Search

Purpose: The aim of this study is to compare the long-term outcomes of three-port laparoscopic right hemicolectomy (TPLRC) and five-port laparoscopic right hemicolectomy (FPLRC) with retrospective analysis. Methods: A total of 182 patients who accepted laparoscopic right hemicolectomy with either three ports (86 patients) or five ports (96 patients) from January 2012 to June 2017 were non-randomly selected and analyzed retrospectively. Results: More lymph nodes were harvested in the TPLRC group than in the FPLRC group [17.5 (7), 14 (8) ml, p < 0.001]. There was less blood loss in the TPLRC group [50 (80) vs. 100 (125) ml, p = 0.015]. There were no significant differences in the other short-term or oncological outcomes between the two groups. The overall survival and disease-free survival were equivalent. Conclusions: TPLRC is recommendable as it guarantees short- and long-term equivalent outcomes compared with FPLRC. [ABSTRACT FROM AUTHOR]

Background: In recent years, the differences between left-sided colon cancer (LCC) and right-sided colon cancer (RCC) have received increasing attention due to the clinicopathological variation between them. However, some of these differences have remained unclear and conflicting results have been reported.Methods: From The Cancer Genome Atlas (TCGA), we obtained RNA sequencing data and gene mutation data on 323 and 283 colon cancer patients, respectively. Differential analysis was firstly done on gene expression data and mutation data between LCC and RCC, separately. Machine learning (ML) methods were then used to select key genes or mutations as features to construct models to classify LCC and RCC patients. Finally, we conducted correlation analysis to identify the correlations between differentially expressed genes (DEGs) and mutations using logistic regression (LR) models.Results: We found distinct gene mutation and expression patterns between LCC and RCC patients and further selected the 30 most important mutations and 17 most important gene expression features using ML methods. The classification models created using these features classified LCC and RCC patients with high accuracy (areas under the curve (AUC) of 0.8 and 0.96 for mutation and gene expression data, respectively). The expression of PRAC1 and BRAF V600E mutation (rs113488022) were the most important feature for each model. Correlations of mutations and gene expression data were also identified using LR models. Among them, rs113488022 was found to have significance relevance to the expression of four genes, and thus should be focused on in further study.Conclusions: On the basis of ML methods, we found some key molecular differences between LCC and RCC, which could differentiate these two groups of patients with high accuracy. These differences might be key factors behind the variation in clinical features between LCC and RCC and thus help to improve treatment, such as determining the appropriate therapy for patients. [ABSTRACT FROM AUTHOR]