Factors associated with family decision-making self-efficacy among family members of patients with advanced cancer in mainland China.

OBJECTIVES: In Chinese culture, family members are the main decision maker on end-of-life (EoL) issues for patients with advanced cancer. Yet little is known about Chinese families' confidence in making EoL decisions and its associated factors. This study aims to investigate the status and associated factors of Chinese family members' confidence in making EoL decisions for patients with advanced cancer. METHODS: This cross-sectional study used a convenience sample of 147 family members of patients with stage III or stage IV cancer from a tertiary cancer center in Guangzhou, China. The questionnaires included demographic information of patients and their family members, patients' EoL preferences, and the Chinese version of the Family Decision-Making Self-Efficacy (FDMSE) Scale. RESULTS: A total of145 family members (98.64%) completed the questionnaires. The average score of FDMSE was 3.92 ± 0.53. A multiple regression analysis showed that the factors associated with FDMSE included patients' duration of disease, health insurance, participation in EoL decision-making, the expression of unfilled wishes, and family members' employment status. SIGNIFICANCE OF RESULTS: Chinese family members were not confident enough in making EoL decisions for patients with advanced cancer. It is recommended to develop cultural-tailored advanced care planning models to clarify patient preferences and to enhance the family members' self-efficacy in making EoL decisions with or for patients with advanced cancer.

Single-cell multiomics guided mechanistic understanding of Fontan-associated liver disease.

The Fontan operation is the current standard of care for single-ventricle congenital heart disease. Individuals with a Fontan circulation (FC) exhibit central venous hypertension and face life-threatening complications of hepatic fibrosis, known as Fontan-associated liver disease (FALD). The fundamental biology and mechanisms of FALD are little understood. Here, we generated a transcriptomic and epigenomic atlas of human FALD at single-cell resolution using multiomic snRNA-ATAC-seq. We found profound cell type-specific transcriptomic and epigenomic changes in FC livers. Central hepatocytes (cHep) exhibited the most substantial changes, featuring profound metabolic reprogramming. These cHep changes preceded substantial activation of hepatic stellate cells and liver fibrosis, suggesting cHep as a potential first "responder" in the pathogenesis of FALD. We also identified a network of ligand-receptor pairs that transmit signals from cHep to hepatic stellate cells, which may promote their activation and liver fibrosis. We further experimentally demonstrated that activins A and B promote fibrotic activation in vitro and identified mechanisms of activin A's transcriptional activation in FALD. Together, our single-cell transcriptomic and epigenomic atlas revealed mechanistic insights into the pathogenesis of FALD and may aid identification of potential therapeutic targets.

Examining the transformation of postindustrial land in reversing the lack of urban vitality: A paradigm spanning top-down and bottom-up approaches in urban planning studies.

A distinct rise of global interest in the role of cultural and creative industries in post-industrial urban development calls for the transformation of our industrial heritage into creative clusters. Despite many factories being removed in deindustrialization, some enterprises still preserve and reuse the former industrial lands. However, the inconclusive judgments on the performances of creative clusters call for an in-depth examination of the underlying mechanism of transforming dilapidated urban industrial spaces. In addition, few empirical studies have revealed the commonalities and differences of market entities employing both the top-down and bottom-up approaches during the redevelopment. In this article, a multilevel transformation, and the consequent spatial performance in three postindustrial lands in Beijing have been examined based on big data analysis, field observation and in-depth interviews. We reached three conclusions. First, in addition to the angle of participatory planning, the deep exploration of specific market entities as an intermediate joint among different parties in protecting and reusing the postindustrial lands uncovers a distinct perspective of urban revitalization. Second, the finding of the enterprises' role in mitigating the conflicts between bottom-up and top-down approaches further testifies to a less-dichotomous perspective in urban regeneration studies. Last, though the examination of the multilevel transformation mechanism reflecting the difference between state and nonstate enterprises in property-led management, a generic paradigm behind the trend of relying on enterprises in revitalizing the large-scale postindustrial lands is revealed.

The role of sirtuin1 in liver injury: molecular mechanisms and novel therapeutic target.

