Aims Regorafenib, an FDA-approved drug for advanced primary liver cancer (PLC), could provide survival benefits for patients. However, markers for its therapeutic sensitivity are lacking. This study seeks to identify sensitive targets of regorafenib in PLC from the perspective of small molecular metabolites. Materials and methods Initiated with network pharmacology (NP) to map regorafenib's target landscape and metabolic regulatory network in liver cancer. Subsequently, regorafenib's impact on hepatoma cells was evaluated by flow cytometry, western blotting (WB) and cell viability assay. Advanced metabolomics and lipidomics were employed to elucidate regorafenib's metabolic reprogramming effects in liver cancer. Metabolic enzyme expression was assessed by WB, immunohistochemical and immunofluorescence assays. Ultimately, mendelian randomization (MR) analysis was utilized to investigate the potential causality of sphingolipid metabolism in hepatic cancer. Key findings Regorafenib was observed to inhibit hepatoma cell proliferation and cell cycle progression at G0/G1 phase, resulting in significant alterations in sphingolipid levels. It promoted the significant accumulation of 16:0 dihydroceramide (16:0 dhCer) by upregulating ceramide synthase 6 (CERS6) expression and inhibiting dihydroceramide desaturase 1 (DEGS1) activity. The MR analysis revealed that DEGS1 was a risk factor for the development and progression of liver cancer, while cumulative 16:0 dhCer was a protective factor. Significance Sphingolipids, particularly dhCer and regulatory enzymes, may be potential sensitive markers of regorafenib in the treatment of liver cancer, providing new insights for enhancing the treated efficacy of regorafenib in liver cancer.

Background. The molecular classification of HCC premised on metabolic genes might give assistance for diagnosis, therapy, prognosis prediction, immune infiltration, and oxidative stress in addition to supplementing the limitations of the clinical staging system. This would help to better represent the deeper features of HCC. Methods. TCGA datasets combined with GSE14520 and HCCDB18 datasets were used to determine the metabolic subtype (MC) using ConsensusClusterPlus. ssGSEA method was used to calculate the IFNγ score, the oxidative stress pathway scores, and the score distribution of 22 distinct immune cells, and their differential expressions were assessed with the use of CIBERSORT. To generate a subtype classification feature index, LDA was utilized. Screening of the metabolic gene coexpression modules was done with the help of WGCNA. Results. Three MCs (MC1, MC2, and MC3) were identified and showed different prognoses (MC2-poor and MC1-better). Although MC2 had a high immune microenvironment infiltration, T cell exhaustion markers were expressed at a high level in MC2 in contrast with MC1. Most oxidative stress-related pathways are inhibited in the MC2 subtype and activated in the MC1 subtype. The immunophenotyping of pan-cancer showed that the C1 and C2 subtypes with poor prognosis accounted for significantly higher proportions of MC2 and MC3 subtypes than MC1, while the better prognostic C3 subtype accounted for significantly lower proportions of MC2 than MC1. As per the findings of the TIDE analysis, MC1 had a greater likelihood of benefiting from immunotherapeutic regimens. MC2 was found to have a greater sensitivity to traditional chemotherapy drugs. Finally, 7 potential gene markers indicate HCC prognosis. Conclusion. The difference (variation) in tumor microenvironment and oxidative stress among metabolic subtypes of HCC was compared from multiple angles and levels. A complete and thorough clarification of the molecular pathological properties of HCC, the exploration of reliable markers for diagnosis, the improvement of the cancer staging system, and the guiding of individualized treatment of HCC all gain benefit greatly from molecular classification associated with metabolism.

Objective: To evaluate the therapeutic efficacy and safety of S1 monotherapy or combination with nab-paclitaxel for the treatment of elderly patients with metastatic or locally advanced pancreatic adenocarcinoma. Method: PubMed, Embase, Cochrane Central Library, China Biology Medicine, and China National Knowledge Infrastructure databases were searched without time limits according to the inclusion criteria. RevMan (Version 5.3) software was used for data extraction and meta-analysis. Objective response rate (ORR) and disease control rate (DCR) were used to evaluate therapeutic effects while side effects including leukopenia, thrombocytopenia, neurotoxicity, vomit, and alopecia were extracted for evaluation. There was no need for ethical review in this study because no ethical experiments were conducted and all data used were public data. All relevant data are within the paper and its Supporting Information files. Results: Four retrospective studies comprising 308 elderly patients with metastatic or locally advanced pancreatic adenocarcinoma were included in the analysis. One hundred fifty-one patients underwent S1 monotherapy and 157 received S1 combined nab-paclitaxel. Meta-analysis indicated that compared with S1 monotherapy, S1 combined with nab-paclitaxel had higher ORR (OR 2.25, 95% CI: 1.42-3.55; P = .0005) and DCR (OR 2.94, 95% CI: 1.55-5.58; P = .0009). The adverse reaction of leukopenia was higher in the combined therapy group (OR 1.85, 95% CI: 1.09-3.13, P = .02), but no significant difference was found in thrombocytopenia, neurotoxicity, vomiting, and alopecia between the 2 groups (P > .05). Conclusion: Nab-paclitaxel plus S1 was more efficient in terms of ORR and DCR than S1 monotherapy in elderly pancreatic ductal adenocarcinoma patients while the side effect was controllable with a higher probability of leukopenia. Thus, combined nab-paclitaxel and S1 could be safely used in elderly patients.

