Identifying diagnostic biomarkers for cancer is crucial in the field of personalized medicine. The available transcriptome and interactome provide unprecedented opportunities and challenges for biomarker screening. From a systematic perspective, network-based medicine methods provide alternative approaches to organizing the available high-throughput omics data for deciphering molecular interactions and their associations with phenotypic states. In this work, we propose a bioinformatics strategy named TopMarker for discovering diagnostic biomarkers by comparing the network topology differences in control and disease samples. Specifically, we build up gene-gene interaction networks in the two states of control and disease respectively. The network rewiring status across the two networks results in differential network topologies reflecting dynamics and changes in normal samples when compared with those in disease. Thus, we identify the potential biomarker genes with differential network topological parameters between the control and disease gene networks. For a proof-of-concept study, we introduce the computational pipeline of biomarker discovery in hepatocellular carcinoma (HCC). We prove the effectiveness of the proposed TopMarker method using these candidate biomarkers in classifying HCC samples and validate its signature capability across numerous independent datasets. We also compare the discriminant power of biomarker genes identified by TopMarker with those identified by other baseline methods. The higher classification performances and functional implications indicate the advantages of our proposed method for discovering biomarkers from differential network topology.

The root system plays a decisive role in the growth and development of plants. The water requirement of a root system depends strongly on the plant species. Potatoes are an important food and vegetable crop grown worldwide, especially under irrigation in arid and semi-arid regions. However, the expected impact of global warming on potato yields calls for an investigation of genes related to root development and drought resistance signaling pathways in potatoes. In this study, we investigated the molecular mechanisms of different drought-tolerant potato root systems in response to drought stress under controlled water conditions, using potato as a model. We analyzed the transcriptome and proteome of the drought-sensitive potato cultivar Atlantic (Atl) and the drought-tolerant cultivar Qingshu 9 (Q9) under normal irrigation (CK) and weekly drought stress (D). The results showed that a total of 14,113 differentially expressed genes (DEGs) and 5596 differentially expressed proteins (DEPs) were identified in the cultivars. A heat map analysis of DEGs and DEPs showed that the same genes and proteins in Atl and Q9 exhibited different expression patterns under drought stress. Weighted gene correlation network analysis (WGCNA) showed that in Atl, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG)-enriched pathways were related to pyruvate metabolism and glycolysis, as well as cellular signaling and ion transmembrane transporter protein activity. However, GO terms and KEGG-enriched pathways related to phytohormone signaling and the tricarboxylic acid cycle were predominantly enriched in Q9. The present study provides a unique genetic resource to effectively explore the functional genes and uncover the molecular regulatory mechanism of the potato root system in response to drought stress.

Biomarker screening is critical for precision oncology. However, one of the main challenges in precision oncology is that the screened biomarkers often fail to achieve the expected clinical effects and are rarely approved by regulatory authorities. Considering the close association between cancer pathogenesis and the evolutionary events of organisms, we first explored the evolutionary feature underlying clinically approved biomarkers, and two evolutionary features of approved biomarkers (Ohnologs and specific evolutionary stages of genes) were identified. Subsequently, we utilized evolutionary features for screening potential prognostic biomarkers in four common cancers: head and neck squamous cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. Finally, we constructed an evolution-strengthened prognostic model (ESPM) for cancers. These models can predict cancer patients\' survival time across different cancer cohorts effectively and perform better than conventional models. In summary, our study highlights the application potentials of evolutionary information in precision oncology biomarker screening.

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.

Central precocious puberty (CPP) is a developmental disorder caused by early activation of the hypothalamic-pituitary-gonadal axis. The incidence of CPP is rapidly increasing, but the underlying mechanisms are not fully understood. Previous studies have shown that gain-of-function mutations in the KISS1R and KISS1 genes and loss-of-function mutations in the MKRN3, LIN28, and DLK1 genes may lead to early initiation of pubertal development. Recent research has also revealed the significant role of epigenetic factors such as DNA methylation and microRNAs in the regulation of gonadotropin-releasing hormone neurons, as well as the modulating effect of gene networks involving multiple variant genes on pubertal initiation. This review summarizes the genetic etiology and pathogenic mechanisms underlying CPP.
中枢性性早熟（central precocious puberty, CPP）是下丘脑-垂体-性腺轴提早激活所导致的发育异常性疾病，其发病率快速增加，但发病机制尚未完全明确。既往研究发现KISS1R、KISS1基因的功能获得性突变，以及MKRN3、LIN28和DLK1基因的功能缺失性突变可导致青春发育期启动时间提前。新近研究发现表观遗传因素如DNA甲基化、微小核糖核酸在促性腺激素释放激素神经元的调控中起重要作用；基因网络中多个变异基因的协同作用也可影响青春发育启动。该文综述了导致CPP的遗传学病因进展及其致病机制。.

