate differences in metabolic profiles and transcriptomes in tumor models derived from patients with basal-like and classical tumors.

METHODS

Patient-derived organoids (PDOs) were established from tumor biopsies collected from patients with metastatic PDAC, including three PDOs from basal-like and five PDOs from classical tumors. Metabolic analyses included assessment of differences in metabolic activity using Seahorse Glycolysis and Mito Stress tests and 13C-glucose metabolites tracing analysis. In order to investigate the influence of mitochondrial pyruvate transport on metabolic differences, PDOs were treated with the mitochondrial pyruvate carrier 1 (MPC1) inhibitor UK-5099. Prognostic relevance of MPC1 was determined using a tumor tissue microarray (TMA) in resectable, and proteomics profiling in metastatic PDAC datasets. Whole genome and transcriptome sequencing, differential gene expression and gene set enrichment analyses were performed in PDOs.

RESULTS

Metastatic PDAC PDOs showed subtype-specific differences in glycolysis and oxidative phosphorylation (OXPHOS). Basal-like tumor-derived PDOs had a lower baseline extracellular acidification rate, but higher glycolytic reserves and oxygen consumption rate (OCR) than classical tumor-derived PDOs. OCR difference was eliminated following treatment with UK-5099. In the 13C-glucose metabolites tracing experiment, a basal-like tumor PDO showed lower fractions of some M + 2 metabolites but higher sensitivity to UK-5099 mediated reduction in M + 2 metabolites than a classical tumor PDO. Protein level analyses revealed lower MPC1 protein levels in basal-like PDAC cases and association of low MPC1 levels with clinicopathologic parameters of tumor aggressiveness in PDAC. PDO differential gene expression analyses identified additional subtype-specific cellular pathways and potential disease outcome biomarkers.

CONCLUSIONS

Our findings point to distinct metabolic profiles in PDAC subtypes with basal-like tumor PDOs showing higher OXPHOS and sensitivity to MPC1 inhibition. Subtypes-specific metabolic vulnerabilities may be exploited for selective therapeutic targeting.

Novel tumor gene expression signatures improve the overall survival prediction efficiency over tumor mutation burden and PD-L1 expression in bladder carcinoma with checkpoint blockade immunotherapy

Although immune checkpoint blockade therapy (ICBT) has revolutionized cancer treatment with good therapeutic response in a number of human cancers, including bladder cancer, many cancers still do not respond to ICBT. Analyzing genetic signatures helps the understanding of underlying biological mechanisms. Here, based on two cohorts of bladder cancer patients receiving ICBT, we identified three novel ICBT-associated signatures in the bladder cancer microenvironment, involving genomic stability, angiogenesis and RNA regulatory, which affect PD-L1 expression and patient response to ICBT. The combinations of these signatures with TMB or PD-L1 expression improved the overall survival prediction efficiency over TMB and PD-L1 expression alone for patients receiving ICBT. Moreover, we utilized two methods to search potential drugs or small-molecules that have an impact on ICBT-associated signatures. This study provides new molecular insight into ICBT response of bladder cancer and has the potential to improve the prediction accuracy for patients to benefit from ICBT.

PAF1/HIF1α axis rewires the glycolytic metabolism to fuel aggressiveness of pancreatic cancer

BACKGROUND

PAF1/PD2 deregulation contributes to tumorigenesis, drug resistance, and cancer stem cell maintenance in Pancreatic Cancer (PC). Recent studies demonstrate that metabolic reprogramming plays a role in PC progression, but the mechanism is poorly understood. Here, we focused on examining the role of PAF1/PD2 in the metabolic rewiring of PC.

METHODS

Cell lines were transfected with shRNAs to knockdown PAF1/PD2. Metabolic genes regulated by PAF1/PD2 were identified by qPCR/western blot, and metabolic assays were performed. Immunoprecipitations/ChIP were performed to identify PAF1/PD2 protein partners and confirm PAF1/HIF1α sub-complex binding to LDHA.

RESULTS

PAF1 and LDHA showed progressively increased expression in human pancreatic tumor sections. Aerobic glycolysis genes were downregulated in PAF1-depleted PC cells. Metabolic assays indicated a decreased lactate production and glucose uptake in knockdown cells. Furthermore, PAF1/PD2 depletion showed a reduced glycolytic rate and increased oxidative phosphorylation by ECAR and OCR analysis. Interestingly, we identified that HIF1α interacts and co-localizes with PAF1, specifically in PC cells. We also observed that the PAF1/PD2-HIF1α complex binds to the LDHA promoter to regulate its expression, reprogramming the metabolism to utilize the aerobic glycolysis pathway preferentially.

CONCLUSION

Overall, the results indicate that PAF1/PD2 rewires PC metabolism by interacting with HIF1α to regulate the expression of LDHA.

Hyperpolarized 13C Metabolic MRI of Patients with Pancreatic Ductal Adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDA) is the third leading cause of cancer-related death in the United States. However, early response assessment using the current approach of measuring changes in tumor size on computed tomography (CT) or MRI is challenging.

PURPOSE

To investigate the feasibility of hyperpolarized (HP) [1-13C]pyruvate MRI to quantify metabolism in the normal appearing pancreas and PDA, and to assess changes in PDA metabolism following systemic chemotherapy.

STUDY TYPE

Prospective.

SUBJECTS

Six patients (65.0 ± 7.6 years, 2 females) with locally advanced or metastatic PDA enrolled prior to starting a new line of systemic chemotherapy.

FIELD STRENGTH/SEQUENCE

3-T, T1-weighted gradient echo, metabolite-selective 13C echoplanar imaging.

ASSESSMENT

Time-resolved HP [1-13C]pyruvate data were acquired before (N = 6) and 4-weeks after (N = 3) treatment initiation. Pyruvate metabolism, as quantified by pharmacokinetic modeling and metabolite area-under-the-curve ratios, was assessed in manually segmented PDA and normal appearing pancreas ROIs (N = 5). The change in tumor metabolism before and 4-weeks after treatment initiation was assessed in primary PDA (N = 2) and liver metastases (N = 1), and was compared to objective tumor response defined by response evaluation criteria in solid tumors (RECIST) on subsequent CTs.

STATISTICAL TESTS

Descriptive tests (mean ± standard deviation), model fit error for pharmacokinetic rate constants.

RESULTS

Primary PDA showed reduced alanine-to-lactate ratios when compared to normal pancreas, due to increased lactate-to-pyruvate or reduced alanine-to-pyruvate ratios. Of the three patients who received HP [1-13C]pyruvate MRI before and 4-weeks after treatment initiation, one patient had a primary tumor with early metabolic response (increase in alanine-to-lactate) and subsequent partial response according to RECIST, one patient had a primary tumor with relatively stable metabolism and subsequent stable disease by RECIST, and one patient had metastatic PDA with increase in lactate-to-pyruvate of the liver metastases and corresponding progressive disease according to RECIST.