Objective:To construct a prognosis associated micro RNA(miRNA) prediction model based on bioinformatics analysis and evaluate its application value in pancreatic cancer patients.Methods:The retrospective cohort study was conducted. The clinicopathological data of 171 pancreatic cancer patients from the Cancer Genome Atlas (TCGA) (https: //cancergenome.nih.gov/) between establishment of database and September 2017 were collected. There were 93 males and 78 females, aged from 35 to 88 years, with a median age of 65 years. Of the 171 patients, 64 had complete clinicopathological data. Patients were allocated into training dataset consisting of 123 patients and validation dataset consisting of 48 patients using the random sampling method, with a ratio of 7∶3. The training dataset was used to construct a prediction model, and the validation dataset was used to evaluate performance of the prediction model. Nine pairs of miRNA sequencing data (GSE41372) of pancreatic cancer and adjacent tissues were downloaded from Gene Expression Omnibus database. The candidate miRNAs were selected from differentially expressed miRNAs in pancreatic cancer and adjacent tissues for LASSO-COX regression analysis based on the patients of training dataset. A prognosis associated miRNA prediction model was constructed upon survival associated miRNAs which were selected from candidate differentially expressed miRNAs. The performance of prognosis associated miRNA prediction model was validated in training dataset and validation dataset, the accuracy of model was evaluated using the area under curve (AUC) of the receiver operating characteristic curves and the efficiency was evaluated using the consistency index (C-index). Observation indicarors: (1) survival of patients; (2) screening results of differentially expressed miRNAs; (3) construction of prognosis associated miRNA model; (4) validation of prognosis associated miRNA model; (5) comparison of clinicopathological factors in pancreatic cancer patients; (6) analysis of factors for prognosis of pancreatic cancer patients; (7) comparison of prediction performance between prognosis associated miRNA model and the eighth edition TNM staging. Measurement data with normal distribution were represented as Mean± SD, comparison between groups was analyzed by the student- t test, and comparison between multiple groups was analyzed by the AVONA. Measurement data with skewed data were represented as M (range), and comparison between groups was analyzed using the Mann-Whitney U test. Count data were described as absolute numbers or percentages, and comparison between groups was conducted using the chi-square test. Ordinal data were analyzed using the rank sum test. Correlation analysis was conducted based on count data to mine the correlation between prognosis associated miRNA model and clinicopathological factors. COX univariate analysis and multivariate analysis were applied to evaluate correlation with the results described as hazard ratio ( HR) and 95% confidence interval ( CI). HR<1 indicated the factor as a protective factor, HR>1 indicated the factor as a risk factor, and HR equal to 1 indicated no influence on survival. The Kaplan-Meier method was used to draw survival curve and calculate survival rates, and the Log-rank test was used for survival analysis. Results:(1) Survival of patients: 123 patients in the training dataset were followed up for 31-2 141 days, with a median follow-up time of 449 days. The 3- and 5-year survival rates were 16.67% and 8.06%. Forty-eight patients in the validation dataset were followed up for 41-2 182 days, with a median follow-up time of 457 days. The 3- and 5-year survival rates were 15.63% and 9.68%. There was no significant difference in the 3- or 5-year survival rates between the two groups ( χ2=0.017, 0.068, P>0.05). (2) Screening results of differentially expressed miRNAs. Results of bioinformatics analysis showed that 102 candidate differentially expressed miRNAs were selected, of which 63 were up-regulated in tumor tissues while 39 were down-regulated. (3) Construction of prognosis associated miRNA model: of the 102 candidate differentially expressed miRNAs, 5 survival associated miRNAs were selected, including miR-21, miR-125a-5p, miR-744, miR-374b, miR-664. The differential expression patterns of pancreatic cancer to adjacent tissues were up-regulation, up-regulation, down-regulation, up-regulation, and down-regulation, respectively, with the fold change of 4.00, 3.43, 3.85, 2.62, and 2.35. A prognostic expression equation constructed based on 5 survival associated miRNAs = 0.454×miR-21 expression level-0.492×miR-125a-5p expression level-0.49×miR-744 expression level-0.419×miR-374b expression level-0.036×miR-664 expression level. (4) Validation of prognosis associated miRNA model: The C-index of prognosis associated miRNA model was 0.643 and 0.642 for the training dataset and validation dataset, respectively. (5) Comparison of clinicopathological factors in pancreatic cancer patients: results of COX analysis showed that the prognosis associated miRNA model was highly related with pathological T stage and location of pancreatic cancer ( Z=45.481, χ2=10.176, P<0.05). (6) Analysis of factors for prognosis of pancreatic cancer patients: results of univariate analysis showed that pathological N stage, radiotherapy, molecular targeted therapy, score of prognosis associated miRNA model were related factors for prognosis pf pancreatic cancer patients ( HR=2.471, 0.290, 0.172, 2.001, 95% CI: 1.012-6.032, 0.101-0.833, 0.082-0.364, 1.371-2.922, P<0.05). Results of multivariate analysis showed that molecular targeted therapy was an independent protective factor for prognosis of pancreatic cancer patients ( HR=0.261, 95% CI: 0.116-0.588, P<0.05) and score of prognosis associated miRNA model≥1.16 was an independent risk factor for prognosis of pancreatic cancer patients ( HR=1.608, 95% CI: 1.091-2.369, P<0.05). (7) Comparison of prediction performance between prognosis associated miRNA model and the eighth edition TNM staging: in the training dataset, there was a significant difference in the prediction probability for 3- and 5-year survival of pancreatic cancer patients between prognosis associated miRNA model and the eighth edition TNM staging ( Z=-1.671, -1.867, P<0.05). The AUC of the prognosis associated miRNA model and the eight edition TNM staging for 3- and 5-year survival prediction was 0.797, 0.935 and 0.737 , 0.703, with the 95% CI of 0.622-0.972, 0.828-1.042 and 0.571-0.904 , 0.456-0.951. The C-index was 0.643 and 0.534. In the validation dataset, there was a significant difference in the prediction probability for 3- and 5-year survival of pancreatic cancer patients between prognosis associated miRNA model and the eighth edition TNM staging ( Z=-1.729, -1.923, P<0.05). The AUC of the prognosis associated miRNA model and the eight edition TNM staging was 0.750, 0.873 and 0.721 , 0.703, with the 95% CI of 0.553-0.948, 0.720-1.025 and 0.553-0.889, 0.456-0.950, respectively. The C-index was 0.642 and 0.544. Conclusions:A prognosis associated miRNA prediction model can be constructed based on 5 survival associated miRNAs in pancreatic cancer patients, as a complementation to current TNM staging and other clinicopathological parameters, which provides individual and accurate prediction of survival for reference in the clinical treatment.