Liver disease is a common and serious threat to human health. The progression of liver diseases is influenced by many physiologic processes, including oxidative stress, inflammation, bile acid metabolism, and autophagy. Various factors lead to the dysfunction of these processes and basing on the different pathogeny, pathology, clinical manifestation, and pathogenesis, liver diseases are grouped into different categories. Specifically, Sirtuin1 (SIRT1), a member of the sirtuin protein family, has been extensively studied in the context of liver injury in recent years and are confirmed the significant role in liver disease. SIRT1 has been found to play a critical role in regulating key processes in liver injury. Further, SIRT1 seems to cause divers outcomes in different types of liver diseases. Recent studies have showed some therapeutic strategies involving modulating SIRT1, which may bring a novel therapeutic target. To elucidate the mechanisms underlying the role of sirtuin1 in liver injury and its potentiality as a therapeutic target, this review outlines the key signaling pathways associated with sirtuin1 and liver injury, and discusses recent advances in therapeutic strategies targeting sirtuin1 in liver diseases.

A novel mouse model carrying a gene trap insertion into the Hmgxb4 gene locus to examine Hmgxb4 expression in vivo.

HMG (high mobility group) proteins are a diverse family of nonhistone chromosomal proteins that interact with DNA and a wide range of transcriptional regulators to regulate the structural architecture of DNA. HMGXB4 (also known as HMG2L1) is an HMG protein family member that contains a single HMG box domain. Our previous studies have demonstrated that HMGXB4 suppresses smooth muscle differentiation and exacerbates endotoxemia by promoting a systemic inflammatory response in mice. However, the expression of Hmgxb4 in vivo has not fully examined. Herein, we generated a mouse model that harbors a gene trap in the form of a lacZ gene insertion into the Hmgxb4 gene. This mouse enables the visualization of endogenous HMGXB4 expression in different tissues via staining for the ß-galactosidase activity of LacZ which is under the control of the endogenous Hmgxb4 gene promoter. We found that HMGXB4 is widely expressed in mouse tissues and is a nuclear protein. Furthermore, the Hmgxb4 gene trap mice exhibit normal cardiac function and blood pressure. Measurement of ß-galactosidase activity in the Hmgxb4 gene trap mice demonstrated that the arterial injury significantly induces Hmgxb4 expression. In summary, the Hmgxb4 gene trap reporter mouse described here provides a valuable tool to examine the expression level of endogenous Hmgxb4 in both physiological and pathological settings in vivo.

Molecular mechanism of miRNA mediated biosynthesis of secondary metabolites in medicinal plants.

Plant secondary metabolites are important raw materials for the pharmaceutical industry, and their biosynthetic processes are subject to diverse and precise regulation by miRNA. The identification of miRNA molecules in medicinal plants and exploration of their mechanisms not only contribute to a deeper understanding of the molecular genetic mechanisms of plant growth, development and resistance to stress, but also provide a theoretical basis for elucidating the pharmacological effects of authentic medicinal materials and constructing bioreactors for the synthesis of medicinal secondary metabolite components. This paper summarizes the research reports on the discovery of miRNA in medicinal plants and their regulatory mechanisms on the synthesis of secondary metabolites by searching the relevant literature in public databases. It summarizes the currently discovered miRNA and their functions in medicinal plants, and summarizes the molecular mechanisms regulating the synthesis and degradation of secondary metabolites. Furthermore, it provides a prospect for the research and development of medicinal plant miRNA. The compiled information contributes to a comprehensive understanding of the research progress on miRNA in medicinal plants and provides a reference for the industrial development of related secondary metabolite biosynthesis.

MicroRNA-7 deficiency ameliorates d-galactose-induced aging in mice by regulating senescence of Kupffer cells.

Aging is intricately linked to immune system dysfunction. Recent studies have highlighted the biological function of microRNA-7 (miR-7) as a novel regulator of immune cell function and related diseases. However, the potential role of miR-7 in aging remains unexplored. Here, we investigated the contribution of miR-7 to d-gal-induced aging in mice, focusing on its regulation of senescent Kupffer cells. Our findings revealed that miR-7 deficiency significantly ameliorated the aging process, characterized by enhanced CD4+ T-cell activation. However, the adoptive transfer of miR-7-deficient CD4+ T cells failed to improve the age-related phenotype. Further analysis showed that miR-7 deficiency significantly reduced IL-1ß production in liver tissue, and inhibiting IL-1ß in vivo slowed down the aging process in mice. Notably, IL-1ß is mainly produced by senescent Kupffer cells in the liver tissue of aging mice, and miR-7 expression was significantly up-regulated in these cells. Mechanistically, KLF4, a target of miR-7, was down-regulated in senescent Kupffer cells in aging mouse model. Furthermore, miR-7 deficiency also modulated the NF-κB activation and IL-1ß production in senescent Kupffer cells through KLF4. In conclusion, our findings unveil the role of miR-7 in d-gal-induced aging in mice, highlighting its regulation of KLF4/NF-κB/IL-1ß pathways in senescent Kupffer cells. This research may enhance our understanding of miRNA-based aging immune cells and offer new avenues for new intervention strategies in aging process.

