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Zinc finger proteins are transcription factors with the finger domain, which plays a significant role in gene regulation. As the largest family of transcription factors in the human genome, zinc finger (ZNF) proteins are characterized by their different DNA binding motifs, such as C2H2 and Gag knuckle. Different kinds of zinc finger motifs exhibit a wide variety of biological functions. Zinc finger proteins have been reported in various diseases, especially in several cancers. Hepatocellular carcinoma (HCC) is the third leading cause of cancer-associated death worldwide, especially in China. Most of HCC patients have suffered from hepatitis B virus (HBV) and hepatitis C virus (HCV) injection for a long time. Although the surgical operation of HCC has been extremely developed, the prognosis of HCC is still very poor, and the underlying mechanisms in HCC tumorigenesis are still not completely understood. Here, we summarize multiple functions and recent research of zinc finger proteins in HCC tumorigenesis and progression. We also discuss the significance of zinc finger proteins in HCC diagnosis and prognostic evaluation.

Discoidin domain receptor 1 (DDR1) is a member of the receptor tyrosine kinase family, and its ligand is collagen. Previous studies demonstrated that DDR1 is highly expressed in many tumors. However, its role in hepatocellular carcinoma (HCC) remains obscure. In this study, we found that DDR1 was upregulated in HCC tissues, and the expression of DDR1 in TNM stage II-IV was higher than that in TNM stage I in HCC tissues, and high DDR1 expression was associated with poor prognosis. Gene expression analysis showed that DDR1 target genes were functionally involved in HCC metastasis. DDR1 positively regulated the migration and invasion of HCC cells and promoted lung metastasis. Human Phospho-Kinase Array showed that DDR1 activated ERK/MAPK signaling pathway. Mechanically, DDR1 interacted with ARF6 and activated ARF6 through recruiting PSD4. The kinase activity of DDR1 was required for ARF6 activation and its role in metastasis. High expression of PSD4 was associated with poor prognosis in HCC. In summary, our findings indicate that DDR1 promotes HCC metastasis through collagen induced DDR1 signaling mediated PSD4/ARF6 signaling, suggesting that DDR1 and ARF6 may serve as novel prognostic biomarkers and therapeutic targets for metastatic HCC.

To explore the differences of vaginal microbes in women with preterm birth (PTB), and to construct prediction model. We searched for articles related to vaginal microbiology in preterm women and obtained four 16S rRNA-sequence datasets. We analyzed that for species diversity and differences, and constructed a random forest model with 20 differential genera. We introduce an independent whole genome-sequencing (WGS) data for validation. In addition, we collected vaginal and cervical swabs from 33 pregnant women who delivered spontaneously full-term and preterm infants, performed WGS in our lab to further validate the model. Compared to term birth (TB) samples, PTB women vagina were characterized by a decrease in Firmicutes , Lactobacillus , and an increase in diversity accompanied by the colonization of pathogenic bacteria such as Gardnerella , Atopobium and Prevotella . Twenty genus markers, including Lactobacillus , Prevotella , Streptococcus , and Gardnerella performed well in predicting PTB, with study-to-study transfer validation and LODO validation, different gestation validation showing good results, and in two independent cohorts (external WGS cohorts and woman samples WGS cohorts) in which the accuracy was maintained. PTB women have unique vaginal microbiota characteristics. A predictive model of PTB was constructed and its value validated from multiple perspectives. Importance This study integrates current data on the vaginal microbiome of women with preterm birth to comprehensively investigate the unique vaginal microbiome of women with preterm birth. A preterm birth risk prediction model based on 20 characteristic genera was constructed, and its effectiveness was verified by internal, external, and collected whole genome sequencing data.

