Single cell landscape of potential mechanisms in primary and metastatic hepatocellular carcinoma.

Hepatocellular carcinoma metastasis occurs mainly in the portal vein and lymph node metastasis, and is the main cause of patient death. However, the cellular origins and driving mechanisms of hepatocellular carcinoma metastasis have not been elucidated. In this study, we found that different tumor metastasis samples originated from different branches of the primary tumor subclone.

Primary yolk sac tumor of the endometrium combined with situs inversus totalis: a case report and literature review.

Background: Yolk sac tumor (YST) is a highly malignant germ cell tumor, a majority of which originate from the gonads and are extremely rare from endometrium.

Case Presentation: Here we present a case of a 42-year-old woman suffered from primary pure yolk sac tumor of the endometrium complicated with situs inversus totalis. The patient presented at our hospital with irregular vaginal bleeding.

Construction of an immune-related gene prognostic model for obese endometrial cancer patients based on bioinformatics analysis.

Background: The tumor microenvironment (TME) affected the prognosis of tumors. However, its effect on the outcomes of obese endometrial cancer (EC) patients had not been reported.

Methods: This research performed a retrospective analysis of the transcriptome profiles and medical data of 503 EC patients.

A deep learning drug screening framework for integrating local-global characteristics: A novel attempt for limited data.

At the beginning of the "Disease X" outbreak, drug discovery and development are often challenged by insufficient and unbalanced data. To address this problem and maximize the information value of limited data, we propose a drug screening model, LGCNN, based on convolutional neural network (CNN), which enables rapid drug screening by integrating features of drug molecular structures and drug-target interactions at both local and global (LG) levels. Experimental results show that LGCNN exhibits better performance compared to other state-of-the-art classification methods under limited data.

Crosstalk of different cell-death patterns predicts prognosis and drug sensitivity in glioma.

Background: Glioma is a malignant brain tumor originating from glial cells, and there still a challenge to accurately predict the prognosis. Programmed cell death (PCD) plays a key role in tumorigenesis and immune response. However, the crosstalk and potential role of various PCDs in prognosis and tumor microenvironment remains unknown.

Predicting drug synergy using a network propagation inspired machine learning framework.

Combination therapy is a promising strategy for cancers, increasing therapeutic options and reducing drug resistance. Yet, systematic identification of efficacious drug combinations is limited by the combinatorial explosion caused by a large number of possible drug pairs and diseases. At present, machine learning techniques have been widely applied to predict drug combinations, but most studies rely on the response of drug combinations to specific cell lines and are not entirely satisfactory in terms of mechanism interpretability and model scalability.

Multi-level characteristics recognition of cancer core therapeutic targets and drug screening for a broader patient population.

Target therapy for cancer cell mutation has brought attention to several challenges in clinical applications, including limited therapeutic targets, less patient benefits, and susceptibility to acquired due to their clear biological mechanisms and high specificity in targeting cancers with specific mutations. However, the identification of truly lethal synthetic lethal therapeutic targets for cancer cells remains uncommon, primarily due to compensatory mechanisms. In our pursuit of core therapeutic targets (CTTs) that exhibit extensive synthetic lethality in cancer and the corresponding potential drugs, we have developed a machine-learning model that utilizes multiple levels and dimensions of cancer characterization.

Integrating multiple machine learning algorithms for prognostic prediction of gastric cancer based on immune-related lncRNAs.

Long non-coding RNAs (lncRNAs) play an important role in the immune regulation of gastric cancer (GC). However, the clinical application value of immune-related lncRNAs has not been fully developed. It is of great significance to overcome the challenges of prognostic prediction and classification of gastric cancer patients based on the current study.

The Tribological Properties of 30CrMnSiA Bearing Steels Treated by the Strengthening Grinding Process under Lubrication Wear.

This study used the strengthening grinding process (SGP) to treat the surface of 30CrMnSiA bearing steels. The effect of the jet angle of SGP on the tribological properties of 30CrMnSiA bearing steels under lubrication was investigated. The principle of enhancing wear resistance of 30CrMnSiA bearing steel ascribed to SGP was discussed in detail.

Exploring Precise Medication Strategies for OSCC Based on Single-Cell Transcriptome Analysis from a Dynamic Perspective.

At present, most patients with oral squamous cell carcinoma (OSCC) are in the middle or advanced stages at the time of diagnosis. Advanced OSCC patients have a poor prognosis after traditional therapy, and the complex heterogeneity of OSCC has been proven to be one of the main reasons. Single-cell sequencing technology provides a powerful tool for dissecting the heterogeneity of cancer.

Concepts and applications of chemical fingerprint for hit and lead screening.

Molecular fingerprints are used to represent chemical (structural, physicochemical, etc.) properties of large-scale chemical sets in a low computational cost way. They have a prominent role in transforming chemical data sets into consistent input formats (bit strings or numeric values) suitable for in silico approaches.

Identification of Warning Transition Points from Hepatitis B to Hepatocellular Carcinoma Based on Mutation Accumulation for the Early Diagnosis and Potential Drug Treatment of HBV-HCC.

