Background Early detection of pancreatic ductal adenocarcinoma (PDAC) may improve the prognosis of patients. This study was to identify metabolic features of PDAC and to discover early detection biomarkers for PDAC by tissue and serum metabolomics analysis. Methods We conducted nontargeted metabolomics analysis in tissue samples of 51 PDAC tumors, 40 noncancerous pancreatic tissues (NT), and 14 benign pancreatic neoplasms (BP) as well as serum samples from 80 patients with PDAC, 36 with BP, and 48 healthy controls (Ctr). The candidate metabolites identified from the initial analysis were further quantified using targeted analysis in serum samples of an independent cohort of 22 early stage PDAC, 27 BP, and 27 Ctr subjects. Unconditional binary logistic regression analysis was used to construct the optimal model for PDAC diagnosis. Results Upregulated levels of fatty acids and lipids and downregulated amino acids were observed in tissue and serum samples of PDAC patients. Proline, creatine, and palmitic acid were identified as a panel of potential biomarkers to distinguish PDAC from BP and Ctr (odds ratio = 2.17, [95% confidence interval 1.34–3.53]). The three markers showed area under the receiver‐operating characteristic curves (AUCs) of 0.854 and 0.865, respectively, for the comparison of PDAC versus Ctr and PDAC versus BP. The AUCs were 0.830 and 0.852 in the validation set and were improved to 0.949 and 0.909 when serum carbohydrate antigen 19‐9 (CA19‐9) was added to the model. Conclusion The novel metabolite biomarker panel identified in this study exhibited promising performance in distinguishing PDAC from BP or Ctr, especially in combination with CA19‐9.

IntroductionXiaoai Jiedu recipe (XJR), a classical prescription of traditional Chinese medicine (TCM), has been clinically proven to be effective in ameliorating colorectal cancer (CRC). However, its exact mechanism of action is still elusive, limiting its clinical application and promotion to a certain extent. This study aims to evaluate the effect of XJR on CRC and further illustrate mechanism underlying its action.MethodsWe investigated the anti-tumor efficacy of XJR in vitro and vivo experiments. An integrated 16S rRNA gene sequencing and UPLC-MS based metabolomics approach were performed to explore possible mechanism of XJR anti-CRC on the gut microbiota and serum metabolic profiles. The correlation between altered gut microbiota and disturbed serum metabolites was investigated using Pearson’s correlation analysis.ResultsXJR effectively displayed anti-CRC effect both in vitro and in vivo. The abundance of aggressive bacteria such as Bacteroidetes, Bacteroides, and Prevotellaceae decreased, while the levels of beneficial bacteria increased (Firmicutes, Roseburia, and Actinobacteria). Metabolomics analysis identified 12 potential metabolic pathways and 50 serum metabolites with different abundances possibly affected by XJR. Correlation analysis showed that the relative abundance of aggressive bacteria was positively correlated with the levels of Arachidonic acid, Adrenic acid, 15(S)−HpETE, DL−Arginine, and Lysopc 18:2, which was different from the beneficial bacteria.DiscussionThe regulation of gut microbiota and related metabolites may be potential breakthrough point to elucidate the mechanism of XJR in the treatment of the CRC. The strategy employed would provide theoretical basis for clinical application of TCM.

Background: Pancreatic ductal adenocarcinoma (PDAC) develops rapidly and has a poor prognosis. It has been demonstrated that pancreatic ductal adenocarcinoma and chronic pancreatitis (CP) have a close connection. However, the underlying mechanisms for chronic pancreatitis transforming into pancreatic ductal adenocarcinoma are still unclear. The purpose of this study was to identify real hub genes in the development of chronic pancreatitis and pancreatic ductal adenocarcinoma.Methods: RNA-seq data of chronic pancreatitis and pancreatic ductal adenocarcinoma were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between chronic pancreatitis and pancreatic ductal adenocarcinoma. GEO2R and a Venn diagram were used to identify differentially expressed genes. Then visualized networks were constructed with ClueGO, and modules of PPI network were calculated by MCODE plugin. Further validation of the results was carried out in two additional cohorts. Analyses of CEL-coexpressed genes and regulators including miRNAs and transcription factors were performed by using the corresponding online web tool. Finally, the influence of CEL in the tumor immune microenvironment (TIME) was assessed by immune contextual analysis.Results: With the help of WGCNA and GEO2R, four co-expression modules and six hub genes were identified, respectively. ClueGO enrichment analysis and MCODE cluster analysis revealed that the dysfunctional transport of nutrients and trace elements might contribute to chronic pancreatitis and pancreatic ductal adenocarcinoma development. The real hub gene CEL was identified with a markedly low expression in pancreatic ductal adenocarcinoma in external validation sets. According to the miRNA-gene network construction, hsa-miR-198 may be the key miRNA. A strong correlation exists between CEL and TIME after an evaluation of the influence of CEL in TIME.Conclusion: Our study revealed the dysfunctional transport of nutrients and trace elements may be common pathogenesis of pancreatic ductal adenocarcinoma and chronic pancreatitis. Examination on these common pathways and real hub genes may shed light on the underlying mechanism.

Editorial on the Research TopicNovel circulating biomarkers and radiomics in pancreatic cancer Pancreatic cancer (PC) is a highly lethal malignancy with a 5-year survival rate of < 9%. Approximately 80% of patients with advanced or metastatic PC suffer from an extremely poor prognosis, and the diagnosis of PC is generally at an advanced stage, which also presents a major challenge for clinical treatment, especially when the tumor has metastasized to other organs and proliferated to the extent that adequate surgical resection cannot be performed. Therefore, accurate PC diagnosis and treatment strategies are urgently needed in the early developmental stage. Due to its limited specificity, carbohydrate antigen 19-9 (CA19-9) is generally not recommended for early screening of PC. As the discovery of new serum biomarkers has recently received more attention, its combined detection of PC with CA19-9 has also been explored. Liquid biopsies, including circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), microRNAs, and exosomes in blood, as well as biomarkers in urine and saliva, have been increasingly used in PC diagnosis. In addition, new technologies including radiomics, and the generation of mineable high-throughput data from medical images, have also provided a deeper understanding of pancreatic tissue heterogeneity, which holds great promise for the early diagnosis of PC.This edition of Frontiers in Oncology aims to highlight the impact of novel circulating biomarkers and radiomics in the early detection of PC. Advances in new therapeutic strategies for PC have increased the opportunities for tumor downstaging. After peer review, a total of 4 wonderful works were published in this Research Topic, all of which were original studies. The studies were conducted in Germany, the Netherlands, Italy, and China respectively with a total of 40 contributing authors.Different from traditional practice where medical images are solely treated for visual interpretation, Radiomics appears to offer a nearly limitless imaging biomarkers which helps to make clinically effective and cost-effective contributions to cancer care and serve as a decision-making tool for personalized medicine. Van Der Kroft et al. found that it is feasible to implement a data-driven radiomics approach to body composition imaging, and they were capable of extracting radiomics features which held improved predictive value Frontiers in Oncology frontiersin.org 01