Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies

ObjectiveSignificant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy.MethodsWe retrieved gene expression and clinical data of patients with HCC from the GSE14520 dataset and the Cancer Genome Atlas (TCGA) database. Next, we used the “one-class logistic regression (OCLR)” algorithm to obtain the mRNAsi of patients with HCC. We performed “unsupervised consensus clustering” to classify patients with HCC based on the mRNAsi scores and stemness subtypes. The relationships between the mRNAsi model, clinicopathological features, and genetic profiles of patients were compared using various bioinformatic methods. We screened for differentially expressed genes to establish a stemness-based classifier for predicting the patient’s prognosis. Next, we determined the effect of risk scores on the tumor immune microenvironment (TIME) and the response of patients to immune checkpoint blockade (ICB). Finally, we used qRT-PCR to investigate gene expression in patients with HCC.ResultsWe screened CSC-related genes using various bioinformatics tools in patients from the TCGA-LIHC cohort. We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient’s prognosis and response to ICBs. Further, the model was validated in an independent GSE14520 dataset and performed well. Our model could predict the status of TIME, immunogenomic expressions, congenic pathway, and response to chemotherapy drugs. Furthermore, a significant increase in the proportion of infiltrating macrophages, Treg cells, and immune checkpoints was observed in patients in the high-risk group. In addition, tumor cells in patients with high mRNAsi scores could escape immune surveillance. Finally, we observed that the constructed model had a good expression in the clinical samples. The HCC tumor size and UCK2 genes expression were significantly alleviated and decreased, respectively, by treatments of anti-PD1 antibody. We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines.ConclusionThe novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC

Vehicle Routing Problem with Uncertain Demands: An Advanced Particle Swarm Algorithm

The Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades. However, the main focus has been on the deterministic version where customer demands are fixed and known in advance. Uncertainty in demand has not received enough consideration. When demands are uncertain, several problems arise in the VRP. For example, there might be unmet customers¿ demands, which eventually lead to profit loss. A reliable plan and set of routes, after solving the VRP, can significantly reduce the unmet demand costs, helping in obtaining customer satisfaction. This paper investigates a variant of an uncertain VRP in which the customers¿ demands are supposed to be uncertain with unknown distributions. An advanced Particle Swarm Optimization (PSO) algorithm has been proposed to solve such a VRP. A novel decoding scheme has also been developed to increase the PSO efficiency. Comprehensive computational experiments, along with comparisons with other existing algorithms, have been provided to validate the proposed algorithms.Moghaddam, BF.; Ruiz García, R.; Sadjadic, SJ. (2012). Vehicle Routing Problem with Uncertain Demands: An Advanced Particle Swarm Algorithm. Computers and Industrial Engineering. 62(1):306-317. doi:10.1016/j.cie.2011.10.001S30631762

Hereditary Nonpolyposis Colorectal Cancer and Cancer Syndromes: Recent Basic and Clinical Discoveries

Approximately one-third of individuals diagnosed with colorectal cancer have a family history of cancer, suggesting that CRCs may result from a heritable component. Despite the availability of current gene-identification techniques, only 5% of all CRCs emerge from well-identifiable inherited causes for predisposition, including polyposis and nonpolyposis syndromes. Hereditary nonpolyposis colorectal cancer represents a large proportion of cases, and robustly affected patients are at increased risk for early onset, synchronous, and metachronous colorectal malignancies and extracolonic malignancies. HNPCC encompasses several cancer syndromes, such as Lynch syndrome, Lynch-like syndrome, and familial colorectal cancer type X, which have remarkable clinical presentations and overlapping genetic profiles that make clinical diagnosis a challenging task. Therefore, distinguishing between the HNPCC disorders is crucial for physicians as an approach to tailor different recommendations for patients and their at-risk family members according to the risks for colonic and extracolonic cancer associated with each syndrome. Identification of these potential patients through epidemiological characteristics and new genetic testing can estimate the individual risk, which informs appropriate cancer screening, surveillance, and/or treatment strategies. In the past three years, many appealing and important advances have been made in our understanding of the relationship between HNPCC and CRC-associated syndromes. The knowledge from the genetic profile of cancer syndromes and unique genotype-phenotype profiles in the different syndromes has changed our cognition. Therefore, this review presents and discusses HNPCC and several common nonpolyposis syndromes with respect to molecular phenotype, histopathologic features, and clinical presentation

Small but Heavy Role: MicroRNAs in Hepatocellular Carcinoma Progression

Hepatocellular carcinoma (HCC), which accounts for 85–90% of primary liver cancer, is the fifth most common malignant tumor and the third leading cause of cancer-related deaths worldwide, but the pathological mechanism of HCC is still not fully elucidated. miRNAs are evolutionarily endogenous small noncoding RNAs that negatively regulate gene expression via posttranscriptional inhibition or target mRNA degradation in several diseases, especially human cancer. Therefore, discovering the roles of miRNAs is appealing to scientific researchers. Emerging evidence has shown that the aberrant expressions of numerous miRNAs are involved in many HCC biological processes. In hepatocarcinogenesis, miRNAs with dysregulated expression can exert their function as oncogenes or tumor suppressors depending on their cellular target during the cell cycle, and in tumor development, differentiation, apoptosis, angiogenesis, metastasis, and progression of the tumor microenvironment. In this review, we summarize current findings on miRNAs and assess their functions to explore the molecular mechanisms of tumor progression in HCC

