Development of Prognostic Features of Hepatocellular Carcinoma Based on Metabolic Gene Classification and Immune and Oxidative Stress Characteristic Analysis.

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.

Safety and efficacy of S1 monotherapy or combined with nab-paclitaxel in advanced elderly pancreatic cancer patients: A meta-analysis.

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.

HDNA methylation data-based molecular subtype classification related to the prognosis of patients with hepatocellular carcinoma.

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.

Identification of a long non­coding RNA­mediated competitive endogenous RNA network in hepatocellular carcinoma.

The present study was designed to identify the endogenous RNA regulatory networks involved in hepatocellular carcinoma (HCC) by bioinformatic analysis. Both miRNA interaction network­based correlation analysis and expression­based Spearman correlation coefficients were utilized to identify potential mRNA­lncRNA 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.