Background Circular RNAs (circRNAs) function as crucial regulators in multiple cancers, including hepatocellular carcinoma (HCC). However, the roles of circRNAs in HCC remains largely unknown. Methods circTOLLIP was identified in HCC by screening of two public circRNA microarray datasets and detected in HCC cells and tissues through quantitative real-time PCR (qRT–PCR) and in situ hybridization (ISH). Gain- and loss-of-function assays were performed to confirm the biological effects of circTOLLIP on HCC in vitro and in vivo. Mechanistically, bioinformatics analysis of online databases, MS2-RNA pulldown, biotin-labeled circTOLLIP/miR-516a-5p RNA pulldown, RNA immunoprecipitation (RIP), luciferase reporter assay, fluorescence in situ hybridization assay (FISH) and RNA sequencing were used to confirm the regulation of Eukaryotic initiation factor 4A3 (EIF4A3) on circTOLLIP and the interaction among circTOLLIP, miR-516a-5p and PBX homeobox 3 (PBX3). Results circTOLLIP was significantly upregulated in HCC cells and tissues. High circTOLLIP expression was correlated with poor overall survival (OS) and disease-free survival (DFS) in patients. circTOLLIP promoted the proliferation and metastasis of HCC cells in vitro and in vivo. Mechanistically, EIF4A3 promoted the biogenesis of circTOLLIP without affecting its stability. Moreover, circTOLLIP sponged miR-516a-5p to elevate the expression of PBX3, thereby activating the epithelial-to-mesenchymal transition (EMT) pathway and facilitating tumor progression in HCC. Conclusions Our findings indicate that EIF4A3-induced circTOLLIP promotes the progression of HCC through the circTOLLIP/miR-516a-5p/PBX3/EMT axis.

The relationship between dysbiosis of the gastrointestinal microbiota and gastric cancer (GC) has been extensively studied. However, microbiota alterations in GC patients vary widely across studies, and reproducible diagnostic biomarkers for early GC are still lacking in multiple populations. Thus, this study aimed to characterize the gastrointestinal microbial communities involved in gastric carcinogenesis through a meta-analysis of multiple published and open datasets. We analyzed 16S rRNA sequencing data from 1,642 gastric biopsy samples and 394 stool samples across 11 independent studies. VSEARCH, QIIME and R packages such as vegan, phyloseq, cooccur, and random forest were used for data processing and analysis. PICRUSt software was employed to predict functions. The α-diversity results indicated significant differences in the intratumoral microbiota of cancer patients compared to non-cancer patients, while no significant differences were observed in the fecal microbiota. Network analysis showed that the positive correlation with GC-enriched bacteria increased, and the positive correlation with GC-depleted bacteria decreased compared to healthy individuals. Functional analyses indicated that pathways related to carbohydrate metabolism were significantly enriched in GC, while biosynthesis of unsaturated fatty acids was diminished. Additionally, we investigated non- commensals, which are crucial in both -negative and -positive GC. Random forest models, constructed using specific taxa associated with GC identified from the LEfSe analysis, revealed that the combination of Lactobacillus and Streptococcus included alone could effectively discriminate between GC patients and healthy individuals in fecal samples (area under the curve (AUC) = 0.7949). This finding was also validated in an independent cohort (AUC = 0.7712). This study examined the intratumoral and fecal microbiota of GC patients from a dual microecological perspective and identified and as intratumoral and intestinal-specific co-differential bacteria. Furthermore, it confirmed the validity of the combination of and as GC-specific microbial markers across multiple populations, which may aid in the early non-invasive diagnosis of GC.

