pcnaDeep: a fast and robust single-cell tracking method using deep-learning mediated cell cycle profiling.

SUMMARY: Computational methods that track single cells and quantify fluorescent biosensors in time-lapse microscopy images have revolutionized our approach in studying the molecular control of cellular decisions. One barrier that limits the adoption of single-cell analysis in biomedical research is the lack of efficient methods to robustly track single cells over cell division events. Here, we developed an application that automatically tracks and assigns mother-daughter relationships of single cells. By incorporating cell cycle information from a well-established fluorescent cell cycle reporter, we associate mitosis relationships enabling high fidelity long-term single-cell tracking. This was achieved by integrating a deep-learning-based fluorescent proliferative cell nuclear antigen signal instance segmentation module with a cell tracking and cell cycle resolving pipeline. The application offers a user-friendly interface and extensible APIs for customized cell cycle analysis and manual correction for various imaging configurations. AVAILABILITY AND IMPLEMENTATION: pcnaDeep is an open-source Python application under the Apache 2.0 licence. The source code, documentation and tutorials are available at https://github.com/chan-labsite/PCNAdeep. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AGLR is a novel index for the prognosis of hepatocellular carcinoma patients: a retrospective study.

BACKGROUND: Most hepatocellular carcinoma (HCC) patients' liver function indexes are abnormal. We aimed to investigate the relationship between (alkaline phosphatase + gamma-glutamyl transpeptidase)/lymphocyte ratio (AGLR) and the progression as well as the prognosis of HCC. METHODS: A total of 495 HCC patients undergoing radical hepatectomy were retrospectively analyzed. We randomly divided these patients into the training cohort (n = 248) and the validation cohort (n = 247). In the training cohort, receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of AGLR for predicting postoperative survival of HCC patients, and the predictive value of AGLR was evaluated by concordance index (C-index). Further analysis of clinical and biochemical data of patients and the correlation analysis between AGLR and other clinicopathological factors were finished. Univariate and multivariate analyses were performed to identify prognostic factors for HCC patients. Survival curves were analyzed using the Kaplan-Meier method. RESULTS: According to the ROC curve analysis, the optimal predictive cut-off value of AGLR was 90. The C-index of AGLR was 0.637 in the training cohort and 0.654 in the validation cohort, respectively. Based on this value, the HCC patients were divided into the low-AGLR group (AGLR ≤ 90) and the high-AGLR group (AGLR > 90). Preoperative AGLR level was positively correlated with alpha-fetoprotein (AFP), tumor size, tumor-node-metastasis (TNM) stage, and microvascular invasion (MVI) (all p < 0.05). In the training and validation cohorts, patients with AGLR > 90 had significantly shorter OS than patients with AGLR ≤ 90 (p < 0.001). Univariate and multivariate analyses of the training cohort (HR, 1.79; 95% CI 1.21-2.69; p < 0.001) and validation cohort (HR, 1.82; 95% CI 1.35-2.57; p < 0.001) had identified AGLR as an independent prognostic factor. A new prognostic scoring model was established based on the independent predictors determined in multivariate analysis. CONCLUSIONS: The elevated preoperative AGLR level indicated poor prognosis for patients with HCC; the novel prognostic scoring model had favorable predictive capability for postoperative prognosis of HCC patients, which may bring convenience for clinical management.

Data mining of the expression and regulatory role of BCAT1 in hepatocellular carcinoma.

Branched chain amino acid transaminase 1 (BCAT1) catalyzes the production of glutamates and branched-chain α-ketoacids from branched chain amino acids, and a normal BCAT1 expression is associated with tumorigenesis. Sequencing data from public databases, including The Cancer Genome Atlas, was used to analyze BCAT1 expression and regulation networks for hepatocellular carcinoma (HCC). Expression and methylation were assessed using UALCAN analysis, and data from multiple datasets concerning the BCAT1 expression level and associated survival rates were further analyzed using HCCDB; interaction networks of biological function were constructed using GeneMANIA. LinkedOmics was used to indicate correlations between BCAT1 and any identified differentially expressed genes. Gene enrichment analysis of BCAT-associated genes was conducted using the Web-based Gene SeT AnaLysis Toolkit. The expression levels of BCAT1 were increased in patients with HCC and in most cases, the level of BCAT1 promoter methylation was reduced. Interaction network analysis suggested that BCAT1 was involved in 'metabolism', 'carcinogenesis' and the 'immune response' via numerous cancer-associated pathways. The present study revealed the expression patterns and potential function networks of BCAT1 in HCC, providing insights for future research into the role of BCAT1 in hepatocarcinogenesis. In addition, the study provided researchers with a way to analyze the genes of interest so they can continue their research in the right direction.

Prognostic Value of Gamma-Glutamyl Transpeptidase to Lymphocyte Count Ratio in Patients With Single Tumor Size ≤ 5 cm Hepatocellular Carcinoma After Radical Resection.

Prediction of prognosis of hepatocellular carcinoma (HCC) has shown an important role in improving treatment outcomes and preventing disease progression, however, the prognostic indicator of HCC is still lacking. The purpose of this study is to investigate the predictive value of GLR (gamma-glutamyl transpeptidase to lymphocyte count ratio) in single HCC with a tumor size (TS) ≤ 5 cm. A retrospective analysis was performed on 272 patients with TS ≤ 5 cm who underwent radical resection. The Pearson χ2 test was applied to discuss the relationship between HCC and GLR, alpha-fetoprotein (AFP). Then univariate and multivariate analysis was utilized to predict the risk factors for survival prognosis in patients. In this study, GLR showed a positive relation with tumor size, tumor-node-metastasis (TNM) stage, microvascular invasion, early recurrence, and serum aspartate aminotransferase (AST) level, while the AFP value only correlated with drinking. Elevated GLR value had poor overall survival (OS) and progression-free survival (PFS) of TS ≤ 5 cm HCC patients, GLR level and tumor size were closely related to the prognosis of small HCC patients compared with AFP. GLR may serve as a prognostic marker for dynamic monitoring of HCC patients with single TS ≤ 5 cm after radical resection.

Prospect of Circular RNA in Hepatocellular Carcinoma: A Novel Potential Biomarker and Therapeutic Target.

CircRNA, a kind of tissue specific and covalently closed circular non-coding RNA is very abundant in eukaryocyte. Generally, circRNA is generated by back-splicing of protein-coding genes' pre-mRNA. Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. Due to the characteristics of poor prognosis and high recurrence, the pathogenesis of HCC is highly concerned by researchers worldwide. Recent studies demonstrated that numerous circRNAs were differentially expressed in HCC tissues and normal liver tissues, which is closely related with the development and prognosis of HCC. However, the mechanism of circRNA in HCC remains unclear. In this review, we summarized the abnormal expressions of circRNAs in HCC, discussed its role, and potential mechanisms, and tried to explore the prospective values of circRNA in the diagnosis, therapy, and prognosis of HCC.