Accumulating evidence suggests that circular RNAs (circRNAs) may be a key contributor to oncogenesis. Yet, the function of circRNAs in laryngeal squamous cell carcinoma (LSCC) is still not clear. In this study, we examined the function of circRNA_103862 in LSCC progression by analyzing the tissue specimens collected from a patient with LSCC by using different LSCC cell models in vitro and an LSCC xenograft model in nude mice. We found that circRNA_103862 was frequently upregulated in the tissues of LSCC and was correlated with metastasis and prognosis of LSCC patients. Furthermore, circRNA_103862 downregulation could reduce proliferation, migration, and invasion ability of LSCC cells. In terms of mechanism exploration, miR-493-5p was sponged by circRNA_103862. Rescue experiments also showed that circRNA_103862 could achieve a carcinogenic effect by regulating miR-493-5p. Moreover, a luciferase reporter analysis showed that Golgi membrane protein 1 (GOLM1) is a downstream effector of miR-493-5p. In conclusion, our data suggested that circRNA_103862 promotes the proliferation of LSCC through targeting the miR-493-5p/GOLM1 axis, and it might serve as a potential prognosis marker and therapy target for LSCC.

Background: Uveal melanoma (UM) is the most common primary intraocular cancer in adults. Genomic studies have provided insights into molecular subgroups and oncogenic drivers of UM that may lead to novel therapeutic strategies. Methods: Dataset TCGA-UVM, download from TCGA portal, were taken as the training cohort, and dataset GSE22138, obtained from GEO database, was set as the validation cohort. In training cohort, Kaplan-Meier analysis and univariate Cox regression model were applied to preliminary screen prognostic genes. Besides, the Cox regression model with LASSO was implemented to build a multi-gene signature, which was then validated in the validation cohorts through Kaplan-Meier, Cox, and ROC analyses. In addition, the correlation between copy number aberrations and risk score was evaluated by Spearman test. GSEA and immune infiltrating analyses were conducted for understanding function annotation and the role of the signature in the tumor microenvironment. Results: A ten-gene signature was built, and it was examined by Kaplan-Meier analysis revealing that significantly overall survival, progression-free survival, and metastasis-free survival difference was seen. The ten-gene signature was further proven to be an independent risk factor compared to other clinic-pathological parameters via the Cox regression analysis. Moreover, the receiver operating characteristic curve (ROC) analysis results demonstrated a better predictive power of the UM prognosis that our signature owned. The ten-gene signature was significantly correlated with copy numbers of chromosome 3, 8q, 6q, and 6p. Furthermore, GSEA and immune infiltrating analyses showed that the signature had close interactions with immune-related pathways and the tumor environment. Conclusions: Identifying the ten-gene signature (SIRT3, HMCES, SLC44A3, TCTN1, STPG1, POMGNT2, RNF208, ANXA2P2, ULBP1, and CA12) could accurately identify patients' prognosis and had close interactions with the immunodominant tumor environment, which may provide UM patients with personalized prognosis prediction and new treatment insights.

Dynein axonemal assembly factor 5 (DNAAF5) is the transcription factor of regulating the cytoskeleton and hydrodynamic protein complex assembly, however, it was not well elucidated in the malignant progression of hepatocellular carcinoma (HCC).

To establish a predictive model incorporating clinical features and contrast enhanced ultrasound liver imaging and reporting and data system (CEUS LI-RADS) for estimation of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients.

The aim was to build a predictive model based on ultrasonography (US)-based deep learning model (US-DLM) and clinical features (Clin) for differentiating hepatocellular carcinoma (HCC) from other malignancy (OM) in cirrhotic patients. 112 patients with 120 HCCs and 60 patients with 61 OMs were included. They were randomly divided into training and test cohorts with a 4:1 ratio for developing and evaluating US-DLM model, respectively. Significant Clin predictors of OM in the training cohort were combined with US-DLM to build a nomogram predictive model (US-DLM+Clin). The diagnostic performance of US-DLM and US-DLM+Clin were compared with that of contrast enhanced magnetic resonance imaging (MRI) liver imaging and reporting system category M (MRI LR-M). US-DLM was the best independent predictor for evaluating OMs, followed by clinical information, including high cancer antigen 199 (CA199) level and female. The US-DLM achieved an AUC of 0.74 in the test cohort, which was comparable with that of MRI LR-M (AUC=0.84, p=0.232). The US-DLM+Clin for predicting OMs also had similar AUC value (0.81) compared with that of LR-M+Clin (0.83, p>0.05). US-DLM+Clin obtained a higher specificity, but a lower sensitivity, compared to that of LR-M +Clin (Specificity: 82.6% vs. 73.9%, p=0.007; Sensitivity: 78.6% vs. 92.9%, p=0.006) for evaluating OMs in the test set. The US-DLM+Clin model is valuable in differentiating HCC from OM in the setting of cirrhosis.

The Guanylate binding proteins (GBPs) are a family of large GTPases and the most studied GBP family member is the guanylate binding protein 1 (GBP1). Earlier studies revealed that GBP1 expression was inflammatory cytokines-inducible, and most of the studies focused on inflammation diseases. Increasing number of cancer studies began to reveal its biological role in cancers recently, although with contradictory findings in literature. It was discovered from our earlier prostate cancer cell line models studies that when prostate cancer cells treated with either ethidium bromide or a cell cycle inhibitor flavopiridol for a long-term, the treatment-survived tumor cells experienced metabolic reprogramming toward Warburg effect pathways with greater aggressive features, and one common finding from these cells was the upregulation of GBP1. In this study, possible role of GBP1 in two independent prostate cancer lines by application of CRISR/Cas9 gene knockout (KO) technology was investigated. The GBP1 gene KO DU145 and PC3 prostate cancer cells were significantly less aggressive in vitro, with less proliferation, migration, wound healing, and colony formation capabilities, in addition to a significantly lower level of mitochondrial oxidative phosphorylation and glycolysis. At the same time, such GBP1 KO cells were significantly more sensitive to chemotherapeutic reagents. Xenograft experiments verified a significantly slower tumor growth of the GBP1 KO cells in nude mouse model. Furthermore, GBP1 protein expression in clinical prostate cancer sample revealed its aggressive clinical feature correlation and shorter overall survival association. Collectively, our results indicate a pro-survival or oncogenic role of GBP1 in prostate cancer.