Background. A risk assessment model for prognostic prediction of colon adenocarcinoma (COAD) was established based on weighted gene co-expression network analysis (WGCNA). Methods. From the Cancer Genome Atlas (TCGA) database, RNA-seq data and clinical data of COAD patients were retrieved. After screening of differentially expressed genes (DEGs), WGCNA was performed to identify gene modules and screen those associated with COAD progression. Then, via protein-protein interaction (PPI) network construction of module genes, hub genes were obtained, which were then subjected to the least absolute shrinkage and selection operator (LASSO) and Cox regression to build a hub gene-based prognostic scoring model. The receiver operating characteristic curve (ROC curve) was plotted for the optimal cutoff (OCO) of the risk score, based on which, patients were assigned to high or low-risk groups. Areas under the ROC curve (AUCs) were calculated, and model performance was visualized using Kaplan–Meier (KM) survival curves and verified in the external dataset GSE29621. Finally, the model’s independent prognostic value was evaluated by univariate and multivariate Cox regression analyses, and a nomogram was built. Results. Totally 2840 DEGs were screened from COAD dataset of TCGA, including 1401 upregulated ones and 1439 downregulated ones, which were divided into 10 modules by WGCNA. The eigenvalue of the black module was found to have a high correlation with COAD progression. PPI interaction networks were constructed for genes in the black module, and 34 hub genes were obtained by using the MCODE plug-in. A LASSO-Cox regression approach was utilized to analyze the hub genes, and a prognostic risk score model based on the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) was constructed. KM analysis identified shorter overall lower survival in the high-risk group. The model was verified to have favorable predictive ability through training set and validation set. The nomogram, composed of tumor node metastasis (TNM) staging and risk score, was of good predictability. Conclusions. The COAD prognostic risk model constructed upon the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) can effectively predict the survival status of COAD patients.

Background Massive, delayed bleeding (DB) is the most common major complication of Rubber Band Ligation (RBL) for internal hemorrhoids caused by premature band slippage. In this study we modified conventional RBL to prevent early rubber band slippage and evaluated its clinical efficacy and safety. Methods Study participants were consecutive patients with grade II or III internal hemorrhoids treated with RBL at Ningbo Medical Center of Lihuili Hospital from January 2019 to December 2020. Postoperative minor complications such as pain, swelling, anal edema, prolapse recurrence and major complications like DB were retrospectively reviewed. Results A total of 274 patients were enrolled, including 149 patients treated with modified RBL and 125 treated with conventional RBL. There was no statistically significant difference between the two groups at baseline. Five cases of postoperative DB have been observed in the conventional RBL group, compared to none in the modified ones, with a significant difference (P < 0.05). Within three months after surgery, 8 cases in the modified RBL group experienced a recurrence rate of 5.4%, whereas 17 patients in the conventional RBL group experienced a recurrence rate of 13.6%. The difference was statistically significant (P < 0.05). The VAS score, edema, and incidence of sensation of prolapse between the two groups were not significantly different at 3 and 7 days after surgery (P < 0.05). There were also no significant differences in HDSS and SHS scores between the two groups after surgery (P > 0.05). Conclusion Modified RBL may be associated with a lower rate of complications, especially with lower DB rate in comparison with standard RBL. Further studies in larger samples and different design are necessary to confirm these results.