DATA CONCLUSION

Altered pyruvate metabolism with increased lactate or reduced alanine was observed in the primary tumor. Early metabolic response assessed at 4-weeks after treatment initiation correlated with subsequent objective tumor response assessed using RECIST.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

A Novel AMPK Inhibitor Sensitizes Pancreatic Cancer Cells to Ferroptosis Induction

Cancer cells must develop strategies to adapt to the dynamically changing stresses caused by intrinsic or extrinsic processes, or therapeutic agents. Metabolic adaptability is crucial to mitigate such challenges. Considering metabolism as a central node of adaptability, it is focused on an energy sensor, the AMP-activated protein kinase (AMPK). In a subtype of pancreatic ductal adenocarcinoma (PDAC) elevated AMPK expression and phosphorylation is identified. Using drug repurposing that combined screening experiments and chemoproteomic affinity profiling, it is identified and characterized PF-3758309, initially developed as an inhibitor of PAK4, as an AMPK inhibitor. PF-3758309 shows activity in pre-clinical PDAC models, including primary patient-derived organoids. Genetic loss-of-function experiments showed that AMPK limits the induction of ferroptosis, and consequently, PF-3758309 treatment restores the sensitivity toward ferroptosis inducers. The work established a chemical scaffold for the development of specific AMPK-targeting compounds and deciphered the framework for the development of AMPK inhibitor-based combination therapies tailored for PDAC.

Molecular characterization and clinical relevance of metabolic signature subtypes in gastric cancer

Metabolic reprogramming dictates tumor molecular attributes and therapeutic potentials. However, the comprehensive metabolic characteristics in gastric cancer (GC) remain obscure. Here, metabolic signature-based clustering analysis identifies three subtypes with distinct molecular and clinical features: MSC1 showed better prognosis and upregulation of the tricarboxylic acid (TCA) cycle and lipid metabolism, combined with frequent TP53 and RHOA mutation; MSC2 had moderate prognosis and elevated nucleotide and amino acid metabolism, enriched by intestinal histology and mismatch repair deficient (dMMR); and MSC3 exhibited poor prognosis and enhanced glycan and energy metabolism, accompanied by diffuse histology and frequent CDH1 mutation. The Shandong Provincial Hospital (SDPH) in-house dataset with matched transcriptomic, metabolomic, and spatial-metabolomic analysis also validated these findings. Further, we constructed the metabolic subtype-related prognosis gene (MSPG) scoring model to quantify the activity of individual tumors and found a positive correlation with cuproptosis signaling. In conclusion, comprehensive recognition of the metabolite signature can enhance the understanding of diversity and heterogeneity in GC.

Research on molecular characteristics of ADME-related genes in kidney renal clear cell carcinoma

Genes involved in drug absorption, distribution, metabolism, and excretion (ADME) are named ADME genes. However, the comprehensive role of ADME genes in kidney renal clear cell carcinoma (KIRC) remains unclear. Using the clinical and gene expression data of KIRC patients downloaded from The Cancer Genome Atlas (TCGA), ArrayExpress, and the Gene Expression Omnibus (GEO) databases, we cluster patients into two patterns, and the population with a relatively poor prognosis demonstrated higher level of immunosuppressive cell infiltration and higher proportion of glycolytic subtypes. Then, 17 ADME genes combination identified through the least absolute shrinkage and selection operator algorithm (LASSO, 1000 times) was utilized to calculate the ADME score. The ADME score was found to be an independent predictor of prognosis in KIRC and to be tightly associated with the infiltration level of immune cells, metabolic properties, tumor-related signaling pathways, genetic variation, and responses to chemotherapeutics. Our work revealed the characteristics of ADME in KIRC. Assessing the ADME profiles of individual patients can deepen our comprehension of tumor microenvironment (TME) features in KIRC and can aid in developing more personalized and effective therapeutic strategies.

LMO3 is a suppressor of the basal-like/squamous subtype and reduces disease aggressiveness of pancreatic cancer through glycerol 3-phosphate metabolism

Pancreatic ductal adenocarcinoma (PDAC) encompasses diverse molecular subtypes, including the classical/progenitor and basal-like/squamous subtypes, each exhibiting distinct characteristics, with the latter known for its aggressiveness. We employed an integrative approach combining transcriptome and metabolome analyses to pinpoint potential genes contributing to the basal-like/squamous subtype differentiation. Applying this approach to our NCI-UMD-German and a validation cohort, we identified LIM Domain Only 3 (LMO3), a transcription co-factor, as a candidate suppressor of the basal-like/squamous subtype. Reduced LMO3 expression was significantly associated with higher pathological grade, advanced disease stage, induction of the basal-like/squamous subtype and decreased survival among PDAC patients. In vitro experiments demonstrated that LMO3 transgene expression inhibited PDAC cell proliferation and migration/invasion, concurrently downregulating the basal-like/squamous gene signature. Metabolome analysis of patient tumors and PDAC cells revealed a metabolic program linked to elevated LMO3 and the classical/progenitor subtype, characterized by enhanced lipogenesis and suppressed amino acid metabolism. Notably, glycerol 3-phosphate (G3P) levels positively correlated with LMO3 expression and associated with improved patient survival. Furthermore, glycerol-3-phosphate dehydrogenase 1 (GPD1), a crucial enzyme in G3P synthesis, showed upregulation in LMO3-high and classical/progenitor PDAC, suggesting its potential role in mitigating disease aggressiveness. Collectively, our findings suggest that heightened LMO3 expression reduces transcriptome and metabolome characteristics indicative of basal-like/squamous tumors with decreased disease aggressiveness in PDAC patients. The observations describe LMO3 as a candidate for diagnostic and therapeutic targeting in PDAC.

Histology-Based Radiomics for [18F]FDG PET Identifies Tissue Heterogeneity in Pancreatic Cancer

Radiomics features can reveal hidden patterns in a tumor but usually lack an underlying biologic rationale. In this work, we aimed to investigate whether there is a correlation between radiomics features extracted from [18F]FDG PET images and histologic expression patterns of a glycolytic marker, monocarboxylate transporter-4 (MCT4), in pancreatic cancer. Methods: A cohort of pancreatic ductal adenocarcinoma patients (n = 29) for whom both tumor cross sections and [18F]FDG PET/CT scans were available was used to develop an [18F]FDG PET radiomics signature. By using immunohistochemistry for MCT4, we computed density maps of MCT4 expression and extracted pathomics features. Cluster analysis identified 2 subgroups with distinct MCT4 expression patterns. From corresponding [18F]FDG PET scans, radiomics features that associate with the predefined MCT4 subgroups were identified. Results: Complex heat map visualization showed that the MCT4-high/heterogeneous subgroup was correlating with a higher MCT4 expression level and local variation. This pattern linked to a specific [18F]FDG PET signature, characterized by a higher SUVmean and SUVmax and second-order radiomics features, correlating with local variation. This MCT4-based [18F]FDG PET signature of 7 radiomics features demonstrated prognostic value in an independent cohort of pancreatic cancer patients (n = 71) and identified patients with worse survival. Conclusion: Our cross-modal pipeline allows the development of PET scan signatures based on immunohistochemical analysis of markers of a particular biologic feature, here demonstrated on pancreatic cancer using intratumoral MCT4 expression levels to select [18F]FDG PET radiomics features. This study demonstrated the potential of radiomics scores to noninvasively capture intratumoral marker heterogeneity and identify a subset of pancreatic ductal adenocarcinoma patients with a poor prognosis.