Mitochondrial respiratory chain component NDUFA4: a promising therapeutic target for gastrointestinal cancer.

Gastrointestinal cancer, one of the most common cancers, continues to be a major cause of mortality and morbidity globally. Accumulating evidence has shown that alterations in mitochondrial energy metabolism are involved in developing various clinical diseases. NADH dehydrogenase 1 alpha subcomplex 4 (NDUFA4), encoded by the NDUFA4 gene located on human chromosome 7p21.3, is a component of mitochondrial respiratory chain complex IV and integral to mitochondrial energy metabolism. Recent researchers have disclosed that NDUFA4 is implicated in the pathogenesis of various diseases, including gastrointestinal cancer. Aberrant expression of NDUFA4 leads to the alteration in mitochondrial energy metabolism, thereby regulating the growth and metastasis of cancer cells, indicating that it might be a new promising target for cancer intervention. This article comprehensively reviews the structure, regulatory mechanism, and biological function of NDUFA4. Of note, the expression and roles of NDUFA4 in gastrointestinal cancer including colorectal cancer, liver cancer, gastric cancer, and so on were discussed. Finally, the existing problems of NDUFA4-based intervention on gastrointestinal cancer are discussed to provide help to strengthen the understanding of the carcinogenesis of gastrointestinal cancer, as well as the development of new strategies for clinical intervention.

Subject-Independent Emotion Recognition Based on EEG Frequency Band Features and Self-Adaptive Graph Construction.

Emotion is one of the most important higher cognitive functions of the human brain and plays an important role in transaction processing and decisions. In traditional emotion recognition studies, the frequency band features in EEG signals have been shown to have a high correlation with emotion production. However, traditional emotion recognition methods cannot satisfactorily solve the problem of individual differences in subjects and data heterogeneity in EEG, and subject-independent emotion recognition based on EEG signals has attracted extensive attention from researchers. In this paper, we propose a subject-independent emotion recognition model based on adaptive extraction of layer structure based on frequency bands (BFE-Net), which is adaptive in extracting EEG map features through the multi-graphic layer construction module to obtain a frequency band-based multi-graphic layer emotion representation. To evaluate the performance of the model in subject-independent emotion recognition studies, extensive experiments are conducted on two public datasets including SEED and SEED-IV. The experimental results show that in most experimental settings, our model has a more advanced performance than the existing studies of the same type. In addition, the visualization of brain connectivity patterns reveals that some of the findings are consistent with previous neuroscientific validations, further validating the model in subject-independent emotion recognition studies.

Integrating image and gene-data with a semi-supervised attention model for prediction of KRAS gene mutation status in non-small cell lung cancer.

KRAS is a pathogenic gene frequently implicated in non-small cell lung cancer (NSCLC). However, biopsy as a diagnostic method has practical limitations. Therefore, it is important to accurately determine the mutation status of the KRAS gene non-invasively by combining NSCLC CT images and genetic data for early diagnosis and subsequent targeted therapy of patients. This paper proposes a Semi-supervised Multimodal Multiscale Attention Model (S2MMAM). S2MMAM comprises a Supervised Multilevel Fusion Segmentation Network (SMF-SN) and a Semi-supervised Multimodal Fusion Classification Network (S2MF-CN). S2MMAM facilitates the execution of the classification task by transferring the useful information captured in SMF-SN to the S2MF-CN to improve the model prediction accuracy. In SMF-SN, we propose a Triple Attention-guided Feature Aggregation module for obtaining segmentation features that incorporate high-level semantic abstract features and low-level semantic detail features. Segmentation features provide pre-guidance and key information expansion for S2MF-CN. S2MF-CN shares the encoder and decoder parameters of SMF-SN, which enables S2MF-CN to obtain rich classification features. S2MF-CN uses the proposed Intra and Inter Mutual Guidance Attention Fusion (I2MGAF) module to first guide segmentation and classification feature fusion to extract hidden multi-scale contextual information. I2MGAF then guides the multidimensional fusion of genetic data and CT image data to compensate for the lack of information in single modality data. S2MMAM achieved 83.27% AUC and 81.67% accuracy in predicting KRAS gene mutation status in NSCLC. This method uses medical image CT and genetic data to effectively improve the accuracy of predicting KRAS gene mutation status in NSCLC.

Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet.

An accurate ultra-short-term time series prediction of a power load is an important guarantee for power dispatching and the safe operation of power systems. Problems of the current ultra-short-term time series prediction algorithms include low prediction accuracy, difficulty capturing the local mutation features, poor stability, and others. From the perspective of series decomposition, a multi-scale sequence decomposition model (TFDNet) based on power spectral density and the Morlet wavelet transform is proposed that combines the multidimensional correlation feature fusion strategy in the time and frequency domains. By introducing the time-frequency energy selection module, the "prior knowledge" guidance module, and the sequence denoising decomposition module, the model not only effectively delineates the global trend and local seasonal features, completes the in-depth information mining of the smooth trend and fluctuating seasonal features, but more importantly, realizes the accurate capture of the local mutation seasonal features. Finally, on the premise of improving the forecasting accuracy, single-point load forecasting and quantile probabilistic load forecasting for ultra-short-term load forecasting are realized. Through the experiments conducted on three public datasets and one private dataset, the TFDNet model reduces the mean square error (MSE) and mean absolute error (MAE) by 19.80 and 11.20% on average, respectively, as compared with the benchmark method. These results indicate the potential applications of the TFDNet model.

Promoter A1312C mutation leads to microRNA-7 downregulation in human non-small cell lung cancer.

MicroRNA-7 (miRNA-7, miR-7) is a unique class of tumor suppressors, plays an important role in various physiological and pathological processes including human non-small cell lung cancer (NSCLC). In previous works, we revealed that miR-7 could regulate the growth and metastasis of human NSCLC cells. However, the mechanism of dysregulated miR-7 expression in NSCLC remains to be further elucidated. In this study, based on clinical sample analysis, we found that the downregulated expression of miR-7 was dominantly attributed to the decreased level of pri-miR-7-2 in human NSCLC. Furthermore, there were four site mutations in the miR-7-2 promoter sequence. Notably, among these four sites, mutation at -1312 locus (A â†’ C, termed as A1312C mutation) was dominate, and A1312C mutation further led to decreased expression of miR-7 in human NSCLC cells, accompanied with elevated transduction of NDUFA4/ERK/AKT signaling pathway. Mechanistically, homeobox A5 (HOXA5) is the key transcription factors regulating miR-7 expression in NSCLC. A1312C mutation impairs HOXA5 binding, thereby reducing the transcriptional activity of miR-7-2 promoter, resulting in downregulation of miR-7 expression. Together, these data may provide new insights into the dysregulation of specific miRNA expression in NSCLC and ultimately prove to be helpful in the diagnostic, prognostic, and therapeutic strategies against NSCLC.

Effective lung nodule detection using deep CNN with dual attention mechanisms.

Novel methods are required to enhance lung cancer detection, which has overtaken other cancer-related causes of death as the major cause of cancer-related mortality. Radiologists have long-standing methods for locating lung nodules in patients with lung cancer, such as computed tomography (CT) scans. Radiologists must manually review a significant amount of CT scan pictures, which makes the process time-consuming and prone to human error. Computer-aided diagnosis (CAD) systems have been created to help radiologists with their evaluations in order to overcome these difficulties. These systems make use of cutting-edge deep learning architectures. These CAD systems are designed to improve lung nodule diagnosis efficiency and accuracy. In this study, a bespoke convolutional neural network (CNN) with a dual attention mechanism was created, which was especially crafted to concentrate on the most important elements in images of lung nodules. The CNN model extracts informative features from the images, while the attention module incorporates both channel attention and spatial attention mechanisms to selectively highlight significant features. After the attention module, global average pooling is applied to summarize the spatial information. To evaluate the performance of the proposed model, extensive experiments were conducted using benchmark dataset of lung nodules. The results of these experiments demonstrated that our model surpasses recent models and achieves state-of-the-art accuracy in lung nodule detection and classification tasks.

Decoding the complexity of on-target integration: characterizing DNA insertions at the CRISPR-Cas9 targeted locus using nanopore sequencing.

