Explore the vast range of titles available.

Background N6-methyladenosine (m6A) modification, the most abundant internal methylation of eukaryotic RNA transcripts, is critically implicated in RNA processing. As the largest known component in the m6A methyltransferase complex, KIAA1429 plays a vital role in m6A methylation. However, its function and mechanism in hepatocellular carcinoma (HCC) remain poorly defined. Methods Quantitative PCR, western blot and immunohistochemistry were used to measure the expression of KIAA1429 in HCC. The effects of KIAA1429 on the malignant phenotypes of hepatoma cells were examined in vitro and in vivo. MeRIP-seq, RIP-seq and RNA-seq were performed to identify the target genes of KIAA1429. Results KIAA1429 was considerably upregulated in HCC tissues. High expression of KIAA1429 was associated with poor prognosis among HCC patients. Silencing KIAA1429 suppressed cell proliferation and metastasis in vitro and in vivo. GATA3 was identified as the direct downstream target of KIAA1429-mediated m6A modification. KIAA1429 induced m6A methylation on the 3′ UTR of GATA3 pre-mRNA, leading to the separation of the RNA-binding protein HuR and the degradation of GATA3 pre-mRNA. Strikingly, a long noncoding RNA (lncRNA) GATA3-AS, transcribed from the antisense strand of the GATA3 gene, functioned as a cis -acting element for the preferential interaction of KIAA1429 with GATA3 pre-mRNA. Accordingly, we found that the tumor growth and metastasis driven by KIAA1429 or GATA3-AS were mediated by GATA3. Conclusion Our study proposed a complex KIAA1429-GATA3 regulatory model based on m6A modification and provided insights into the epi-transcriptomic dysregulation in hepatocarcinogenesis and metastasis.

Metabolic reprogramming of amino acids has been recognized as a significant characteristic in various types of cancers. Numerous studies have indicated that the metabolic reprogramming of amino acids in tumors significantly supports certain malignant behaviors, including tumor proliferation, survival, invasion, and even immune escape. Amino acids can provide biomolecules such as nucleotides and glutathione (GSH) for tumors, and the bioavailability of amino acids influences tumor progression. Meanwhile, as essential metabolites, amino acids are closely associated with immune cell activation and can contribute to tumor immune processes by modulating the function of immune cells. Thus, targeting amino acids metabolism has emerged as a promising therapeutic strategy. Herein, we provide an overview of the effects of amino acids on the central carbon cycle and autophagy. We also provide an in-depth review of potential therapies for cancer treatment associated with amino acids, including metabolic enzymes of amino acids, dietary therapy of amino acids, and so on. Furthermore, we summarize some current nano-systems relevant to amino acids. This review aims to offer a theoretical foundation for understanding amino acids metabolism in cancer and identifying potential therapeutic strategies.

ObjectiveThe aim of this study was to develop quantitative feature-based models from histopathological images to distinguish hepatocellular carcinoma (HCC) from adjacent normal tissue and predict the prognosis of HCC patients after surgical resection.MethodsA fully automated pipeline was constructed using computational approaches to analyze the quantitative features of histopathological slides of HCC patients, in which the features were extracted from the hematoxylin and eosin (H&E)-stained whole-slide images of HCC patients from The Cancer Genome Atlas and tissue microarray images from West China Hospital. The extracted features were used to train the statistical models that classify tissue slides and predict patients’ survival outcomes by machine-learning methods.ResultsA total of 1733 quantitative image features were extracted from each histopathological slide. The diagnostic classifier based on 31 features was able to successfully distinguish HCC from adjacent normal tissues in both the test [area under the receiver operating characteristic curve (AUC) 0.988] and external validation sets (AUC 0.886). The random-forest prognostic model using 46 features was able to significantly stratify patients in each set into longer- or shorter-term survival groups according to their assigned risk scores. Moreover, the prognostic model we constructed showed comparable predicting accuracy as TNM staging systems in predicting patients’ survival at different time points after surgery.ConclusionsOur findings suggest that machine-learning models derived from image features can assist clinicians in HCC diagnosis and its prognosis prediction after hepatectomy.

