Locally advanced rectal cancer (LARC) is a heterogeneous disease with little information about KRAS status and image features. The purpose of this study was to analyze the association between T2 magnetic resonance imaging (MRI) radiomics features and KRAS status in LARC patients.

Recently, increasing evidence has suggested that Glutamine-fructose-6-phosphate transaminase 2 (GFPT2) is related to carcinogenesis. However, the potential roles of GFPT2 in colon cancer still need to be fully investigated.

Colorectal cancer (CRC) is the third most common cancer with a high global incidence and mortality. Mutated genes or dysregulated pathways responsible for CRC progression have been identified and employed as biomarkers for diagnosis and prognosis. In this study, a ubiquitination regulator, MARCH9, was shown to accelerate CRC progression both in vitro and in vivo. CRC samples from The Cancer Genome Atlas (TCGA) showed significantly upregulated MARCH9 expression by individual cancer stage, histological subtype, and nodal metastasis status. Knockdown of MARCH9 inhibited, while MARCH9 overexpression promoted, CRC cell proliferation and migration. Knockdown of MARCH9 also induced CRC cell apoptosis and caused cell cycle arrest. Further investigation showed that MARCH9 promoted CRC progression by downregulating the expression of a deubiquitinase cylindromatosis (CYLD) gene and activating p65, a member of the nuclear factor-κB (NF-κB) protein family. Finally, in vivo xenograft studies confirmed that MARCH9 knockdown suppressed tumor growth in nude mice. Thus, this study demonstrated that MARCH9 may be a novel and effective therapeutic target for CRC therapy.

Background and Aims: The best treatment modalities for elderly patients with stage I-II HCC (hepatocellular carcinoma) remain controversial in an era of a shortage of liver donors. Methods: From the SEER database (Surveillance, Epidemiology, and End Results program), 2,371 elderly patients were sampled as Cohort 1. OS (Overall Survival) and CSS (Cancer-Specific Survival) were compared between the Non-surgery and Surgery groups. A stratification analysis in a CSS Cox model was also conducted among sub-groups, and propensity score matching was performed to generate Cohort 2 (746 pairs), reducing the influences of confounders. Results: For Cohort 1, the median follow-up times of the Non-surgery and Surgery groups were 11 months (95% CI, confidence interval: 9.74-12.26) vs. 49 months (44.80-53.21) in OS, and 14 months (12.33-15.67) vs. 74 months (64.74-83.26) in CSS, respectively. In the stratification analysis, for the elderly patients (age >= 70 years), Larger Resection was associated with a higher HR (hazard ratio) than Segmental Resection: 0.30 (95% CI, confidence interval: 0.22-0.41) vs. 0.29 (0.21-0.38) in 70-74 year-olds; 0.26 (0.18-0.38) vs. 0.23 (0.16-0.32) in 75-79 year-olds; 0.32 (0.21-0.49) vs. 0.21 (0.13-0.32) in those 80+ years old. For Cohort 2, a similar result could be seen in the CSS Cox forest plot. The HRs of Larger Resection and Segmental Resection were 0.27 (0.21-0.33) and 0.25 (0.20-0.31), respectively. Conclusions: It is cautiously recommended that, when liver transplantation is not available, segmental or wedge liver resection is the better treatment choice for elderly patients with stage I-II HCC (AJCC edition 6), especially those over 70 years old, compared with other surgeries, based on the SEER data.

The role of preoperative embolization (PE) in reducing intraoperative blood loss (IBL) during surgical treatment of spinal metastases remains controversial.

Purpose: The aim of this study was to investigate the diagnostic value of machine-learning models with radiomic features and clinical features in preoperative differentiation of common lesions located in the anterior skull base. Methods: A total of 235 patients diagnosed with pituitary adenoma, meningioma, craniopharyngioma, or Rathke cleft cyst were enrolled in the current study. The discrimination was divided into three groups: pituitary adenoma vs. craniopharyngioma, meningioma vs. craniopharyngioma, and pituitary adenoma vs. Rathke cleft cyst. In each group, five selection methods were adopted to select suitable features for the classifier, and nine machine-learning classifiers were employed to build discriminative models. The diagnostic performance of each combination was evaluated with area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity calculated for both the training group and the testing group. Results: In each group, several classifiers combined with suitable selection methods represented feasible diagnostic performance with AUC of more than 0.80. Moreover, the combination of least absolute shrinkage and selection operator as the feature-selection method and linear discriminant analysis as the classification algorithm represented the best comprehensive discriminative ability. Conclusion: Radiomics-based machine learning could potentially serve as a novel method to assist in discriminating common lesions in the anterior skull base prior to operation.