Metabolic detection and systems analyses of pancreatic ductal adenocarcinoma through machine learning, lipidomics, and multi-omics.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang G
PROVIDER: S-EPMC8694594 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Pancreatic ductal adenocarcinoma (PDAC) is the most common and among the deadliest of pancreatic cancers. Its 5-year survival is only ∼8%. Pancreatic cancers are a heterogeneous group of diseases, of which PDAC is particularly aggressive. Like many other cancers, PDAC also starts as a pre-invasive precursor lesion (known as pancreatic intraepithelial neoplasia, PanIN), which offers an opportunity for both early detection and early treatment. Even advanced PDAC can benefit from prognostic biomarkers. However, reliable biomarkers for early diagnosis or those for prognosis of therapy remain an unfulfilled goal for PDAC. In this study, we selected 153 PDAC patients from the TCGA database and used their clinical, DNA methylation, gene expression, and micro-RNA (miRNA) and long non-coding RNA (lncRNA) expression data for multi-omics analysis. Differential methylations at about 12,000 CpG sites were observed in PDAC tumor genomes, with about 61% of them hypermethylated, predominantly in the promoter regions and in CpG-islands. We correlated promoter methylation and gene expression for mRNAs and identified 17 genes that were previously recognized as PDAC biomarkers. Similarly, several genes (B3GNT3, DMBT1, DEPDC1B) and lncRNAs (PVT1, and GATA6-AS) are strongly correlated with survival, which have not been reported in PDAC before. Other genes such as EFR3B, whose biological roles are not well known in mammals are also found to strongly associated with survival. We further identified 406 promoter methylation target loci associated with patients survival, including known esophageal squamous cell carcinoma biomarkers, cg03234186 (ZNF154), and cg02587316, cg18630667, and cg05020604 (ZNF382). Overall, this is one of the first studies that identified survival associated genes using multi-omics data from PDAC patients.
| S-EPMC6659773 | biostudies-literature
Project description:The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is associated with the tumour heterogeneity. To explore intra- and inter-tumoural heterogeneity in PDAC, we analysed the multi-omics profiles of 61 PDAC lesion samples, along with the matched pancreatic normal tissue samples, from 19 PDAC patients. Haematoxylin and Eosin (H&E) staining revealed that diversely differentiated lesions coexisted both within and across individual tumours. Whole exome sequencing (WES) of samples from multi-region revealed diverse types of mutations in diverse genes between cancer cells within a tumour and between tumours from different individuals. The copy number variation (CNV) analysis also showed that PDAC exhibited intra- and inter-tumoural heterogeneity in CNV and that high average CNV burden was associated poor prognosis of the patients. Phylogenetic tree analysis and clonality/timing analysis of mutations displayed diverse evolutionary pathways and spatiotemporal characteristics of genomic alterations between different lesions from the same or different tumours. Hierarchical clustering analysis illustrated higher inter-tumoural heterogeneity than intra-tumoural heterogeneity of PDAC at the transcriptional levels as lesions from the same patients are grouped into a single cluster. Immune marker genes are differentially expressed in different regions and tumour samples as shown by tumour microenvironment (TME) analysis. TME appeared to be more heterogeneous than tumour cells in the same patient. Lesion-specific differentially methylated regions (DMRs) were identified by methylated DNA immunoprecipitation sequencing (MeDIP-seq). Furthermore, the integration analysis of multi-omics data showed that the mRNA levels of some genes, such as PLCB4, were significantly correlated with the gene copy numbers. The mRNA expressions of potential PDAC biomarkers ZNF521 and KDM6A were correlated with copy number alteration and methylation, respectively. Taken together, our results provide a comprehensive view of molecular heterogeneity and evolutionary trajectories of PDAC and may guide personalised treatment strategies in PDAC therapy.