Identification of subgroups and development of prognostic risk models along the glycolysis-cholesterol synthesis axis in lung adenocarcinoma

Lung cancer is one of the most dangerous malignant tumors affecting human health. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Both glycolytic and cholesterogenic pathways play critical roles in metabolic adaptation to cancer. A dataset of 585 LUAD samples was downloaded from The Cancer Genome Atlas database. We obtained co-expressed glycolysis and cholesterogenesis genes by selecting and clustering genes from Molecular Signatures Database v7.5. We compared the prognosis of different subtypes and identified differentially expressed genes between subtypes. Predictive outcome events were modeled using machine learning, and the top 9 most important prognostic genes were selected by Shapley additive explanation analysis. A risk score model was built based on multivariate Cox analysis. LUAD patients were categorized into four metabolic subgroups: cholesterogenic, glycolytic, quiescent, and mixed. The worst prognosis was the mixed subtype. The prognostic model had great predictive performance in the test set. Patients with LUAD were effectively typed by glycolytic and cholesterogenic genes and were identified as having the worst prognosis in the glycolytic and cholesterogenic enriched gene groups. The prognostic model can provide an essential basis for clinicians to predict clinical outcomes for patients. The model was robust on the training and test datasets and had a great predictive performance.

Integrating metabolic profiling of pancreatic juice with transcriptomic analysis of pancreatic cancer tissue identifies distinct clinical subgroups

Introduction

Metabolic reprogramming is a hallmark feature of pancreatic ductal adenocarcinoma (PDAC). A pancreatic juice (PJ) metabolic signature has been reported to be prognostic of oncological outcome for PDAC. Integration of PJ profiling with transcriptomic and spatial characterization of the tumor microenvironment would help in identifying PDACs with peculiar vulnerabilities.

Methods

We performed a transcriptomic analysis of 26 PDAC samples grouped into 3 metabolic clusters (M_CL) according to their PJ metabolic profile. We analyzed molecular subtypes and transcriptional differences. Validation was performed by multidimensional imaging on tumor slides.

Results

Pancreatic juice metabolic profiling was associated with PDAC transcriptomic molecular subtypes (p=0.004). Tumors identified as M_CL1 exhibited a non-squamous molecular phenotype and demonstrated longer survival. Enrichment analysis revealed the upregulation of immune genes and pathways in M_CL1 samples compared to M_CL2, the group with worse prognosis, a difference confirmed by immunofluorescence on tissue slides. Enrichment analysis of 39 immune signatures by xCell confirmed decreased immune signatures in M_CL2 compared to M_CL1 and allowed a stratification of patients associated with longer survival.

Discussion

PJ metabolic fingerprints reflect PDAC molecular subtypes and the immune microenvironment, confirming PJ as a promising source of biomarkers for personalized therapy.

Oncological outcomes in minimally invasive vs. open distal pancreatectomy: a systematic review and network meta-analysis

Background

Advancements in surgical techniques have improved outcomes in patients undergoing pancreatic surgery. To date there have been no meta-analyses comparing robotic and laparoscopic approaches for distal pancreatectomies (DP) in patients with pancreatic adenocarcinoma (PDAC). This systematic review and network meta-analysis aims to explore the oncological outcomes of laparoscopic distal pancreatectomy (LDP), robotic distal pancreatectomy (RDP) and open distal pancreatectomy (ODP).

Methods

A systematic search was conducted for studies reporting laparoscopic, robotic or open surgery for DP. Frequentist network meta-analysis of oncological outcomes (overall survival, resection margins, tumor recurrence, examined lymph nodes, administration of adjuvant therapy) were performed.

Results

Fifteen studies totalling 9,301 patients were included in the network meta-analysis. 1,946, 605 and 6,750 patients underwent LDP, RDP and ODP respectively. LDP (HR: 0.761, 95% CI: 0.642-0.901, p = 0.002) and RDP (HR: 0.757, 95% CI: 0.617-0.928, p = 0.008) were associated with overall survival (OS) benefit when compared to ODP. LDP (HR: 1.00, 95% CI: 0.793-1.27, p = 0.968) was not associated with OS benefit when compared to RDP. There were no significant differences between LDP, RDP and ODP for resection margins, tumor recurrence, examined lymph nodes and administration of adjuvant therapy.

Conclusion

This study highlights the longer OS in both LDP and RDP when compared to ODP for patients with PDAC.

Systematic Review Registration

https://www.crd.york.ac.uk/, PROSPERO (CRD42022336417).

Single-cell transcriptome reveals the heterogeneity of malignant ductal cells and the prognostic value of REG4 and SPINK1 in primary pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related deaths, with very limited therapeutic options available. This study aims to comprehensively depict the heterogeneity and identify prognostic targets for PDAC with single-cell RNA sequencing (scRNA-seq) analysis.

Methods

ScRNA-seq analysis was performed on 16 primary PDAC and three adjacent lesions. A series of analytical methods were applied for analysis in cell clustering, gene profiling, lineage trajectory analysis and cell-to-cell interactions. In vitro experiments including colony formation, wound healing and sphere formation assay were performed to assess the role of makers.

Results

A total of 32,480 cells were clustered into six major populations, among which the ductal cell cluster expressing high copy number variants (CNVs) was defined as malignant cells. Malignant cells were further subtyped into five subgroups which exhibited specific features in immunologic and metabolic activities. Pseudotime trajectory analysis indicated that components of various oncogenic pathways were differentially expressed along tumor progression. Furthermore, intensive substantial crosstalk between ductal cells and stromal cells was identified. Finally, genes (REG4 and SPINK1) screened out of differentially expressed genes (DEGs) were upregulated in PDAC cell lines. Silencing either of them significantly impaired proliferation, invasion, migration and stemness of PDAC cells.

Conclusions

Our findings offer a valuable resource for deciphering the heterogeneity of malignant ductal cells in PDAC. REG4 and SPINK1 are expected to be promising targets for PDAC therapy.

Single-Cell RNA Sequencing Analysis Reveals Metabolic Changes in Epithelial Glycosphingolipids and Establishes a Prognostic Risk Model for Pancreatic Cancer

OBJECTIVE

Metabolic reprogramming serves as a distinctive feature of cancer, impacting proliferation and metastasis, with aberrant glycosphingolipid expression playing a crucial role in malignancy. Nevertheless, limited research has investigated the connection between glycosphingolipid metabolism and pancreatic cancer.

METHODS

This study utilized a single-cell sequencing dataset to analyze the cell composition in pancreatic cancer tissues and quantified single-cell metabolism using a newly developed computational pipeline called scMetabolism. A gene signature developed from the differential expressed genes (DEGs), related to epithelial cell glycosphingolipid metabolism, was established to forecast patient survival, immune response, mutation status, and reaction to chemotherapy with pancreatic adenocarcinoma (PAAD).