Additional file 7: Table S7. Identifying the methylation-based molecular subtypes. To identify methylation-based molecular subtype sites, the obtained 10,699 × 6 matrix was used as the input data of EpiDiff software.

Background DNA methylation is a common chemical modification of DNA in the carcinogenesis of hepatocellular carcinoma (HCC). Methods In this bioinformatics analysis, 348 liver cancer samples were collected from the Cancer Genome Atlas (TCGA) database to analyse specific DNA methylation sites that affect the prognosis of HCC patients. Results 10,699 CpG sites (CpGs) that were significantly related to the prognosis of patients were clustered into 7 subgroups, and the samples of each subgroup were significantly different in various clinical pathological data. In addition, by calculating the level of methylation sites in each subgroup, 119 methylation sites (corresponding to 105 genes) were selected as specific methylation sites within the subgroups. Moreover, genes in the corresponding promoter regions in which the above specific methylation sites were located were subjected to signalling pathway enrichment analysis, and it was discovered that these genes were enriched in the biological pathways that were reported to be closely correlated with HCC. Additionally, the transcription factor enrichment analysis revealed that these genes were mainly enriched in the transcription factor KROX. A naive Bayesian classification model was used to construct a prognostic model for HCC, and the training and test data sets were used for independent verification and testing. Conclusion This classification method can well reflect the heterogeneity of HCC samples and help to develop personalized treatment and accurately predict the prognosis of patients.

