Single-Cell Transcriptome Reveals the Metabolic and Clinical Features of a Highly Malignant Cell Subpopulation in Pancreatic Ductal Adenocarcinoma

Single-cell transcriptomics reveal the prognostic roles of epithelial and T cells and DNA methylation-based prognostic models in pancreatic cancer

BACKGROUND

Pancreatic adenocarcinoma (PDAC) exhibits a complex microenvironment with diverse cell populations influencing patient prognosis. Single-cell RNA sequencing (scRNA-seq) was used to identify prognosis-related cell types, and DNA methylation (DNAm)-based models were developed to predict outcomes based on their cellular characteristics.

METHODS

We integrated scRNA-seq, bulk data, and clinical information to identify key cell populations associated with prognosis. The TCGA dataset was used for validation, and cell composition was inferred from DNAm data. Prognostic models were constructed based on cell-type-specific DNAm markers, and genomic features were compared across risk groups. Nomograms were created to assess treatment responses in different risk levels.

RESULTS

Epithelial and T cells were major prognostic factors. Genomic analysis showed that epithelial cells in PDAC followed a malignant trajectory. DNAm data from TCGA confirmed the association of higher epithelial and T cell proportions with worse prognosis. Prognostic models based on DNAm markers of these cells effectively predicted patient survival, especially 5-year overall survival (AUC = 0.834). High-risk group with epithelial cell model showed altered pathways (tight junctions, NOTCH, and P53 signaling), while high-risk group with T cell model had changes in glycolysis, hypoxia, and NOTCH signaling, with more KRAS or TP53 mutations. Low-risk groups in the T cell model displayed stronger antitumor immune responses. Treatment predictions and nomograms were developed for clinical use.

CONCLUSIONS

scRNA-seq and DNAm data integration enabled the creation of predictive models based on epithelial and T cell-specific methylation patterns, offering robust prognosis prediction for PDAC patients.

Single-cell transcriptome analysis identifies a novel tumor-associated macrophage subtype predicting better prognosis in pancreatic ductal adenocarcinoma

Background

Characterized by an immune-suppressive tumor microenvironment (TME), pancreatic ductal adenocarcinoma (PDAC) is well-known for its poor prognosis. Tumor associated macrophages (TAMs) play a critical role in PDAC TME. An in-depth understanding of TAMs is helpful to develop new strategies for immunotherapy.

Methods

A large number of single-cell RNA sequencing data and bulk RNA sequencing data of PDAC were collected for systematic bioinformatics analysis. Characterize subtypes of TAMs at single-cell resolution and its effect on prognosis. Differential gene analysis and cell-cell communication were used to describe the effect on prognosis and validated by the TCGA dataset.

Results

We used two prognosis-favorable genes, SLC12A5 and ENPP2, to identify a benign M2-like TAMs (bM2-like TAMs), which shared similarities with C1QC + TAMs, CXCL9+ TAMs and CD169+ TAMs, by analyzing scRNA-seq data and bulk RNA data of PDAC. The bM2-like TAMs were revealed to promote T cell activation and proliferation through ALCAM/CD6 interaction. Meanwhile, the bM2-like TAMs were responsible for stroma modeling by altering αSMA+/αSMA-cell ratio. On the contrast, the rest of the M2-like TAMs were defined as malignant M2-like TAMs (mM2-like TAMs), partly overlapping with SPP1+ TAMs. mM2-like TAMs were revealed to promote tumor progression by secretion of MIF and SPP1.

Conclusion

Our study used two prognosis-favorable genes to divide M2-like TAMs of PDAC into anti-tumor bM2-like TAMs and pro-tumor mM2-like TAMs. The bM2-like TAMs activate T cells through ALCAM/CD6 and generate prognosis-favorable αSMA+ myofibroblasts through secreting TGFβ, which brings insight into heterogeneity of TAMs, prognosis prediction and immunotherapy of PDAC.