RESULTS

The single-cell sequencing analysis revealed a significant increase in epithelial cell proportions in PAAD, with high glycosphingolipid metabolism occurring in the cancerous tissue. A six-gene signature prognostic model based on abnormal epithelial glycosphingolipid metabolism was created and confirmed using publicly available databases. Patients with PAAD were divided into high- and low-risk categories according to the median risk score, with those in the high-risk group demonstrating a more unfavorable survival outcome in all three cohorts, with higher rates of gene mutations (e.g., KRAS, CDKN2A), increased levels of immunosuppressive cells (macrophages, Th2 cells, regulatory T cells), and heightened sensitivity to Acetalax and Selumetinlb.

CONCLUSIONS

Abnormal metabolism of glycosphingolipids in epithelial cells may promote the development of PAAD. A model utilizing a gene signature associated with epithelial glycosphingolipids metabolism has been established, serving as a valuable indicator for the prognostic stratification of patients with PAAD.

Prediction of prognosis and immunotherapy efficacy based on metabolic landscape in lung adenocarcinoma by bulk, single-cell RNA sequencing and Mendelian randomization analyses

Immunotherapy has been a remarkable clinical advancement in cancer treatment, but only a few patients benefit from it. Metabolic reprogramming is tightly associated with immunotherapy efficacy and clinical outcomes. However, comprehensively analyzing their relationship is still lacking in lung adenocarcinoma (LUAD). Herein, we evaluated 84 metabolic pathways in TCGA-LUAD by ssGSEA. A matrix of metabolic pathway pairs was generated and a metabolic pathway-pair score (MPPS) model was established by univariable, LASSO, multivariable Cox regression analyses. The differences of metabolic reprogramming, tumor microenvironment (TME), tumor mutation burden and drug sensitivity in different MPPS groups were further explored. WGCNA and 117 machine learning algorithms were performed to identify MPPS-related genes. Single-cell RNA sequencing and in vitro experiments were used to explore the role of C1QTNF6 on TME. The results showed MPPS model accurately predicted prognosis and immunotherapy efficacy of LUAD patients regardless of sequencing platforms. High-MPPS group had worse prognosis, immunotherapy efficacy and lower immune cells infiltration, immune-related genes expression and cancer-immunity cycle scores than low-MPPS group. Seven MPPS-related genes were identified, of which C1QTNF6 was mainly expressed in fibroblasts. High C1QTNF6 expression in fibroblasts was associated with more infiltration of M2 macrophage, Treg cells and less infiltration of NK cells, memory CD8+ T cells. In vitro experiments validated silencing C1QTNF6 in fibroblasts could inhibit M2 macrophage polarization and migration. The study depicted the metabolic landscape of LUAD and constructed a MPPS model to accurately predict prognosis and immunotherapy efficacy. C1QTNF6 was a promising target to regulate M2 macrophage polarization and migration.

Deciphering fatty acid biosynthesis-driven molecular subtypes in pancreatic ductal adenocarcinoma with prognostic insights

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) poses a significant challenge due to its high heterogeneity and aggressiveness. Recognizing the urgency to delineate molecular subtypes, our study focused on the emerging field of lipid metabolism remodeling in PDAC, particularly exploring the prognostic potential and molecular classification associated with fatty acid biosynthesis.

METHODS

Gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (ssGSEA) were performed to evaluate the dysregulation of lipid metabolism in PDAC. Univariate cox analysis and the LASSO module were used to build a prognostic risk score signature. The distinction of gene expression in different risk groups was explored by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The biological function of Acyl-CoA Synthetase Long Chain Family Member 5 (ACSL5), a pivotal gene within 7-hub gene signature panel, was validated through in vitro assays.

RESULTS

Our study identified a 7-hub gene signature associated with fatty acid biosynthesis-related genes (FRGs), providing a robust tool for prognosis prediction. The high-FRGs score group displayed a poorer prognosis, decreased immune cell infiltration, and a higher tumor mutation burden. Interestingly, this group exhibited enhanced responsiveness to various compounds according to the Genomics of Drug Sensitivity in Cancer (GDSC) database. Notably, ACSL5 was upregulated in PDAC and essential for tumor progression.

CONCLUSION

In conclusion, our research defined two novel fatty acid biosynthesis-based subtypes in PDAC, characterized by distinct transcriptional profiles. These subtypes not only served as prognostic indicator, but also offered valuable insights into their metastatic propensity and therapeutic potential.

Molecular subtype construction and prognosis model for stomach adenocarcinoma characterized by metabolism-related genes

Background

Metabolic reprogramming is implicated in cancer progression. However, the impact of metabolism-associated genes in stomach adenocarcinomas (STAD) has not been thoroughly reviewed. Herein, we characterized metabolic transcription-correlated STAD subtypes and evaluated a metabolic RiskScore for evaluation survival.

Method

Genes related to metabolism were gathered from previous study and metabolic subtypes were screened using ConsensusClusterPlus in TCGA-STAD and GSE66229 dataset. The ssGSEA, MCP-Count, ESTIMATE and CIBERSORT determined the immune infiltration. A RiskScore model was established using the WGCNA and LASSO Cox regression in the TCGA-STAD queue and verified in the GSE66229 datasets. RT-qPCR was employed to measure the mRNA expressions of genes in the model.

Result

Two metabolism-related subtypes (C1 and C2) of STAD were constructed on account of the expression profiles of 113 prognostic metabolism genes with different immune outcomes and apparently distinct metabolic characteristic. The overall survival (OS) of C2 subtype was shorter than that of C1 subtype. Four metabolism-associated genes in turquoise model, which closely associated with C2 subtype, were employed to build the RiskScore (MATN3, OSBPL1A, SERPINE1, CPNE8) in TCGA-train dataset. Patients developed a poorer prognosis if they had a high RiskScore than having a low RiskScore. The promising effect of RiskScore was verified in the TCGA-test, TCGA-STAD and GSE66229 datasets. The prediction reliability of the RiskScore was validated by time-dependent receiver operating characteristic curve (ROC) and nomogram. Moreover, samples with high RiskScore had an enhanced immune status and TIDE score. Moreover, MATN3, OSBPL1A, SERPINE1 and CPNE8 mRNA levels were all elevated in SGC7901 cells. Inhibition of OSBPL1A decreased SGC7901 cells invasion numbers.

Conclusion

This work provided a new perspective into heterogeneity in metabolism and its association with immune escape in STAD. RiskScore was considered to be a strong prognostic label that could help individualize the treatment of STAD patients.

ALYREF m5C RNA methylation reader predicts bladder cancer prognosis by regulating the tumor immune microenvironment

BACKGROUND

5-Methylcytidine (m5C) methylation is a recently emerging epigenetic modification that is closely related to tumor proliferation, occurrence, and metastasis. This study aimed to investigate the clinicopathological characteristics and prognostic value of m5C regulators in bladder cancer (BLCA), and their correlation with the tumor immune microenvironment.