This study focused on examining the exact role of circulating immune cells in the development of diabetic retinopathy (DR). In vitro co-culture experiments showed that peripheral blood mononuclear cells (PBMCs) from patients with type 1 DR crucially modulated the biological functions of human retinal microvascular endothelial cells (HRMECs), consequently disrupting their normal functionality. Single-cell RNA sequencing (scRNA-seq) study revealed unique differentially expressed genes and pathways in circulating immune cells among healthy controls, non-diabetic retinopathy (NDR) patients, and DR patients. Of significance was the observed upregulation of JUND in each subset of PBMCs in patients with type 1 DR. Further studies showed that downregulating JUND in DR patient-derived PBMCs led to the amelioration of HRMEC dysfunction. These findings highlighted the notable alterations in the transcriptomic patterns of circulating immune cells in type 1 DR patients and underscored the significance of JUND as a key factor for PBMCs in participating in the pathogenesis of DR.

This work focuses on data sampling in cancer-gene association prediction. Currently, researchers are using machine learning methods to predict genes that are more likely to produce cancer-causing mutations. To improve the performance of machine learning models, methods have been proposed, one of which is to improve the quality of the training data. Existing methods focus mainly on positive data, i.e. cancer driver genes, for screening selection. This paper proposes a low-cancer-related gene screening method based on gene network and graph theory algorithms to improve the negative samples selection. Genetic data with low cancer correlation is used as negative training samples. After experimental verification, using the negative samples screened by this method to train the cancer gene classification model can improve prediction performance. The biggest advantage of this method is that it can be easily combined with other methods that focus on enhancing the quality of positive training samples. It has been demonstrated that significant improvement is achieved by combining this method with three state-of-the-arts cancer gene prediction methods.

Intramuscular fat (IMF) refers to the fat that accumulates between muscle bundles or within muscle cells, whose content significantly impacts the taste, tenderness, and flavor of meat products, making it a crucial economic characteristic in livestock production. However, the intricate mechanisms governing IMF deposition, involving non-coding RNAs (ncRNAs), genes, and complex regulatory networks, remain largely enigmatic. Identifying adipose tissue-specific genes and ncRNAs is paramount to unravel these molecular mysteries. This study, conducted on Jiaxian red cattle, harnessed whole transcriptome sequencing to unearth the nuances of circRNAs and miRNAs across seven distinct tissues. The interplay of these ncRNAs was assessed through differential expression analysis and network analysis. These findings are not only pivotal in unveiling the intricacies of fat deposition mechanisms but also lay a robust foundation for future research, setting the stage for enhancing IMF content in Jiaxian red cattle breeding.

Stochastic gene expression dynamics can be modelled either discretely or continuously. Previous studies have shown that the mRNA or protein number distributions of some simple discrete and continuous gene expression models are related by Gardiner\'s Poisson representation. Here, we systematically investigate the Poisson representation in complex stochastic gene regulatory networks. We show that when the gene of interest is unregulated, the discrete and continuous descriptions of stochastic gene expression are always related by the Poisson representation, no matter how complex the model is. This generalizes the results obtained in Dattani & Barahona (Dattani & Barahona 2017 J. R. Soc. Interface 14, 20160833 (doi:10.1098/rsif.2016.0833)). In addition, using a simple counter-example, we find that the Poisson representation in general fails to link the two descriptions when the gene is regulated. However, for a general stochastic gene regulatory network, we demonstrate that the discrete and continuous models are approximately related by the Poisson representation in the limit of large protein numbers. These theoretical results are further applied to analytically solve many complex gene expression models whose exact distributions are previously unknown.

Papaya is a tropical fruit crop renowned for its rich nutrition, particularly pro-vitamin A. Aroma substances are a major component of fruit quality. While extensive research has been conducted on papaya aroma, there has been a notable lack of in-depth research into a specific class of substances. To bridge this gap, our study focused on analyzing the aroma components of various papaya varieties and their biosynthesis pathways. We compared the volatile components of three papaya varieties with distinct flavors at various ripeness stages. A continuous accumulation of linalool, a volatile compound, in the \'AU9\' fruit was detected as it matured. The linalool content reached 56% of the total volatile components upon full ripening. Notably, this percentage was significantly higher than that observed in the other two varieties, \'ZhongBai\' and \'Malaysian 7\', indicating that linalool serves as the primary component influencing the papaya\'s odor. Subsequently, we identified CpTPS18, a gene associated with linalool biosynthesis, and demonstrated its ability to catalyze linalool production from GPP and enhance its accumulation through overexpression in papaya fruits, both in vivo and in vitro. Based on transcriptomic analysis, it was predicted that CpMYB56 and CpNAC56 may transcriptionally activate the expression of CpTPS18. Subsequent yeast one-hybrid assay and dual luciferase analysis revealed that CpNAC56 activates the transcription of CpTPS18. Transient overexpression in vivo demonstrated that this gene could upregulate the expression of CpTPS18 and promote linalool accumulation. These results uncovered the primary volatile molecule responsible for papaya fruit odor and identified two major genes influencing its biosynthesis. The genomic resources and information obtained from this study will expedite papaya improvement for fruit quality.