Analysis and Comparison of Aroma Compounds of Brown Sugar in Guangdong, Guangxi and Yunnan Using GC-O-MS

Guangdong, Guangxi and Yunnan are the three provinces in China that yield the most brown sugar, a brown-red colored solid or powdered sugar product made from sugar cane. In the present study, the differences between odor compounds of brown sugar from Guangdong, Guangxi, and Yunnan provinces in China were compared and analyzed by gas chromatography-olfactometry-mass spectrometry (GC-O-MS). A total of 80 odor compounds, including 5 alcohols, 9 aldehydes, 8 phenols, 21 acids, 14 ketones, 5 esters, 12 pyrazines, and 6 other compounds, were detected. The fingerprint analysis of the brown sugar odor compounds showed 90% similarity, indicating a close relationship among the odor properties of brown sugar in each province. Moreover, the orthogonal partial least squares discriminant analysis (OPLS-DA) was performed to identify the compounds contributing to the volatile classification of the brown sugar from three provinces, which confirmed that OPLS-DA could be a potential tool to distinguish the brown sugar of three origins

Efficacy of Jiuzao polysaccharides in ameliorating alcoholic fatty liver disease and modulating gut microbiota

Jiuzao, the residue from Baijiu production, has shown radical scavenging properties in prior investigations, suggesting its potential as a hepatoprotective agent against acute liver damage. This study reveals that Jiuzao polysaccharides ameliorated liver morphological damage in zebrafish larvae afflicted with alcoholic fatty liver disease (AFLD), as evidenced by Oil red O, H&E, and Nile red staining. These polysaccharides notably modulated antioxidant enzyme levels and lipid peroxidation components. The real-time quantitative polymerase chain reactions analyses illustrated the significant impact of Jiuzao polysaccharides on genes integral to ethanol and lipid metabolism. The 16 S rRNA results showed that Jiuzao polysaccharides could improve the intestinal flora in zebrafish larvae exposed to ethanol. In summary, Jiuzao polysaccharides efficaciously mitigate liver lipid accumulation, enhance ethanol metabolism, and reduce oxidative stress by downregulating genes involved in AFLD development. They also regulate the changes in gut microbiota, providing further protection against acute alcoholic liver insult in zebrafish larvae

6-DOF Bilateral Teleoperation Hybrid Control System for Power Distribution Live-Line Operation Robot

In the master-slave heterogeneous teleoperation, the workspace of the slave manipulator is usually much larger than that of the master manipulator. This paper proposes a 6-DOF bilateral hybrid teleoperation control strategy to map the workspace of the manipulators without changing the operation accuracy. The hybrid control includes the admittance and force control based on the feedback of the force sensor at the end of the manipulator. The two control strategies switched autonomously through the positioning of the Sigma.7 handle in the workspace. Compared with the classic bilateral teleoperation control, it overcomes the limitation of pre-matching the workspace of the master and slave. When the tool contacts a rigid environment, the robot can make adaptive compensation through the admittance controller even if the operator has not responded. We conduct extensive experiments to evaluate the changes in displacement and velocity before and after the switching process and under different admittance controller parameters. Finally, teleoperation is applied to live-line operation in distribution networks. The experiment proved that the control strategy is more consistent with human operation habits and can improve assembly success rate and efficiency

Intelligent power distribution live‐line operation robot systems based on stereo camera

Abstract Maintenance tasks in distribution networks are often accompanied by hazards associated with high altitudes and high voltages. By utilising robots instead of human operators to perform these tasks, potential risks can be avoided, while productivity can be increased. This research proposes an intelligent power distribution live‐line operation robot (PDLOR) system based on a stereo camera to replace human to complete work. The PDLOR system consists of several key components, including dual manipulators, wireless tools, a visual perception system, an insulated bucket truck, and a ground control terminal. Once the task is confirmed, the real‐time vision system identification and positioning enable the adjustment of the insulated bucket to position the robot correctly for its intended work. The stereo camera plays a crucial role in accurately recognising and estimating the object's orientation. Additionally, a simplified reconstruction is performed within a virtual simulation environment, which aids in collision detection during path planning. After obtaining the optimal path, it is then communicated to the real manipulator for execution. To validate the feasibility of the PDLOR system, field experiments were conducted in actual distribution network scenarios. The results demonstrate that the PDLOR effectively completes single‐phase power‐line connection tasks within a remarkable 10‐min timeframe