METHODS

Thirteen m5C RNA methylation regulators were analyzed using RNA-sequencing and corresponding clinical information obtained from the TCGA database. The Cluster Profiler package was used to analyze the gene ontology function of potential targets and enriched the Kyoto Encyclopedia of Genes and Genomes pathway. Kaplan-Meier survival analysis was used to compare survival differences using the log-rank test and univariate Cox proportional hazards regression. The correlation between signature prognostic m5C regulators and various immune cells was analyzed. Univariate and multivariate Cox regression analyses identified independence of the ALYREF gene signature.

RESULTS

Nine out of the 13 m5C RNA methylation regulators were differentially expressed in BLCA and normal samples and were co-expressed. These 9 regulators were associated with clinicopathological tumor characteristics, particularly high or low tumor risk, pT or pTNM stage, and migration. Consensus clustering analysis divides the BLCA samples into 4 clusters. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment annotation and gene ontology function analysis identified 273 upregulated and 594 downregulated genes in BLCA. Notably, only ALYREF was significantly correlated with OS (P < .05). ALYREF exhibited significant infiltration levels in macrophage cells. Therefore, we constructed a nomogram for ALYREF as an independent prognostic factor. Additionally, we observed that both the mRNA and protein levels of ALYREF were upregulated, and immunofluorescence showed that ALYREF was mainly distributed in nuclear speckles. ALYREF overexpression was significantly associated with poor OS.

CONCLUSION

Our findings demonstrated the potential of ALYREF to predict clinical prognostic risks in BLCA patients and regulate the tumor immune microenvironment. As such, ALYREF may serve as a novel prognostic indicator in BLCA patients.

Novel molecular typing reveals the risk of recurrence in patients with early-stage papillary thyroid cancer

BACKGROUND

Papillary thyroid cancer (PTC) is an indolent disease with a favorable prognosis but characterized by a high recurrence rate. We aimed to improve precise stratification of recurrence risk in PTC patients with early stage using multi-gene signatures.

PATIENTS AND METHODS

The present study was performed using data from The Cancer Genome Atlas (TCGA) and multi-center datasets. Unsupervised consensus clustering was used to obtain the optimal molecular subtypes and least absolute shrinkage and selection operator (LASSO) analysis was performed to identify potential genes for the construction of recurrence signature. Kaplan-Meier survival analysis and the log-rank test was used to detect survival differences. Harrells concordance index (C-index) was used to assess the performance of the DNA damage repair (DDR) recurrence signature.

RESULTS

Through screening 8 candidate gene sets, the entire cohort was successfully stratified into two recurrence-related molecular subtypes based on DDR genes: DDR-high subtype and DDR-low subtype. The recurrence rate of DDR-high subtype was significantly lower than DDR-low subtype [HR = 0.288 (95%CI, 0.084-0.986), P = 0.047]. Further, a two-gene DDR recurrence signature was constructed, including PER1 and EME2. The high-risk group showed a significantly worse recurrence-free survival (RFS) than the low-risk group [HR = 10.647 (95%CI, 1.363-83.197), P = 0.024]. The multi-center data demonstrated that proportion of patients with low expression of PER1 and EME2 was higher in the recurrence group than those in the non-recurrence group.

CONCLUSIONS

These findings could help accurately and reliably identify PTC patients with high risk of recurrence so that they could receive more radical and aggressive treatment strategies and more rigorous surveillance practices.

Bioinformatics-based diagnosis and evaluation of several pivotal genes and pathways associated with immune infiltration at different time points in spinal cord injury

Spinal Cord Injury (SCI) is a devastating neurological event. To assess the degree of spinal cord damage and classify the injury, it is recommended to use the 2019 version of the AIS standard. The severity of trauma was evaluated using the Trauma Severity Score, and various classification systems have been proposed for injuries at different parts and segments of the spine. Understanding the regulated signaling pathways and immune processes following SCI can lead to a better understanding of SCI-induced biomarkers and their underlying mechanisms. In this study, two gene expression datasets (GSE464 and GSE45006) from the Gene Expression Omnibus database were utilized. Differential gene expression and co-expression network analysis were performed, revealing 370 shared genes in the 3-day group and 111 shared genes in the 14-day group after SCI. The study used functional enrichment analysis methods such as Gene Set Enrichment Analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes. The ssGSEA method was used to assess the levels and composition of immune infiltration in both the sham (control) and SCI groups. The single-cell transcriptomics dataset GSE182803 was analyzed to identify genes associated with immune marker cells. Four key genes (Ptgs2, Fn1, Ccl2, and Icam1) were identified in the 3-day group, while only one gene (Cyp51) was identified in the 14-day group after SCI. The findings offer significant insights into the immune-related genes and signaling pathways involved in secondary SCI at different time points and hold potential for the development of intervention strategies for acute and chronic post-SCI.

Nutrient scavenging-fueled growth in pancreatic cancer depends on caveolae-mediated endocytosis under nutrient-deprived conditions

Pancreatic ductal adenocarcinoma (PDAC) is characterized by its nutrient-scavenging ability, crucial for tumor progression. Here, we investigated the roles of caveolae-mediated endocytosis (CME) in PDAC progression. Analysis of patient data across diverse datasets revealed a strong association of high caveolin-1 (Cav-1) expression with higher histologic grade, the most aggressive PDAC molecular subtypes, and worse clinical outcomes. Cav-1 loss markedly promoted longer overall and tumor-free survival in a genetically engineered mouse model. Cav-1-deficient tumor cell lines exhibited significantly reduced proliferation, particularly under low nutrient conditions. Supplementing cells with albumin rescued the growth of Cav-1-proficient PDAC cells, but not in Cav-1-deficient PDAC cells under low glutamine conditions. In addition, Cav-1 depletion led to significant metabolic defects, including decreased glycolytic and mitochondrial metabolism, and downstream protein translation signaling pathways. These findings highlight the crucial role of Cav-1 and CME in fueling pancreatic tumorigenesis, sustaining tumor growth, and promoting survival through nutrient scavenging.

Qualitative flow metabolic phenotype of pancreatic cancer. A new prognostic biomarker?

BACKGROUND

Retrospective analysis to investigate the relationship between the flow-metabolic phenotype and overall survival (OS) of pancreatic ductal adenocarcinoma (PDAC) and its potential clinical utility.

METHODS

Patients with histopathologically proven PDAC between 2005 and 2014 using tumor attenuation on routine pre-operative CECT as a surrogate for the vascularity and [18F]FDG-uptake as a surrogate for metabolic activity on [18F]FDG-PET.

RESULTS

In total, 93 patients (50 male, 43 female, median age 63) were included. Hypoattenuating PDAC with high [18F]FDG-uptake has the poorest prognosis (median OS 7 ± 1 months), compared to hypoattenuating PDAC with low [18F]FDG-uptake (median OS 11 ± 3 months; p = 0.176), iso- or hyperattenuating PDAC with high [18F]FDG-uptake (median OS 15 ± 5 months; p = 0.004) and iso- or hyperattenuating PDAC with low [18F]FDG-uptake (median OS 23 ± 4 months; p = 0.035). In multivariate analysis, surgery combined with tumor differentiation, tumor stage, systemic therapy and flow metabolic phenotype remained independent predictors for overall survival.