A growing body of evidence indicates that the dysbiosis of both mammary and intestinal microbiota is associated with the initiation and progression of breast tumors. However, the microbial characteristics of patients with breast tumors vary widely across studies, and replicable biomarkers for early-stage breast tumor diagnosis remain elusive. We demonstrate a machine learning-based method for the analysis of breast tissue and gut microbial differences among patients with benign breast disease, patients with breast cancer (BC), and healthy individuals using 16S rRNA sequence data retrieved from eight studies. QIIME 2.0 and R software (version 3.6.1) were used for consistent processing. A naive Bayes classifier was trained on the RDP v16 reference database to assign taxonomy using the Vsearch software. After re-analyzing with a total of 768 breast tissue samples and 1,311 fecal samples, we confirmed that and were the most representative genera of BC tissue. are frequently and significantly enriched in the intestines of patients with breast tumor. The areas under the curve (AUCs) of random forest models were 74.27% and 68.08% for breast carcinoma tissues and stool samples, respectively. The model was validated for effectiveness cohort-to-cohort transfer (average AUC =0.65) and leave-one-cohort-out (average AUC = 0.66). The same BC-associated biomarker exists in the tissues and the gut. The results of the experiments showed that the -specific-related metabolite deoxycholic acid (DCA) promotes the proliferation of HER2-positive BC cells and stimulates G0/G1 phase cells to enter the S phase, which may be related to the activation of peptide-O-fucosyltransferase activity functions and the neuroactive ligand-receptor interaction pathway. The results of this study will improve our understanding of the microbial profile of breast tumors. Changes in the microbial population may be present in both the tissues and the gut of patients with BC, and specific markers could aid in the early diagnosis of BC. The findings from experiments confirmed that -specific metabolite DCA promotes the proliferation of BC cells. We propose the use of stool-based biomarkers in clinical application as a non-invasive and convenient diagnostic method.

Background The relationship between commensal microbiota and lung cancer (LC) has been studied extensively. However, developing replicable microbiological markers for early LC diagnosis across multiple populations has remained challenging. Current studies are limited to a single region, single LC subtype, and small sample size. Therefore, we aimed to perform the first large‐scale meta‐analysis for identifying micro biomarkers for LC screening by integrating gut and respiratory samples from multiple studies and building a machine‐learning classifier. Methods In total, 712 gut and 393 respiratory samples were assessed via 16 s rRNA amplicon sequencing. After identifying the taxa of differential biomarkers, we established random forest models to distinguish between LC populations and normal controls. We validated the robustness and specificity of the model using external cohorts. Moreover, we also used the KEGG database for the predictive analysis of colony‐related functions. Results The α and β diversity indices indicated that LC patients' gut microbiota (GM) and lung microbiota (LM) differed significantly from those of the healthy population. Linear discriminant analysis (LDA) of effect size (LEfSe) helped us identify the top‐ranked biomarkers, Enterococcus, Lactobacillus, and Escherichia, in two microbial niches. The area under the curve values of the diagnostic model for the two sites were 0.81 and 0.90, respectively. KEGG enrichment analysis also revealed significant differences in microbiota‐associated functions between cancer‐affected and healthy individuals that were primarily associated with metabolic disturbances. Conclusions GM and LM profiles were significantly altered in LC patients, compared to healthy individuals. We identified the taxa of biomarkers at the two loci and constructed accurate diagnostic models. This study demonstrates the effectiveness of LC‐specific microbiological markers in multiple populations and contributes to the early diagnosis and screening of LC.

Protein kinases contribute to hepatocellular carcinoma (HCC) development and immune evasion, posing major challenges for HCC management. Here we show STE20/SPS1-related proline/alanine-rich kinase (SPAK) as a candidate immune exhaustion–associated gene identified through a pooled screen of protein kinases. By integrating bioinformatic analyses, data from patient cohorts, and functional studies in mouse models and cell lines, we demonstrate that elevated expression of SPAK promotes HCC progression, enhances stemness, drives immune exhaustion, and contributes to resistance to targeted therapies. Mechanistically, SPAK phosphorylates GSK3β at Ser9, thereby inhibiting proteasome-mediated degradation of c-Jun and PD-L1. Additionally, we find that DNMT3B-dependent intragenic methylation of SPAK contributes to its high expression in HCC. Notably, the SPAK inhibitor exhibits potent inhibitory effects and synergizes with PD-1 blockade to enhance antitumor efficacy. In summary, these findings establish SPAK as a driver of oncogenesis and immune exhaustion in HCC and highlight dual inhibition as a potential therapeutic strategy. Protein kinases contribute to hepatocellular carcinoma progression and immune evasion. This study identifies SPAK as a key driver of oncogenesis and immune exhaustion, showing that SPAK inhibition synergizes with anti-PD-1 therapy to enhance antitumor efficacy.

