Thyroid cancer (TC) is the most common endocrine malignancy, accounting for approximately 90% of all malignancies of the endocrine system. Despite the fact that patients with TC tend to have good prognoses, the high incidence rate and lymph node metastases remain unresolved issues. Autophagy is an indispensable process that maintains intracellular homeostasis; however, the role of autophagy in several steps of the initiation and progression of TC has not yet been elucidated. In this study, we first identified several autophagy-related genes (ARGs) that were provoked in the onset of TC. Subsequently, a bioinformatics analysis hinted that these genes were markedly disturbed in several proliferative signaling pathways. Moreover, we demonstrated that the differentially expressed ARGs were closely related to several aggressive clinical manifestations, including an advanced tumor stage and lymph node metastasis. Our study further selected prognostic ARGs and developed a prognostic signature based on three key genes (ATG9B, BID and B1DNAJB1), which displayed a moderate ability to predict the prognosis of TC. On the whole, the findings of this study demonstrate that ARGs disrupt proliferation-related pathways and consequently lead to aggressive clinical manifestations. These findings provide insight into the potential molecular mechanisms of action of ARGs and their clinical significance, and also provide classification information of potential therapeutic significance.

As one of the most lethal malignancies worldwide, hepatocellular carcinoma (HCC) has a high mortality rate, which is mainly due to the complex and multi-step aberrations in gene expression associated with it. Small nucleolar RNAs (snoRNAs), non-coding RNAs that are 60-300 nucleotides in length, have been proposed to be closely associated with numerous human diseases, including HCC. However, the current knowledge regarding their clinical significance and mechanistic roles in HCC is limited. The present study comprehensively analyzed the snoRNA expression profiles in HCC and identified several ones that were dysregulated. The potential regulatory mechanisms of these snoRNAs were assessed via gene functional enrichment analyses. Univariate and multivariate Cox regression analyses were performed to identify snoRNAs that are independently associated with the risk of mortality. Subsequently, a prognostic index (PI) for survival prediction was established, which may serve as a prognostic biomarker for patients with HCC (hazard ratio, 3.023; 95% confidence interval: 1.785-5.119; P<0.001). In addition, a series of bioinformatics analyses were performed to identify potential differences in the perturbation of pathways between high-and low-risk groups. The PI developed in the present study was determined to have a moderate predictive value regarding the clinical outcome for HCC patients.

Objective To investigate whether radiomics based on ultrasound images can predict lymphovascular invasion (LVI) of rectal cancer (RC) before surgery. Methods A total of 203 patients with RC were enrolled retrospectively, and they were divided into a training set (143 patients) and a validation set (60 patients). We extracted the radiomic features from the largest gray ultrasound image of the RC lesion. The intraclass correlation coefficient (ICC) was applied to test the repeatability of the radiomic features. The least absolute shrinkage and selection operator (LASSO) was used to reduce the data dimension and select significant features. Logistic regression (LR) analysis was applied to establish the radiomics model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the comprehensive performance of the model. Results Among the 203 patients, 33 (16.7%) were LVI positive and 170 (83.7%) were LVI negative. A total of 5350 (90.1%) radiomic features with ICC values of ≥ 0.75 were reported, which were subsequently subjected to hypothesis testing and LASSO regression dimension reduction analysis. Finally, 15 selected features were used to construct the radiomics model. The area under the curve (AUC) of the training set was 0.849, and the AUC of the validation set was 0.781. The calibration curve indicated that the radiomics model had good calibration, and DCA demonstrated that the model had clinical benefits. Conclusion The proposed endorectal ultrasound-based radiomics model has the potential to predict LVI preoperatively in RC.