DISCUSSION

The novel qualitative flow-metabolic phenotype of PDAC using a combination of CECT and [18F]FDG-PET features, predicted significantly worse survival for hypoattenuating-high uptake pancreatic cancers compared to the other phenotypes.

Development of a Novel Lipid Metabolism-related Gene Prognostic Signature for Patients with Colorectal Cancer

BACKGROUND

The purpose of this study was to explore the expression profiles of lipid metabolism-related genes in patients with Colorectal Cancer (CRC).

METHODS

The lipid metabolism statuses of CRC patients from The Cancer Genome Atlas (TCGA) were analyzed. Risk characteristics were constructed by univariate Cox regression and minimum Absolute contraction and Selection Operator (LASSO) Cox regression. A histogram was constructed based on factors such as age, sex, TNM stage, T stage, N stage, and risk score to provide a visual tool for clinicians to predict the probability of 1-year, 3-year, and 5-year OS for CRC patients. By determining Area Under Curve (AUC) values, the time-dependent Receiver Operating characteristic Curve (ROC) was used to evaluate the efficiency of our model in predicting prognosis.

RESULTS

A novel risk signal based on lipid metabolism-related genes was constructed to predict the survival of CRC patients. Risk characteristics were shown to be an independent prognostic factor in CRC patients (p <0.001). There were significant differences in the abundance and immune characteristics of tumor-filtering immune cells between high-risk and low-risk groups. The nomogram had a high potential for clinical application and the ROC AUC value was 0.827. Moreover, ROC analysis demonstrated that the nomogram model was more accurate to predict the survival of CRC patients than age, gender, stage and risk score.

CONCLUSION

In this study, we demonstrated a lipid metabolism-related genes prognosis biomarker associated with the tumor immune micro-environment in patients with CRC.

Multi-dimension metabolic prognostic model for gastric cancer

Background

Metabolic reprogramming is involved in different stages of tumorigenesis. There are six widely recognized tumor-associated metabolic pathways, including cholesterol catabolism process, fatty acid metabolism, glutamine metabolic process, glycolysis, one carbon metabolic process, and pentose phosphate process. This study aimed to classify gastric cancer patients into different metabolic bio-similar clusters.

Method

We analyzed six tumor-associated metabolic pathways and calculated the metabolic pathway score through RNA-seq data using single sample gene set enrichment analysis. The consensus clustering analysis was performed to classify patients into different bio-similar clusters by multi-dimensional scaling. Kaplan-Meier curves were presented between different metabolic bio-similar groups for OS analysis.

Results

A training set of 370 patients from the Cancer Genome Atlas database with primary gastric cancer was chosen. Patients were classified into four metabolic bio-similar clusters, which were identified as metabolic non-specificity, metabolic-active, cholesterol-silence, and metabolic-silence clusters. Survival analysis showed that patients in metabolic-active cluster and metabolic-silence cluster have significantly poor prognosis than other patients (p=0.031). Patients in metabolic-active cluster and metabolic-silence cluster had significantly higher intra-tumor heterogeneity than other patients (p=0.032). Further analysis was performed in metabolic-active cluster and cholesterol-silence cluster. Three cell-cycle-related pathways, including G2M checkpoints, E2F targets, and MYC targets, were significantly upregulated in metabolic-active cluster than in cholesterol-silence cluster. A validation set of 192 gastric cancer patients from the Gene Expression Omnibus data portal verified that metabolic bio-similar cluster can predict prognosis in gastric cancer.

Conclusion

Our study established a multi-dimension metabolic prognostic model in gastric cancer, which may be feasible for predicting clinical outcome.

Genomic analysis and filtration of novel prognostic biomarkers based on metabolic and immune subtypes in pancreatic cancer

PURPOSE

Patients with pancreatic cancer (PC) can be classified into various molecular subtypes and benefit from some precise therapy. Nevertheless, the interaction between metabolic and immune subtypes in the tumor microenvironment (TME) remains unknown. We hope to identify molecular subtypes related to metabolism and immunity in pancreatic cancer METHODS: Unsupervised consensus clustering and ssGSEA analysis were utilized to construct molecular subtypes related to metabolism and immunity. Diverse metabolic and immune subtypes were characterized by distinct prognoses and TME. Afterward, we filtrated the overlapped genes based on the differentially expressed genes (DEGs) between the metabolic and immune subtypes by lasso regression and Cox regression, and used them to build risk score signature which led to PC patients was categorized into high- and low-risk groups. Nomogram were built to predict the survival rates of each PC patient. RT-PCR, in vitro cell proliferation assay, PC organoid, immunohistochemistry staining were used to identify key oncogenes related to PC RESULTS: High-risk patients have a better response for various chemotherapeutic drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. We built a nomogram with the risk group, age, and the number of positive lymph nodes to predict the survival rates of each PC patient with average 1-year, 2-year, and 3-year areas under the curve (AUCs) equal to 0.792, 0.752, and 0.751. FAM83A, KLF5, LIPH, MYEOV were up-regulated in the PC cell line and PC tissues. Knockdown of FAM83A, KLF5, LIPH, MYEOV could reduce the proliferation in the PC cell line and PC organoids CONCLUSION: The risk score signature based on the metabolism and immune molecular subtypes can accurately predict the prognosis and guide treatments of PC, meanwhile, the metabolism-immune biomarkers may provide novel target therapy for PC.

Coupling magnetic levitation of graphene oxide–protein complexes with blood levels of glucose for early detection of pancreatic adenocarcinoma

Introduction

Pancreatic adenocarcinoma (PDAC) has a poor prognosis since often diagnosed too late. Dyslipidemia and hyperglycemia are considered risk factors, but the presence of the tumor itself can determine the onset of these disorders. Therefore, it is not easy to predict which subjects with diabetes or dyslipidemia will develop or have already developed PDAC. Over the past decade, tests based on the use of nanotechnology, alone or coupled with common laboratory tests (e.g., hemoglobin levels), have proven useful in aiding the diagnosis of PDAC. Tests based on magnetic levitation (MagLev) have demonstrated high diagnostic accuracy in compliance with the REASSURED criteria. Here, we aimed to assess the ability of the MagLev test in detecting PDAC when coupled with the blood levels of glycemia, cholesterol, and triglycerides.

Methods

Blood samples from 24 PDAC patients and 22 healthy controls were collected. Human plasma was let to interact with graphene oxide (GO) nanosheets and the emerging coronated systems were put in the MagLev device. Outcomes from Maglev experiments were coupled to glycemia, cholesterol, and triglycerides levels. Linear discriminant analysis (LDA) was carried out to evaluate the classification ability of the test in terms of specificity, sensitivity, and global accuracy. Statistical analysis was performed with Matlab (MathWorks, Natick, MA, USA, Version R2022a) software.