Metastasis is one of the main contributors to the poor prognosis of hepatocellular carcinoma (HCC). However, the underlying mechanism of HCC metastasis remains largely unknown. Here, we showed that TXNDC12, a thioredoxin-like protein, was upregulated in highly metastatic HCC cell lines as well as in portal vein tumor thrombus and lung metastasis tissues of HCC patients. We found that the enforced expression of TXNDC12 promoted metastasis both in vitro and in vivo. Subsequent mechanistic investigations revealed that TXNDC12 promoted metastasis through upregulation of the ZEB1-mediated epithelial–mesenchymal transition (EMT) process. We subsequently showed that TXNDC12 overexpression stimulated the nuclear translocation and activation of β-catenin, a positive transcriptional regulator of ZEB1. Accordingly, we found that TXNDC12 interacted with β-catenin and that the thioredoxin-like domain of TXNDC12 was essential for the interaction between TXNDC12 and β-catenin as well as for TXNDC12-mediated β-catenin activation. Moreover, high levels of TXNDC12 in clinical HCC tissues correlated with elevated nuclear β-catenin levels and predicted worse overall and disease-free survival. In summary, our study demonstrated that TXNDC12 could activate β-catenin via protein–protein interaction and promote ZEB1-mediated EMT and HCC metastasis.

The liver has unique regeneration potential, which ensures the continuous dependence of the human body on hepatic functions. As the composition and function of gut microbiota has been gradually elucidated, the vital role of gut microbiota in liver regeneration through gut-liver axis has recently been accepted. In the process of liver regeneration, gut microbiota composition is changed. Moreover, gut microbiota can contribute to the regulation of the liver immune microenvironment, thereby modulating the release of inflammatory factors including IL-6, TNF-α, HGF, IFN-γ and TGF-β, which involve in different phases of liver regeneration. And previous research have demonstrated that through enterohepatic circulation, bile acids (BAs), lipopolysaccharide, short-chain fatty acids and other metabolites of gut microbiota associate with liver and may promote liver regeneration through various pathways. In this perspective, by summarizing gut microbiota-derived signaling pathways that promote liver regeneration, we unveil the role of gut microbiota in liver regeneration and provide feasible strategies to promote liver regeneration by altering gut microbiota composition.

Background Long non-coding RNAs (lncRNAs) have been demonstrated to play vital roles in cancer development and progression. However, their biological roles and function mechanisms in liver cancer remain largely unknown. Methods RNA-seq was performed with clinical hepatoma tissues and paired adjacent normal liver tissues to identify differentially expressed lncRNAs. qPCR was utilized to examine the expression levels of lncRNAs. We studied the function of TLNC1 in cell growth and metastasis of hepatoma with both cell and mouse models. RNA-seq, RNA pull-down coupled with mass spectrometry, RNA immunoprecipitation, dual luciferase reporter assay, and surface plasmon resonance analysis were used to analyze the functional mechanism of TLNC1. Results Based on the intersection of our own RNA-seq, TCGA RNA-seq, and TCGA survival analysis data, TLNC1 was identified as a potential tumorigenic lncRNA of liver cancer. TLNC1 significantly enhanced the growth and metastasis of hepatoma cells both in vitro and in vivo. TLNC1 exerted its tumorigenic function through interaction with TPR and inducing the TPR-mediated transportation of p53 from nucleus to cytoplasm, thus repressing the transcription of p53 target genes and finally contributing to the progression of liver cancer. Conclusions TLNC1 is a promising prognostic factor of liver cancer, and the TLNC1-TPR-p53 axis can serve as a potential therapeutic target for hepatoma treatment.

Liver repair and regeneration are crucial physiological responses to hepatic injury and are orchestrated through intricate cellular and molecular networks. This review systematically delineates advancements in the field, emphasizing the essential roles played by diverse liver cell types. Their coordinated actions, supported by complex crosstalk within the liver microenvironment, are pivotal to enhancing regenerative outcomes. Recent molecular investigations have elucidated key signaling pathways involved in liver injury and regeneration. Viewed through the lens of metabolic reprogramming, these pathways highlight how shifts in glucose, lipid, and amino acid metabolism support the cellular functions essential for liver repair and regeneration. An analysis of regenerative variability across pathological states reveals how disease conditions influence these dynamics, guiding the development of novel therapeutic strategies and advanced techniques to enhance liver repair and regeneration. Bridging laboratory findings with practical applications, recent clinical trials highlight the potential of optimizing liver regeneration strategies. These trials offer valuable insights into the effectiveness of novel therapies and underscore significant progress in translational research. In conclusion, this review intricately links molecular insights to therapeutic frontiers, systematically charting the trajectory from fundamental physiological mechanisms to innovative clinical applications in liver repair and regeneration.