| S-EPMC8782496 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor characterized by rapid progression, early metastasis, high recurrence, and limited responsiveness to conventional therapies. The 5-year survival rate of PDAC is extremely low (<8%), which lacks effective prognostic evaluation indicators. In this study, we used xCell to analyze infiltrating immune cells in a tumor and through the univariate and multivariate Cox analyses screened out two prognosis-related immune cells, CD4+TN and common lymphoid progenitor (CLP), which were used to construct a Cox model and figure out the risk-score. It was found that the constructed model could greatly improve the sensitivity of prognostic evaluation, that the higher the risk-score, the worse the prognosis. In addition, the risk-score could also identify molecular subtypes with poor prognosis and immunotherapy sensitivity. Through transcriptome and whole-exome sequencing analysis of PDAC dataset from The Cancer Genome Atlas (TCGA), it was found that copy number deletion and low expression of CCL19 might be crucial factors to affect the risk-score. Lastly, validation of the above findings was confirmed not only in Gene Expression Omnibus (GEO) datasets but also in our PDAC patient samples, Peking2020 cohort.
| S-EPMC8580856 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Here we show that shotgun and targeted protein sequencing can be used to identify potential prognostic biomarkers in formalin-fixed paraffin-embedded specimens from 9 patients with PDAC with "short" survival (<12 months) and 10 patients with "long" survival (>45 months) undergoing surgical resection. A total of 24 and 147 proteins were significantly upregulated [fold change ?2 or ?0.5 and P<0.05; or different detection frequencies (?5 samples)] in patients with "short" survival (including GLUT1) and "long" survival (including C9orf64, FAM96A, CDH1 and CDH17), respectively. STRING analysis of these proteins indicated a tight protein-protein interaction network centered on TP53. Ingenuity pathway analysis linked proteins representing "activated stroma factors" and "basal tumor factors" to poor prognosis of PDAC. It also highlighted TCF1 and CTNNB1 as possible upstream regulators. Further parallel reaction monitoring verified that seven proteins were upregulated in patients with "short" survival (MMP9, CLIC3, MMP8, PRTN3, P4HA2, THBS1 and FN1), while 18 proteins were upregulated in patients with "long" survival, including EPCAM, LGALS4, VIL1, CLCA1 and TPPP3. Thus, we verified 25 protein biomarker candidates for PDAC prognosis at the tissue level. Furthermore, an activated stroma status and protein-protein interactions with TP53 might be linked to poor prognosis of PDAC.
| S-EPMC5839402 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers. Developing biomarkers for early detection and chemotherapeutic response prediction is crucial to improve the dismal prognosis of PDAC patients. However, molecular cancer signatures based on transcriptome analysis do not reflect intratumoral heterogeneity. To explore a more accurate stratification of PDAC phenotypes in an easily accessible matrix, plasma metabolome analysis using MxP® Global Profiling and MxP® Lipidomics was performed in 361 PDAC patients. We identified three metabolic PDAC subtypes associated with distinct complex lipid patterns. Subtype 1 was associated with reduced ceramide levels and a strong enrichment of triacylglycerols. Subtype 2 demonstrated increased abundance of ceramides, sphingomyelin and other complex sphingolipids, whereas subtype 3 showed decreased levels of sphingolipid metabolites in plasma. Pathway enrichment analysis revealed that sphingolipid-related pathways differ most among subtypes. Weighted correlation network analysis (WGCNA) implied PDAC subtypes differed in their metabolic programs. Interestingly, a reduced expression among related pathway genes in tumor tissue was associated with the lowest survival rate. However, our metabolic PDAC subtypes did not show any correlation to the described molecular PDAC subtypes. Our findings pave the way for further studies investigating sphingolipids metabolisms in PDAC.