Results

The positions of the levitating bands were measured at the starting point (i.e., as soon as the cuvette containing the sample was subjected to the magnetic field). Significant variations in the starting position of levitating nanosystems in controls and PDACs were detected. The combination of the MagLev outcomes with the blood glycemic levels returned the best value of global accuracy (91%) if compared to the coupling with those of cholesterol and triglycerides (global accuracy of ~ 77% and 84%, respectively).

Conclusion

If confirmed by further studies on larger cohorts, a multiplexed Maglev-based nanotechnology-enabled blood test could be employed as a screening tool for PDAC in populations with hyperglycemia.

Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC)

Background: Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease characterized by a diverse tumor microenvironment. The heterogeneous cellular composition of PDAC makes it challenging to study molecular features of tumor cells using extracts from bulk tumor. The metabolic features in tumor cells from clinical samples are poorly understood, and their impact on clinical outcomes are unknown. Our objective was to identify the metabolic features in the tumor compartment that are most clinically impactful. Methods: A computational deconvolution approach using the DeMixT algorithm was applied to bulk RNASeq data from The Cancer Genome Atlas to determine the proportion of each gene's expression that was attributable to the tumor compartment. A machine learning algorithm designed to identify features most closely associated with survival outcomes was used to identify the most clinically impactful metabolic genes. Results: Two metabolic subtypes (M1 and M2) were identified, based on the pattern of expression of the 26 most important metabolic genes. The M2 phenotype had a significantly worse survival, which was replicated in three external PDAC cohorts. This PDAC subtype was characterized by net glycogen catabolism, accelerated glycolysis, and increased proliferation and cellular migration. Single cell data demonstrated substantial intercellular heterogeneity in the metabolic features that typified this aggressive phenotype. Conclusion: By focusing on features within the tumor compartment, two novel and clinically impactful metabolic subtypes of PDAC were identified. Our study emphasizes the challenges of defining tumor phenotypes in the face of the significant intratumoral heterogeneity that typifies PDAC. Further studies are required to understand the microenvironmental factors that drive the appearance of the metabolic features characteristic of the aggressive M2 PDAC phenotype.

Targeting neoadjuvant chemotherapy-induced metabolic reprogramming in pancreatic cancer promotes anti-tumor immunity and chemo-response

The molecular dynamics of pancreatic ductal adenocarcinoma (PDAC) under chemotherapy remain incompletely understood. The widespread use of neoadjuvant chemotherapy (NAC) provides a unique opportunity to investigate PDAC samples post-chemotherapy. Leveraging a cohort from Fudan University Shanghai Cancer Center, encompassing PDAC samples with and without exposure to neoadjuvant albumin-bound paclitaxel and gemcitabine (AG), we have compiled data from single-cell and spatial transcriptomes, proteomes, bulk transcriptomes, and metabolomes, deepening our comprehension of the molecular changes in PDACs in response to chemotherapy. Metabolic flux analysis reveals that NAC induces a reprogramming of PDAC metabolic patterns and enhances immunogenicity. Notably, NAC leads to the downregulation of glycolysis and the upregulation of CD36. Tissue microarray analysis demonstrates that high CD36 expression is linked to poorer survival in patients receiving postoperative AG. Targeting CD36 synergistically improves the PDAC response to AG both in vitro and in vivo, including patient-derived preclinical models.

Glycolysis-cholesterol metabolic axis in immuno-oncology microenvironment: emerging role in immune cells and immunosuppressive signaling

Cell proliferation and function require nutrients, energy, and biosynthesis activity to duplicate repertoires for each daughter. It is therefore not surprising that tumor microenvironment (TME) metabolic reprogramming primarily orchestrates the interaction between tumor and immune cells. Tumor metabolic reprogramming affords bioenergetic, signaling intermediates, and biosynthesis requirements for both malignant and immune cells. Different immune cell subsets are recruited into the TME, and these manifestations have distinct effects on tumor progression and therapeutic outcomes, especially the mutual contribution of glycolysis and cholesterol metabolism. In particularly, glycolysis-cholesterol metabolic axis interconnection plays a critical role in the TME modulation, and their changes in tumor metabolism appear to be a double-edged sword in regulating various immune cell responses and immunotherapy efficacy. Hence, we discussed the signature manifestation of the glycolysis-cholesterol metabolic axis and its pivotal role in tumor immune regulation. We also highlight how hypothetical combinations of immunotherapy and glycolysis/cholesterol-related metabolic interventions unleash the potential of anti-tumor immunotherapies, as well as developing more effective personalized treatment strategies.

Metabolic classification suggests the GLUT1/ALDOB/G6PD axis as a therapeutic target in chemotherapy-resistant pancreatic cancer

Metabolic reprogramming is known as an emerging mechanism of chemotherapy resistance, but the metabolic signatures of pancreatic ductal adenocarcinomas (PDACs) remain unclear. Here, we characterize the metabolomic profile of PDAC organoids and classify them into glucomet-PDAC (high glucose metabolism levels) and lipomet-PDAC (high lipid metabolism levels). Glucomet-PDACs are more resistant to chemotherapy than lipomet-PDACs, and patients with glucomet-PDAC have a worse prognosis. Integrated analyses reveal that the GLUT1/aldolase B (ALDOB)/glucose-6-phosphate dehydrogenase (G6PD) axis induces chemotherapy resistance by remodeling glucose metabolism in glucomet-PDAC. Increased glycolytic flux, G6PD activity, and pyrimidine biosynthesis are identified in glucomet-PDAC with high GLUT1 and low ALDOB expression, and these phenotypes could be reversed by inhibiting GLUT1 expression or by increasing ALDOB expression. Pharmacological inhibition of GLUT1 or G6PD enhances the chemotherapy response of glucomet-PDAC. Our findings uncover potential metabolic heterogeneity related to differences in chemotherapy sensitivity in PDAC and develop a promising pharmacological strategy for patients with chemotherapy-resistant glucomet-PDAC through the combination of chemotherapy and GLUT1/ALDOB/G6PD axis inhibitors.

Trogocytosis of cancer-associated fibroblasts promotes pancreatic cancer growth and immune suppression via phospholipid scramblase anoctamin 6 (ANO6)

In pancreatic ductal adenocarcinoma (PDAC), the fibroblastic stroma constitutes most of the tumor mass and is remarkably devoid of functional blood vessels. This raises an unresolved question of how PDAC cells obtain essential metabolites and water-insoluble lipids. We have found a critical role for cancer-associated fibroblasts (CAFs) in obtaining and transferring lipids from blood-borne particles to PDAC cells via trogocytosis of CAF plasma membranes. We have also determined that CAF-expressed phospholipid scramblase anoctamin 6 (ANO6) is an essential CAF trogocytosis regulator required to promote PDAC cell survival. During trogocytosis, cancer cells and CAFs form synapse-like plasma membranes contacts that induce cytosolic calcium influx in CAFs via Orai channels. This influx activates ANO6 and results in phosphatidylserine exposure on CAF plasma membrane initiating trogocytosis and transfer of membrane lipids, including cholesterol, to PDAC cells. Importantly, ANO6-dependent trogocytosis also supports the immunosuppressive function of pancreatic CAFs towards cytotoxic T cells by promoting transfer of excessive amounts of cholesterol. Further, blockade of ANO6 antagonizes tumor growth via disruption of delivery of exogenous cholesterol to cancer cells and reverses immune suppression suggesting a potential new strategy for PDAC therapy.