BACKGROUND: CRISPR-Cas9 technology has advanced in vivo gene therapy for disorders like hemophilia A, notably through the successful targeted incorporation of the F8 gene into the Alb locus in hepatocytes, effectively curing this disorder in mice. However, thoroughly evaluating the safety and specificity of this therapy is essential. Our study introduces a novel methodology to analyze complex insertion sequences at the on-target edited locus, utilizing barcoded long-range PCR, CRISPR RNP-mediated deletion of unedited alleles, magnetic bead-based long amplicon enrichment, and nanopore sequencing. RESULTS: We identified the expected F8 insertions and various fragment combinations resulting from the in vivo linearization of the double-cut plasmid donor. Notably, our research is the first to document insertions exceeding ten kbp. We also found that a small proportion of these insertions were derived from sources other than donor plasmids, including Cas9-sgRNA plasmids, genomic DNA fragments, and LINE-1 elements. CONCLUSIONS: Our study presents a robust method for analyzing the complexity of on-target editing, particularly for in vivo long insertions, where donor template integration can be challenging. This work offers a new tool for quality control in gene editing outcomes and underscores the importance of detailed characterization of edited genomic sequences. Our findings have significant implications for enhancing the safety and effectiveness of CRISPR-Cas9 gene therapy in treating various disorders, including hemophilia A.

Members of the class Candidatus Ordosarchaeia imply an alternative evolutionary scenario from methanogens to haloarchaea.

The origin of methanogenesis can be traced to the common ancestor of non-DPANN archaea, whereas haloarchaea (or Halobacteria) are believed to have evolved from a methanogenic ancestor through multiple evolutionary events. However, due to the accelerated evolution and compositional bias of proteins adapting to hypersaline habitats, Halobacteria exhibit substantial evolutionary divergence from methanogens, and the identification of the closest methanogen (either Methanonatronarchaeia or other taxa) to Halobacteria remains a subject of debate. Here, we obtained five metagenome-assembled genomes with high completeness from soda-saline lakes on the Ordos Plateau in Inner Mongolia, China, and we proposed the name Candidatus Ordosarchaeia for this novel class. Phylogenetic analyses revealed that Ca. Ordosarchaeia is firmly positioned near the median position between the Methanonatronarchaeia and Halobacteria-Hikarchaeia lineages. Functional predictions supported the transitional status of Ca. Ordosarchaeia with the metabolic potential of nonmethanogenic and aerobic chemoheterotrophy, as did remnants of the gene sequences of methylamine/dimethylamine/trimethylamine metabolism and coenzyme M biosynthesis. Based on the similarity of the methyl-coenzyme M reductase genes mcrBGADC in Methanonatronarchaeia with the phylogenetically distant methanogens, an alternative evolutionary scenario is proposed, in which Methanonatronarchaeia, Ca. Ordosarchaeia, Ca. Hikarchaeia, and Halobacteria share a common ancestor that initially lost mcr genes. However, certain members of Methanonatronarchaeia subsequently acquired mcr genes through horizontal gene transfer from distantly related methanogens. This hypothesis is supported by amalgamated likelihood estimation, phylogenetic analysis, and gene arrangement patterns. Altogether, Ca. Ordosarchaeia genomes clarify the sisterhood of Methanonatronarchaeia with Halobacteria and provide new insights into the evolution from methanogens to haloarchaea.

Targeting lipid metabolism of macrophages: A new strategy for tumor therapy.

BACKGROUND: Lipid metabolism has been implicated in a variety of normal cellular processes and strongly related to the development of multiple diseases, including tumor. Tumor-associated macrophage (TAM) has emerged as a crucial regulator in tumorigenesis and promising target for tumor treatment. AIM OF REVIEW: A thorough understanding of TAM lipid metabolism and its value in tumorigenesis may provide new ideas for TAM-based anti-tumor therapy. Key scientific concepts of review: TAMs can be divided into two main types, M1-like TAMs and M2-like TAMs, which play anti-tumor and pro-tumor functions in tumor occurrence and development, respectively. Accumulating evidence has shown that lipid metabolic reprogramming, including fatty acid uptake and utilization, cholesterol expulsion, controls the polarization of TAMs and affects the tumorgenesis. These advances in uncovering the intricacies of lipid metabolism and TAMs have yielded new insights on tumor development and treatment. In this review, we aim to provide an update on the current understanding of the lipid metabolic reprogramming made by TAMs to adapt to the harsh tumor microenvironment (TME). In particular, we emphasize that there is complex lipid metabolism connections between TAMs and distinct tumors, which influences TAM to bias from M1 to M2 phenotype in tumor progression, and ultimately promotes tumor occurrence and development. Finally, we discuss the existing issues on therapeutic strategies by reprogramming TAMs based on lipid metabolism regulation (or increasing the ratio of M1/M2-like TAMs) that could be applied in the future to clinical tumor treatment.

