Retroperitoneal Laparoscopic Debridement and Drainage for Pancreatic Abscess.

In patients with severe necrotizing pancreatitis, pancreatic necrosis and secondary infection of surrounding tissues can quickly spread to the whole retroperitoneal space. Treatment of pancreatic abscess complicating necrotizing pancreatitis is difficult and has a high mortality rate. The well-accepted treatment strategy is early debridement of necrotic tissues, drainage, and postoperative continuous retroperitoneal lavage. However, traditional open surgery has several disadvantages, such as severe trauma, interference with abdominal organs, a high rate of postoperative infection and adhesion, and hardness with repeated debridement. The retroperitoneal laparoscopic approach has the advantages of minimal invasion, a better drainage route, convenient repeated debridement, and avoidance of the spread of retroperitoneal infection to the abdominal cavity. In addition, retroperitoneal drainage leads to fewer drainage tube problems, including miscounting, displacement, or siphon. The debridement and drainage of pancreatic abscess tissue via the retroperitoneal laparoscopic approach plays an increasingly irreplaceable role in improving patient prognosis and saving healthcare resources and costs. The main procedures described here include laying the patient on the right side, raising the lumbar bridge and then arranging the trocar; establishing the pneumoperitoneum and cleaning the pararenal fat tissues; opening the lateral pyramidal fascia and the perirenal fascia outside the peritoneal reflections; opening the anterior renal fascia and entering the anterior pararenal space from the rear; clearing the necrotic tissue and accumulating fluid; and placing drainage tubes and performing postoperative continuous retroperitoneal lavage.

Identification of METTL3 as an Adverse Prognostic Biomarker in Hepatocellular Carcinoma.

INTRODUCTION: N6-methyladenosine (m6A), the most prominent mRNA modification, plays a critical role in many physiological and pathological processes. However, the roles of m6A RNA modification in hepatocellular carcinoma (HCC) remain largely unknown. MATERIALS AND METHODS: We investigated the mRNA expression and clinical significance of m6A-related genes using data from The Cancer Genome Atlas (TCGA) liver hepatocellular carcinoma cohort. Mutation, copy number variation (CNV), methylation, differential expression, and gene ontology analyses, gene set enrichment analysis and the construction of a competing endogenous RNA (ceRNA) regulatory network were performed to investigate the underlying mechanisms of the aberrant expression of m6A-related genes. RESULTS: m6A-related genes were frequently dysregulated in cancers but with a cancer-specific pattern. METTL3, YTHDF2, and ZC3H13 were found to be independent prognostic factors of overall survival (OS); however, only METTL3 was found to be an independent prognostic factor of recurrence-free survival (RFS). Joint effects analysis showed the predictive capacity of combining METTL3, YTHDF2, and ZC3H13 for HCC OS. Then the potential mechanisms of METTL3 were further explored due to its prognostic role in both OS and RFS. CNV and DNA methylation, but not somatic mutations, might contribute to the abnormal upregulation of METTL3 in HCC. Significantly altered genes, microRNAs, and lncRNAs were identified, and a ceRNA regulatory network was constructed to explain the upregulation of METTL3 in HCC. CONCLUSIONS: Our study identified several m6A-related genes, especially METTL3, that could be potential prognostic biomarkers in HCC.

Key genes associated with diabetes mellitus and hepatocellular carcinoma.

Accumulating evidence indicates a strong correlation between type 2 diabetes mellitus (T2DM) and hepatocellular carcinoma (HCC), but the underlying pathophysiology is still elusive. We aimed to identify unrecognized but important genes and pathways related to T2DM and HCC by bioinformatic analysis. The GSE64998 and GSE15653 datasets (for T2DM), the GSE121248 dataset and the Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset (for HCC) were downloaded. Differential expression analysis, functional and pathway enrichment analysis, protein-protein interaction (PPI) network construction, survival analysis, transcription factor (TF) prediction, and correlation of gene expression with methylation and tumour-infiltrating immune cells were conducted. Nine genes, namely, CDNF, CRELD2, DNAJB11, DTL, GINS2, MANF, PDIA4, PDIA6, and VCP, were recognized as hub genes. Enrichment analysis revealed several enriched terms and pathways. Transcription factors such as Kruppel-like factor 6, abnormal methylation and immune dysregulation might help explain the dysregulation of hub genes. Our study identified nine hub genes that might play a critical role in both T2DM and HCC. However, more studies are warranted to clarify the mechanisms of these genes.

Identification of a six-gene signature predicting overall survival for hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) remains a major challenge for public health worldwide. Considering the great heterogeneity of HCC, more accurate prognostic models are urgently needed. To identify a robust prognostic gene signature, we conduct this study. MATERIALS AND METHODS: Level 3 mRNA expression profiles and clinicopathological data were obtained in The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC). GSE14520 dataset from the gene expression omnibus (GEO) database was downloaded to further validate the results in TCGA. Differentially expressed mRNAs between HCC and normal tissue were investigated. Univariate Cox regression analysis and lasso Cox regression model were performed to identify and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve, multivariate Cox regression analysis, nomogram, and decision curve analysis (DCA) were used to assess the prognostic capacity of the six-gene signature. The prognostic value of the gene signature was further validated in independent GSE14520 cohort. Gene Set Enrichment Analyses (GSEA) was performed to further understand the underlying molecular mechanisms. The performance of the prognostic signature in differentiating between normal liver tissues and HCC were also investigated. RESULTS: A novel six-gene signature (including CSE1L, CSTB, MTHFR, DAGLA, MMP10, and GYS2) was established for HCC prognosis prediction. The ROC curve showed good performance in survival prediction in both the TCGA HCC cohort and the GSE14520 validation cohort. The six-gene signature could stratify patients into a high- and low-risk group which had significantly different survival. Cox regression analysis showed that the six-gene signature could independently predict OS. Nomogram including the six-gene signature was established and shown some clinical net benefit. Furthermore, GSEA revealed several significantly enriched oncological signatures and various metabolic process, which might help explain the underlying molecular mechanisms. Besides, the prognostic signature showed a strong ability for differentiating HCC from normal tissues. CONCLUSIONS: Our study established a novel six-gene signature and nomogram to predict overall survival of HCC, which may help in clinical decision making for individual treatment.