Lactate enhances NMNAT1 lactylation to sustain nuclear NAD+ salvage pathway and promote survival of pancreatic adenocarcinoma cells under glucose-deprived conditions

The aim of this study was to investigate the underlying molecular mechanism behind the promotion of cell survival under conditions of glucose deprivation by l-lactate. To accomplish this, we performed tissue microarray and immunohistochemistry staining to analyze the correlation between the abundance of pan-Lysine lactylation and prognosis. In vivo evaluations of tumor growth were conducted using the KPC and nude mice xenograft tumor model. For mechanistic studies, multi-omics analysis, RNA interference, and site-directed mutagenesis techniques were utilized. Our findings robustly confirmed that l-lactate promotes cell survival under glucose deprivation conditions, primarily by relying on GLS1-mediated glutaminolysis to support mitochondrial respiration. Mechanistically, we discovered that l-lactate enhances the NMNAT1-mediated NAD+ salvage pathway while concurrently inactivating p-38 MAPK signaling and suppressing DDIT3 transcription. Notably, Pan-Kla abundance was significantly upregulated in patients with Pancreatic adenocarcinoma (PAAD) and associated with poor prognosis. We identified the 128th Lysine residue of NMNAT1 as a critical site for lactylation and revealed EP300 as a key lactyltransferase responsible for catalyzing lactylation. Importantly, we elucidated that lactylation of NMNAT1 enhances its nuclear localization and maintains enzymatic activity, thereby supporting the nuclear NAD+ salvage pathway and facilitating cancer growth. Finally, we demonstrated that the NMNAT1-dependent NAD+ salvage pathway promotes cell survival under glucose deprivation conditions and is reliant on the activity of Sirt1. Collectively, our study has unraveled a novel molecular mechanism by which l-lactate promotes cell survival under glucose deprivation conditions, presenting a promising strategy for targeting lactate and NAD+ metabolism in the treatment of PAAD.

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.

Studying the mechanism underlying lipid metabolism in osteosarcoma based on transcriptomic RNA sequencing and single-cell data

BACKGROUND

We aimed to provide a new typing method for osteosarcoma (OS) based on single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the perspective of lipid metabolism and examine its potential mechanisms in the onset and progression of OS.

METHODS

Scores for six lipid metabolic pathways were calculated by single-sample gene set enrichment analysis (ssGSEA) based on a scRNA-seq dataset and three microarray expression profiles. Subsequently, cluster typing was conducted using unsupervised consistency clustering. Furthermore, single-cell clustering and dimensionality-reduction analyses identified cell subtypes. Finally, an analysis of cellular receptors was performed using CellphoneDB to identify cellular communication.

RESULTS

OS was classified into three subtypes based on lipid metabolic pathways. Among them, patients in clust3 showed poor prognoses, whereas those in clust1 and clust2 exhibited good prognoses. In addition, ssGSEA analysis showed that patients in clust3 had lower immune cell scores. Moreover, the Th17 cell differentiation pathway was significantly differentially enriched between clust2 and clust3, with lower enrichment scores for metabolic pathways in the former relative to clust1 and clust2. In total, 24 genes were upregulated between clust1 and clust2, whereas 20 were downregulated in clust3. These observations were validated by single-cell data analysis. Finally, through scRNA-seq data analysis, we identified nine ligand-receptor pairs particularly critical for communication between normal and malignant cells.

CONCLUSIONS

Three clusters were identified and the single-cell analysis revealed that malignant cells dominated lipid metabolism patterns in tumors, thereby influencing the tumor microenvironment.

Single Cell RNA Sequencing: A New Frontier in Pancreatic Ductal Adenocarcinoma

Simple Summary

Pancreatic cancer has a very low survival rate for several reasons. One of those is primarily due to the difficulty in diagnosing it at an early stage. For this reason, it is important to refine our understanding of this disease to guide the development of new diagnostic and therapeutic modalities to combat this fatal illness. Here we attempt to provide a review of current progress in utilizing single-cell RNA sequencing (scRNA-seq) techniques in the molecular profiling of pancreatic ductal adenocarcinoma.

Abstract

Pancreatic ductal adenocarcinoma is a malignancy with a high mortality rate. It exhibits significant heterogeneity in metabolic pathways which are associated with its progression. In this review, we discuss the role of single cell RNA sequencing in unraveling the metabolic and clinical features of these highly malignant tumors.