The accumulation of multiple genetic mutations is essential during the occurrence and development of hepatocellular carcinoma induced by hepatitis B (HBV-HCC), but understanding their cooperative effects and identifying the warning transition point from hepatitis B to HCC are challenges. In the genomic analysis of somatic mutations of the patient with HBV-HCC in a patient-specific protein-protein interaction (ps-PPI) network, we find mutation influence can propagate along the ps-PPI network. Therefore, in the article, we got the mutation cluster as a new research unit using the Random Walks with Restarts algorithm that is used to describe the efficient boundary of mutation influences.

Computational Prediction of Drug Phenotypic Effects Based on Substructure-Phenotype Associations.

Identifying drug phenotypic effects, including therapeutic effects and adverse drug reactions (ADRs), is an inseparable part for evaluating the potentiality of new drug candidates (NDCs). However, current computational methods for predicting phenotypic effects of NDCs are mainly based on the overall structure of an NDC or a related target. These approaches often lead to inconsistencies between the structures and functions and limit the prediction space of NDCs.

Flight and Reproduction Variations of Rice Leaf Roller, in Response to Different Rearing Temperatures.

Understanding how species that follow different life-history strategies respond to stressful temperature can be essential for efficient treatments of agricultural pests. Here, we focused on how the development, reproduction, flight, and reproductive consequences of migration of were influenced by exposure to different rearing temperatures in the immature stage. We found that the immature rice leaf roller that were reared at low temperatures (18 and 22 °C) developed more slowly than the normal temperature 26 °C, while those reared at high temperatures (34 °C) grew faster.

Precision treatment exploration of breast cancer based on heterogeneity analysis of lncRNAs at the single-cell level.

Background: Breast cancer (BC) is a complex disease with high heterogeneity, which often leads to great differences in treatment results. Current common molecular typing method is PAM50, which shows positive results for precision medicine; however, room for improvement still remains because of the different prognoses of subtypes. Therefore, in this article, we used lncRNAs, which are more tissue-specific and developmental stage-specific than other RNAs, as typing markers and combined single-cell expression profiles to retype BC, to provide a new method for BC classification and explore new precise therapeutic strategies based on this method.

Computational drug repositioning for ischemic stroke: neuroprotective drug discovery.

A comprehensive approach to drug repositioning will be required to overcome translational hurdles and identify more neuroprotective drugs. Gene Set Enrichment Analysis was applied to identify related pathways and enriched genes. Candidate genes were optimized using ToppGene, ToppGenet and pBRIT.

New insights for precision treatment of glioblastoma from analysis of single-cell lncRNA expression.

Introduction: Glioblastoma (GBM) is a complex disease with high intratumoral heterogeneity, understanding the molecular characteristics of intratumoral heterogeneity accurately is the basis for precision treatment. Although the existing typing strategy based on tumor molecular characteristics has a positive effect, there is still room for improvement, which is mainly because the traditional typing is completed based on the sequencing data of tissue samples, that is, the obtained data are the average level of patient tumor tissues, masking the intratumoral heterogeneity of a single patient and cannot reflect the real level of patient tumor cells. At present, cancer molecular typing is mostly performed based on transcriptome (RNA-seq) without considering lncRNA molecules that are also tissue-specific and developmental stage-specific.

Computational drug repositioning based on the relationships between substructure-indication.

At present, computational methods for drug repositioning are mainly based on the whole structures of drugs, which limits the discovery of new functions due to the similarities between local structures of drugs. In this article, we, for the first time, integrated the features of chemical-genomics (substructure-domain) and pharmaco-genomics (domain-indication) based on the assumption that drug-target interactions are mediated by the substructures of drugs and the domains of proteins to identify the relationships between substructure-indication and establish a drug-substructure-indication network for predicting all therapeutic effects of tested drugs through only information on the substructures of drugs. In total, 83 205 drug-indication relationships with different correlation scores were obtained.

Identification of Early Warning Signals at the Critical Transition Point of Colorectal Cancer Based on Dynamic Network Analysis.

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. Due to the lack of early diagnosis methods and warning signals of CRC and its strong heterogeneity, the determination of accurate treatments for CRC and the identification of specific early warning signals are still urgent problems for researchers. In this study, the expression profiles of cancer tissues and the expression profiles of tumor-adjacent tissues in 28 CRC patients were combined into a human protein-protein interaction (PPI) network to construct a specific network for each patient.

CHG: A Systematically Integrated Database of Cancer Hallmark Genes.

Background: The analysis of cancer diversity based on a logical framework of hallmarks has greatly improved our understanding of the occurrence, development and metastasis of various cancers.

Methods: We designed Cancer Hallmark Genes (CHG) database which focuses on integrating hallmark genes in a systematic, standard way and annotates the potential roles of the hallmark genes in cancer processes. Following the conceptual criteria description of hallmark function the keywords for each hallmark were manually selected from the literature.

Bioinformatics profiling utilized a nine immune-related long noncoding RNA signature as a prognostic target for pancreatic cancer.

Purpose: To identify an immune-related long noncoding RNA (lncRNA) signature with potential prognostic value for patients with pancreatic cancer.

Methods: Pancreatic cancer samples with available clinical information and whole genomic mRNA expression data obtained from The Cancer Genome Atlas (TCGA) were enrolled in our research. The immune score of each sample was calculated according to the expression level of immune-related genes and used to identify the most promising immune-related lncRNAs.