The transmembrane and coiled‐coil domains 3 (TMCO3) are highly expressed in many tumors. However, the underlying mechanisms governing the way in which TMCO3 affects the progression of hepatocellular carcinoma (HCC) remain unclear. This study screens out the molecule TMCO3 with high N6‐methyladenosine (m6A) modification level in tumor samples compared to the adjacent non‐cancerous tissues of three pairs of HCC patients through Methylated RNA Immunoprecipitation Sequencing (MeRIP‐seq) and RNA sequencing (RNA‐seq). Subsequently, the oncogenic effect of TMCO3 in HCC is verified through in vivo and in vitro experiments. AlkB Homolog 5 (ALKBH5), an m6A demethylase of TMCO3 is then screened out. The following experiments demonstrate that TMCO3 can activate AKT directly through the Phosphatidylinositol‐3–Kinase (PI3K) pathway, thus promoting the progression of HCC. Meanwhile, the phosphorylation site on TMCO3: the 85th amino acid‐serine, and mutation of this site can directly impair the activity and membrane translocation of AKT is found. Finally, the carcinogenic effect of TMCO3 is further elucidated in HCC through the orthotopic treatment model and the hydrodynamic tail vein injection treatment model. The findings can provide a potential target for targeted AKT treatment in patients with HCC and verify a possible prognostic marker in HCC. TMCO3 as a downstream target of ALKBH5 is found, which regulates its expression in an m6A‐dependent manner, and then a new phosphorylation site on TMCO3 is identified and determines the specific mechanism of TMCO3 with AKT. Moreover, the combined treatment of targeting the TMCO3 and inhibition of AKT in mice models with HCC achieves great results.

Advanced metastasis of hepatocellular carcinoma (HCC) significantly contributes to high death rates among patients. The efficiency of targeted therapies and chemotherapeutic agents shows individual variability. Therefore, there is no effective treatment for advanced HCC. Zinc finger proteins (ZFPs) are known to be crucial in various tumors, especially on HCC. In our study, we verified that ZFP41 could suppress the progression and metastasis of HCC through in vitro and in vivo experiments. During the past years, N6‐methyladenine (m6A) regulation has also been increasingly reported in HCC. To investigate whether ZFP41 could be regulated via m6A methylation, our results showed that YTHDF3 bound to the mRNA of ZFP41 and degrade it. Subsequently, to further elucidate the function of ZFP41, we identified Snail, a well‐known oncogenic molecule, through RNA‐seq. As a canonical component in the epithelial‐to‐mesenchymal transition (EMT) pathway, Snail plays a pivotal role and is a critical marker for tumor invasion and metastasis. Our results showed ZFP41 could inhibit Snail and the EMT pathway through its transcriptional repression. In conclusion, our study revealed that ZFP41 is a potential prognostic element for patients with HCC, and targeting ZFP41 might be used for translational clinical applications as a promising therapeutic target. In normal liver cells, YTHDF3 shows low expression, leading to a reduction in the m6A modification of ZFP41. This results in increased synthesis of ZFP41 mRNA and an enhanced transcriptional inhibitory effect on Snail. In contrast, in HCC cells, the expression of YTHDF3 is significantly elevated, which enhances the m6A modification of ZFP41. This promotes degradation of ZFP41 mRNA, consequently weakening the transcriptional inhibitory effect on Snail. Ultimately, this facilitates activation of the EMT pathway and promotes proliferation and metastasis of HCC.