A protein-based machine learning approach to the identification of inflammatory subtypes in pancreatic ductal adenocarcinoma

BACKGROUND/OBJECTIVES

The inherently immunosuppressive tumor microenvironment along with the heterogeneity of pancreatic ductal adenocarcinoma (PDAC) limits the effectiveness of available treatment options and contributes to the disease lethality. Using a machine learning algorithm, we hypothesized that PDAC may be categorized based on its microenvironment inflammatory milieu.

METHODS

Fifty-nine tumor samples from patients naïve to treatment were homogenized and probed for 41 unique inflammatory proteins using a multiplex assay. Subtype clustering was determined using t-distributed stochastic neighbor embedding (t-SNE) machine learning analysis of cytokine/chemokine levels. Statistics were performed using Wilcoxon rank sum test and Kaplan-Meier survival analysis.

RESULTS

t-SNE analysis of tumor cytokines/chemokines revealed two distinct clusters, immunomodulating and immunostimulating. In pancreatic head tumors, patients in the immunostimulating group (N = 26) were more likely to be diabetic (p = 0.027), but experienced less intraoperative blood loss (p = 0.0008). Though there were no significant differences in survival (p = 0.161), the immunostimulating group trended toward longer median survival by 9.205 months (11.28 vs. 20.48 months).

CONCLUSION

A machine learning algorithm identified two distinct subtypes within the PDAC inflammatory milieu, which may influence diabetes status as well as intraoperative blood loss. Opportunity exists to further explore how these inflammatory subtypes may influence treatment response, potentially elucidating targetable mechanisms of PDAC's immunosuppressive tumor microenvironment.

Features of metabolism associated molecular patterns in pancreatic ductal adenocarcinoma

Exploring pancreatic ductal adenocarcinoma (PDAC) metabolic landscape would contribute to further understand PDAC from the metabolic perspective and provide more details for precise treatment design. This study aims to describe metabolic landscape of PDAC. Bioinformatics analysis was used to investigate the differences of genome, transcriptome, and proteome levels of metabolic patterns. Three subtypes (MC1, MC2, and MC3) were identified and characterized as distinct metabolic patterns. MC1, enriched in lipid metabolism and amino acid metabolism signatures, was associated with lower abundance of immune cells and stromal cells, and non-response to immunotherapy. MC2 displayed immune-activated characteristics, minor genome alterations and good response to immunotherapy. MC3 was characterized by high glucose metabolism, high pathological grade, immune-suppressed features, poor prognosis, and epithelial-mesenchymal transition phenotype. A ninety-three gene classifier preformed robust prediction and high accuracy (training set: 93.7%; validation set 1: 85.0%; validation set 2: 83.9%). Using random forest classifier, probabilities of three patterns could be predicted on pancreatic cancer cell lines, which could be used to find vulnerable targets in response to both genetic and drug perturbation. Our study revealed features of PDAC metabolic landscape, which could be expected to provide a reference for prognosis prediction and precise treatment design.

Machine learning-based integration develops a metabolism-derived consensus model for improving immunotherapy in pancreatic cancer

BACKGROUND

Pancreatic cancer (PAC) is one of the most malignant cancer types and immunotherapy has emerged as a promising treatment option. PAC cells undergo metabolic reprogramming, which is thought to modulate the tumor microenvironment (TME) and affect immunotherapy outcomes. However, the metabolic landscape of PAC and its association with the TME remains largely unexplored.

METHODS

We characterized the metabolic landscape of PAC based on 112 metabolic pathways and constructed a novel metabolism-related signature (MBS) using data from 1,188 patients with PAC. We evaluated the predictive performance of MBS for immunotherapy outcomes in 11 immunotherapy cohorts from both bulk-RNA and single-cell perspectives. We validated our results using immunohistochemistry, western blotting, colony-formation assays, and an in-house cohort.

RESULTS

MBS was found to be negatively associated with antitumor immunity, while positively correlated with cancer stemness, intratumoral heterogeneity, and immune resistant pathways. Notably, MBS outperformed other acknowledged signatures for predicting immunotherapy response in multiple immunotherapy cohorts. Additionally, MBS was a powerful and robust biomarker for predicting prognosis compared with 66 published signatures. Further, we identified dasatinib and epothilone B as potential therapeutic options for MBS-high patients, which were validated through experiments.

CONCLUSIONS

Our study provides insights into the mechanisms of immunotherapy resistance in PAC and introduces MBS as a robust metabolism-based indicator for predicting response to immunotherapy and prognosis in patients with PAC. These findings have significant implications for the development of personalized treatment strategies in patients with PAC and highlight the importance of considering metabolic pathways and immune infiltration in TME regulation.

Classification of subtypes and identification of dysregulated genes in sepsis

Background

Sepsis is a clinical syndrome with high mortality. Subtype identification in sepsis is meaningful for improving the diagnosis and treatment of patients. The purpose of this research was to identify subtypes of sepsis using RNA-seq datasets and further explore key genes that were deregulated during the development of sepsis.

Methods

The datasets GSE95233 and GSE13904 were obtained from the Gene Expression Omnibus database. Differential analysis of the gene expression matrix was performed between sepsis patients and healthy controls. Intersection analysis of differentially expressed genes was applied to identify common differentially expressed genes for enrichment analysis and gene set variation analysis. Obvious differential pathways between sepsis patients and healthy controls were identified, as were developmental stages during sepsis. Then, key dysregulated genes were revealed by short time-series analysis and the least absolute shrinkage and selection operator model. In addition, the MCPcounter package was used to assess infiltrating immunocytes. Finally, the dysregulated genes identified were verified using 69 clinical samples.

Results

A total of 898 common differentially expressed genes were obtained, which were chiefly related to increased metabolic responses and decreased immune responses. The two differential pathways (angiogenesis and myc targets v2) were screened on the basis of gene set variation analysis scores. Four subgroups were identified according to median expression of angiogenesis and myc target v2 genes: normal, myc target v2, mixed-quiescent, and angiogenesis. The genes CHPT1, CPEB4, DNAJC3, MAFG, NARF, SNX3, S100A9, S100A12, and METTL9 were recognized as being progressively dysregulated in sepsis. Furthermore, most types of immune cells showed low infiltration in sepsis patients and had a significant correlation with the key genes. Importantly, all nine key genes were highly expressed in sepsis patients.

Conclusion

This study revealed novel insight into sepsis subtypes and identified nine dysregulated genes associated with immune status in the development of sepsis. This study provides potential molecular targets for the diagnosis and treatment of sepsis.