目的 探讨肿瘤调控因子长链非编码RNA(lncRNA)PVT1对肝癌(HCC)细胞增殖与迁移能力的影响及机制.方法 通过TCGA数据库对HCC中PVT1的表达水平进行分析.采用瞬时转染技术构建PVT1敲低细胞系.通过CCK-8实验及划痕实验检测PVT1对HCC细胞增殖与迁移能力的影响.采用生物信息预测和荧光素酶实验验证PVT1与miR-17-5p的靶向结合情况.利用共转染实验验证PVT1能够通过miR-17-5p实现对HCC细胞增殖与迁移的调控.结果 PVT1在HCC组织和细胞中高表达.敲低PVT1后HCC细胞增殖与迁移能力显著降低.荧光素酶实验证实PVT1能够与miR-17-5p靶向结合,并且miR-17-5p介导了PVT1对HCC细胞增殖和迁移能力的调控作用.结论 ln-cRNA PVT1能够通过靶向抑制miR-17-5p从而促进HCC细胞的增殖和迁移.

The present study was designed to identify the endogenous RNA regulatory networks involved in hepatocellular carcinoma (HCC) by bioinformatic analysis. Both miRNA interaction network‑based correlation analysis and expression‑based Spearman correlation coefficients were utilized to identify potential mRNA‑lncRNA interactions. Then, a competitive endogenous (ce)RNA network was constructed from these interactions, and network topology and Gene Ontology enrichment analyses were conducted to mine potential functions of ceRNAs. In HCC samples, a ceRNA network was constructed. It was composed of 35,657 edges connecting 113 lncRNAs and 6,136 mRNAs which were differentially expressed in HCC and normal liver tissues. Meanwhile, a number of significantly positively correlated mRNA and lncRNA pairs in this ceRNA network were found to be consistently positively correlated in another independent dataset. To be noted, further analyses on the potential roles of ceRNAs demonstrated than various lncRNAs such as LINC00657, TUG1 and SNHG1 may play key roles in HCC by regulating protein phosphorylation or cell cycle pathways or influencing miRNAs. From the perspective that lncRNAs can function as ceRNAs, this study revealed that the interaction between lncRNAs, miRNAs and mRNAs may provide new insight for the diagnosis and treatment in the tumorigenesis of hepatocellular carcinoma.

Objective To assess systematically endoscopic ultrasonography's (EUS)advantages on preoperative localization of gastroentero-pancreatic neuroendocrine tumors (GEP-NETs).Methods We collected the medical literatures published domestically and abroad from Medline,PubMed,Embase,Science Citation Index,CNKI,Wanfang Database,and CQVIP.The eligible literature of studies was screened out according to the inclusion and exclusion criteria,and the reported data were aggregated statistically using StataSE 12.0 software.The inclusion criteria concern the clinical results of patients which have done imageological examinations consisting of EUS at least and confirmed by postoperative biopsy or ultrasound-guided fine needle aspiration (EUS-FNA).Type of literatures included case-control study or retrospective study.Results A total of 10 academic literatures were screened out,including 543 patients and 578 lesions.The overall detected rate of EUS was obviously higher than CT (OR =4.61,95％ CI:2.98,7.13,P =0.000).The detected rate of EUS on tumors smaller than 2 cm was obviously higher than CT (OR=12.66,95％ CI:6.06,26.44,P=0.000).The detected rate of EUS on functional neuroendocrine tumors (FTs) was obviously higher than CT (OR =5.74,95％ CI:2.97,11.09,P =0.000).Conclusions EUS has more advantages than CT and other traditional imageological examinations on GEP-NETs' preoperative localization especially when tumors are smaller than 2 cm or being FTs.

Objective To analyze endoscope and clinical features of ischemic colitis (IC), to improve its diagnosis and treatment, and to lower the rates of missed and erroneous diagnosis .Methods 68 patients with ischemic colitis were retro-spectively reviewed for their clinical features , endoscopic findings , clinicopathological characteristics and treatments .Re-sults The majority of patients were over the age of 60 years.Most of the cases were associated with some underlying diseases such as hypertension , diabetes, coronary heart disease and atherosclerotic disease .Clinical symptoms associated with IC included sudden onset of left lower quadrant abdominal pain , hematochezia and diarrhea .The lesions were mainly located at left side colon with segmental form .In our studies , there were 59 cases with transient or reversible colitis , 9 cases with stricture colitis and no case with necrotic colitis .Conclusion Ischemic colitis mainly occurs in aged patients .Most of the cases were with basic diseases .Early colonoscopy with biopsy plays an important role in diagnosis of IC .Early diagnosis and proper therapy are vital for its prognosis .