Two-stream vision transformer based multi-label recognition for TCM prescriptions construction.

Traditional Chinese medicine (TCM) observation diagnosis images (including facial and tongue images) provide essential human body information, holding significant importance in clinical medicine for diagnosis and treatment. TCM prescriptions, known for their simplicity, non-invasiveness, and low side effects, have been widely applied worldwide. Exploring automated herbal prescription construction based on visual diagnosis holds vital value in delving into the correlation between external features and herbal prescriptions and offering medical services in mobile healthcare systems. To effectively integrate multi-perspective visual diagnosis images and automate prescription construction, this study proposes a multi-herb recommendation framework based on Visual Transformer and multi-label classification. The framework comprises three key components: image encoder, label embedding module, and cross-modal fusion classification module. The image encoder employs a dual-stream Visual Transformer to learn dependencies between different regions of input images, capturing both local and global features. The label embedding module utilizes Graph Convolutional Networks to capture associations between diverse herbal labels. Finally, two Multi-Modal Factorized Bilinear modules are introduced as effective components to fuse cross-modal vectors, creating an end-to-end multi-label image-herb recommendation model. Through experimentation with real facial and tongue images and generating prescription data closely resembling real samples. The precision is 50.06 %, the recall rate is 48.33 %, and the F1-score is 49.18 %. This study validates the feasibility of automated herbal prescription construction from the perspective of visual diagnosis. Simultaneously, it provides valuable insights for constructing herbal prescriptions automatically from more physical information.

Extraction, purification, structural characteristics and biological properties of the polysaccharides from Armillaria mellea (Vahl) P. Kumm.: A review.

Armillaria mellea (Vahl) P. Kumm. is a well-known homoeopathic plant with medicinal and culinary uses. Modern phytochemical researchers have successfully extracted and purified over 40 types of A. mellea polysaccharides (AMPs) from the fruiting bodies, hyphae and fermentation broth of A. mellea, and some of them have been analyzed and identified by their chemical structures. The impressive biological activity of these polysaccharides has been recognized by scientists worldwide. Many studies show that AMPs have remarkable antioxidant, anti-diabetic, anti-tumor, anti-inflammatory, immunoregulatory, hypolipidemic, thrombectomy, anti-aging, pulmonary protective, hepatic protective, anti-Alzheimer's properties, etc. However, the current understanding of the relationships between their chemical structure and biological activity, toxicological effects and pharmacokinetics remains limited. This article provides a systematic review of the research conducted over the past decades on the extraction and purification methods, structural characteristics, biological activity and mechanism of action of AMPs. The aim is to provide a research base that will benefit the future application of AMPs as therapeutic drugs and functional foods, and also provide insights for the further development of AMPs.

The Establishment and Optimization of a Chicken Primordial Germ Cell Induction Model Using Small-Molecule Compounds.

In recent years, inducing pluripotent stem cells to differentiate into functional primordial germ cells (PGCs) in vitro has become an important method of obtaining a large number of PGCs. However, the instability and low induction efficiency of the in vitro PGC induction system restrict the application of PGCs in transgenic animal production, germplasm resource conservation and other fields. In this study, we successfully established a two-step induction model of chicken PGCs in vitro, which significantly improved the formation efficiency of PGC-like cells (PGCLCs). To further improve the PGC formation efficiency in vitro, 5025 differentially expressed genes (DEGs) were obtained between embryonic stem cells (ESCs) and PGCs through RNA-seq. GO and KEGG enrichment analysis revealed that signaling pathways such as BMP4, Wnt and Notch were significantly activated during PGC formation, similar to other species. In addition, we noted that cAMP was activated during PGC formation, while MAPK was suppressed. Based on the results of our analysis, we found that the PGC formation efficiency was significantly improved after activating Wnt and inhibiting MAPK, and was lower than after activating cAMP. To sum up, in this study, we successfully established a two-step induction model of chicken PGCs in vitro with high PGC formation efficiency, which lays a theoretical foundation for further demonstrating the regulatory mechanism of PGCs and realizing their specific applications.