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Introduction: Occult pancreaticobiliary reflux (OPBR) causes chronic inflammation of the gallbladder and associated mucosal malignant changes, but its pathogenic mechanism remains unclear. Objective The aim of this study was to investigate the pathogenic mechanism in OPBR through untargeted metabolomic analysis of bile collected during surgery. Methods Patients undergoing surgery for gallstones between December 2020 and May 2021 were eligible for inclusion. There were 30 OPBR patients and 338 non-OPBR patients, and 30 non-OPBR patients were randomly selected as normal controls. Metabolomics analysis was performed based on bile samples from patients in the OPBR group. Results The principal component analysis by liquid chromatography-mass spectrometry revealed that 30 metabolites were significantly different between patients with OPBR compared to controls. Among these characteristic metabolites, palmitic acid and arachidonic acid are known to be involved in gallstone formation, methionine and lysophosphatidylcholine are known to be associated with carcinogenesis. Conclusions The present study suggested that an LC/MS-based metabolomic studies provide an efficient and promising method for identifying potential biomarkers of OPBR. The identified characteristic metabolites in the OPBR group were significantly different to the control group.

Background: Pancreatic cancer is one of the most malignant tumors of the digestive system and its treatment has rarely progressed for the last two decades. Studies on m6A regulators for the past few years have seemingly provided a novel approach for malignant tumor therapy. m6A-related factors may be potential biomarkers and therapeutic targets. This research is focused on the gene characteristics and clinical values of m6A regulators in predicting prognosis in pancreatic cancer. Methods: In our study, we obtained gene expression profiles with copy number variation (CNV) data and clinical characteristic data of 186 patients with pancreatic cancer from The Cancer Genome Atlas portal (TCGA). Then, we determined the alteration of m6a regulators and their correlation with clinicopathological features using the log-rank tests, Cox regression model, and chi-square test. Results: The results suggested that pancreatic cancer patients with ALKBH5 CNV were associated with worse overall survival and disease-free survival than those with diploid genes. Additionally, upregulation of the writer gene ALKBH5 had a positive correlation with the activation of AKT pathways. Conclusion: Our study not only demonstrated genetic characteristic changes of m6A-related genes in pancreatic cancer and found a strong relationship between the changes of ALKBH5 and poor prognosis but also provided a novel therapeutic target for pancreatic cancer therapy.

Background: Circadian clock genes have been reported to exhibit a regulatory effect on the carcinogenesis and progression of numerous cancers. Nevertheless, the specific relationship between hepatocellular carcinoma (HCC) and circadian rhythm associated genes still remain to be clarified. Therefore, we evaluate the prognosis function of circadian clock genes in HCC with the online datasets of The Cancer Genome Atlas (TCGA) and the international cancer genome consortium (ICGC). Methods: In our research, the RNA-seq of the selected core circadian genes in HCC patients and their relevant clinical data were acquired from the online TCGA database and the ICGC database. R software and cBioPortal website were performed. Results: As consequence, among the 22 typical circadian clock genes, 16 genes were statistically expressed between HCC and adjacent normal tissues. Accordingly, 11 clock genes with regression coefficients were used to constitute a new risk score formula, which was related to the prognosis in HCC. Moreover, the new nomogram, which consisting risk score and several clinical traits , could be applied for the purpose of accurate prediction of the OS time for the patients. Finally, we identified a novel nomogram related with OS in HCC patients with a comprehensive analysis of circadian clock genes and other clinical characteristics profiles. It was also the first time we systematically demonstrated the relationship between clock genes and the HCC prognosis, which would contribute to the treatment of HCC. Conclusions: The current study demonstrated the potential of circadian clock genes as clinically associated biomarkers for prognosis prediction in HCC, which may make a significant contribution to the further investigations of HCC progression.

Background: Utilizing genomic data to predict cancer prognosis was insufficient. Proteomics can improve our understanding of the etiology and progression of cancer and improve the assessment of cancer prognosis. Based on CPTAC (Clinical Proteomic Tumor Analysis Consortium) which has generated extensive proteomics data of the vast majority of tumors, we can perform a proteomic pan-carcinoma analysis.Methods: The proteomics data and clinical features of cancer patients were collected from CPTAC. We screened 69 differentially expressed proteins with R software. GO and KEGG analysis were performed to clarify the function of these proteins. The DEPs-based prognostic model was identified by least absolute shrinkage and selection operator (LASSO)-Cox regression model. The time-dependent receiver operating characteristics analysis was used to evaluate the ability of the prognostic model to predict overall survival.Results: A total of 69 differentially expressed proteins were screened in five different types of cancers: hepatocellular carcinoma (HCC), lung adenocarcinoma (LUAD), children's brain tumor tissue consortium (CBTTC), clear cell renal cell carcinoma (CCRC) and uterine corpus endometrial carcinoma (UCEC). Furthermore, the differentially expressed proteins were related to cell metabolism, cell proliferation and extracellular matrix. Then 24 DEPs-based classifiers for predicting OS was developed by LASSO-Cox regression model in training cohort, which was validated in validation cohort. Conclusions: In the present study, we identified DEPs-based survival-predictor model to predict most cancers. We are the first group to utilize proteomics to construct a pan-cancer prognosis model, which could accurately and effectively predict the survival rate of most cancers.