| S-EPMC8305839 | biostudies-literature
Project description:PurposePancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal disease that develops relatively symptom-free and is therefore advanced at the time of diagnosis. The absence of early symptoms and effective treatments has created a critical need for identifying and developing new noninvasive biomarkers and therapeutic targets.Experimental designWe investigated the metabolism of a panel of PDAC cell lines in culture and noninvasively in vivo with (1)H magnetic resonance spectroscopic imaging (MRSI) to identify noninvasive biomarkers and uncover potential metabolic targets.ResultsWe observed elevated choline-containing compounds in the PDAC cell lines and tumors. These elevated choline-containing compounds were easily detected by increased total choline (tCho) in vivo, in spectroscopic images obtained from tumors. Principal component analysis of the spectral data identified additional differences in metabolites between immortalized human pancreatic cells and neoplastic PDAC cells. Molecular characterization revealed overexpression of choline kinase (Chk)-α, choline transporter 1 (CHT1), and choline transporter-like protein 1 (CTL1) in the PDAC cell lines and tumors.ConclusionsCollectively, these data identify new metabolic characteristics of PDAC and reveal potential metabolic targets. Total choline detected with (1)H MRSI may provide an intrinsic, imaging probe-independent biomarker to complement existing techniques in detecting PDAC. The expression of Chk-α, CHT1, and CTL1 may provide additional molecular markers in aspirated cytological samples.
| S-EPMC4297549 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC), an extremely aggressive invasive cancer, is the fourth most common cause of cancer-related death in the United States. The higher mortality in PDAC is often attributed to the inability to detect it until it has reached advanced stages. The major challenge in tackling PDAC is due to its elusive pathology, minimal effectiveness, and resistance to existing therapeutics. The aggressiveness of PDAC is due to the capacity of tumor cells to alter their metabolism, utilize the diverse available fuel sources to adapt and grow in a hypoxic and harsh environment. Therapeutic resistance is due to the presence of thick stroma with poor angiogenesis, thus making drug delivery to tumor cells difficult. Investigating the metabolic mediators and enzymes involved in metabolic reprogramming may lead to the identification of novel therapeutic targets. The metabolic mediators of glucose, glutamine, lipids, nucleotides, amino acids and mitochondrial metabolism have emerged as novel therapeutic targets. Additionally, the role of autophagy, macropinocytosis, lysosomal transport, recycling, amino acid transport, lipid transport, and the role of reactive oxygen species has also been discussed. The role of various pro-inflammatory cytokines and immune cells in the pathogenesis of PDAC and the metabolites involved in the signaling pathways as therapeutic targets have been previously discussed. This review focuses on the therapeutic potential of metabolic mediators in PDAC along with stemness due to metabolic alterations and their therapeutic importance.
| S-EPMC7697422 | biostudies-literature
Project description:Gene expression analysis was performed on tumor-derived RNA from human PDAC xenograft to study the metabolic differences in the different PDAC subtypes
2021-12-31 | E-MTAB-10784 | biostudies-arrayexpress
Project description:Pancreatic ductal adenocarcinoma
| PRJNA432344 | ENA
Project description:BackgroundGlucose metabolic reprogramming (GMR) is a cardinal feature of carcinogenesis and metastasis. However, the underlying mechanisms have not been fully elucidated. The aim of this study was to profile the metabolic signature of primary tumor and circulating tumor cells from metastatic colorectal cancer (mCRC) patients using integrated omics analysis.MethodsPET-CT imaging, serum metabolomics, genomics and proteomics data of 325 high 18F-fluorinated deoxyglucose (FDGhigh) mCRC patients were analyzed. The para-tumor, primary tumor and liver metastatic tissues of mCRC patients were used for proteomics analysis.ResultsThe glucose uptake in tumor tissues as per the PET/CT images was correlated to serum levels of glutamic-pyruvic transaminase (ALT), total bilirubin (TBIL), creatinine (CRE). Proteomics analysis indicated that several differentially expressed proteins were enriched in both GMR and epithelial-mesenchymal transition (EMT)-related pathways. Using a tissue-optimized proteomic workflow, we identified novel proteomic markers (e.g. CCND1, EPCAM, RPS6), a novel PCK1-CDK6-INSR protein axis, and a potential role for FOLR (FR) in GMR/EMT of CRC cells. Finally, CEA/blood glucose (CSR) was defined as a new index, which can be used to jointly diagnose liver metastasis of colorectal cancer.ConclusionsGMR in CRC cells is closely associated with the EMT pathway, and this network is a promising source of potential therapeutic targets.
| S-EPMC10354426 | biostudies-literature