The tumor microenvironment (TME) has been demonstrated to modulate the biological behavior of tumors intensively. Multiple stress conditions are widely observed in the TME of many cancer types, such as hypoxia, inflammation, and nutrient deprivation. Recently, accumulating evidence demonstrates that the expression levels of noncoding RNAs (ncRNAs) are dramatically altered by TME stress, and the dysregulated ncRNAs can in turn regulate tumor cell proliferation, metastasis, and drug resistance. In this review, we elaborate on the signal transduction pathways or epigenetic pathways by which hypoxia-inducible factors (HIFs), inflammatory factors, and nutrient deprivation in TME regulate ncRNAs, and highlight the pivotal roles of TME stress-related ncRNAs in tumors. This helps to clarify the molecular regulatory networks between TME and ncRNAs, which may provide potential targets for cancer therapy.

Background Considerable evidence shows that circular RNAs (circRNAs) play an important role in tumor development. However, their function in intrahepatic cholangiocarcinoma (ICC) metastasis and the underlying mechanisms are incompletely understood. Methods circNFIB (hsa_circ_0086376, termed as cNFIB hereafter) was identified in human ICC tissues through circRNAs sequencing. The biological role of cNFIB was determined in vitro and in vivo by gain or loss of functional experiments. Fluorescence in situ hybridization (FISH), RNA immunoprecipitation (RIP) and RNA pull-down assays were conducted to analyze the interaction of cNFIB with dual specificity mitogen-activated protein kinase kinase1 (MEK1). Duolink in situ proximity ligation assay (PLA) and coimmunoprecipitation (co-IP) assay were used to investigate the effects of cNFIB on the interaction between MEK1 and mitogen-activated protein kinase 2 (ERK2). Finally, a series of in vitro and in vivo experiments were performed to explore the influences of cNFIB on the anti-tumor activity of trametinib (a MEK inhibitor). Results cNFIB was significantly down-regulated in human ICC tissues with postoperative metastases. The loss of cNFIB was highly associated with aggressive characteristics and predicted unfavorable prognosis in ICC patients. Functional studies revealed that cNFIB inhibited the proliferation and metastasis of ICC cells in vitro and in vivo. Mechanistically, cNFIB competitively interacted with MEK1, which induced the dissociation between MEK1 and ERK2, thereby resulting in the suppression of ERK signaling and tumor metastasis. Moreover, we found that ICC cells with high levels of cNFIB held the potential to delay the trametinib resistance. Consistently, in vivo and in vitro studies demonstrated that cotreatment with trametinib and lentivirus vector encoding cNFIB showed greater inhibitory effect than isolated trametinib treatment. Conclusions Our findings identified that cNFIB played a key role in ICC growth and metastasis by regulating MEK1/ERK signaling. Given the efficacy of cNFIB modulation on ICC suppression and trametinib sensitivity, cNFIB appears to be a potential therapeutic molecule for ICC treatment.

The visual–inertial odometry (VIO) system is not robust enough in long time operation. Especially, the visual–inertial and Global Navigation Satellite System (GNSS) coupled system is prone to dispersion of system position information in case of failure of visual information or GNSS information. To address the above problems, this paper proposes a tightly coupled nonlinear optimized localization system of RGBD visual, inertial measurement unit (IMU), and global position (GDVI-Fusion) to solve the problems of insufficient robustness of carrier position estimation and inaccurate localization information in environments where visual information or GNSS information fails. The preprocessing of depth information in the initialization process is proposed to solve the influence of an RGBD camera by lighting and physical structure and to improve the accuracy of the depth information of image feature points so as to improve the robustness of the localization system. Based on the K-Nearest-Neighbors (KNN) algorithm, to process the feature points, the matching points construct the best geometric constraints and eliminate the feature matching points with an abnormal length and slope of the matching line, which improves the rapidity and accuracy of the feature point matching, resulting in the improvement of the system’s localization accuracy. The lightweight monocular GDVI-Fusion system proposed in this paper achieves a 54.2% improvement in operational efficiency and a 37.1% improvement in positioning accuracy compared with the GVINS system. We have verified the system’s operational efficiency and positioning accuracy using a public dataset and on a prototype.