NEDD4 lactylation promotes APAP induced liver injury through Caspase11 dependent non-canonical pyroptosis.

Caspase-11 detection of intracellular lipopolysaccharide mediates non-canonical pyroptosis, which could result in inflammatory damage and organ lesions in various diseases such as sepsis. Our research found that lactate from the microenvironment of acetaminophen-induced acute liver injury increased Caspase-11 levels, enhanced gasdermin D activation and accelerated macrophage pyroptosis, which lead to exacerbation of liver injury. Further experiments unveiled that lactate inhibits Caspase-11 ubiquitination by reducing its binding to NEDD4, a negative regulator of Caspase-11. We also identified that lactates regulated NEDD4 K33 lactylation, which inhibits protein interactions between Caspase-11 and NEDD4. Moreover, restraining lactylation reduces non-canonical pyroptosis in macrophages and ameliorates liver injury. Our work links lactate to the exquisite regulation of the non-canonical inflammasome, and provides a basis for targeting lactylation signaling to combat Caspase-11-mediated non-canonical pyroptosis and acetaminophen-induced liver injury.

Identification of hepatocellular carcinoma subtypes based on PcG-related genes and biological relevance with cancer cells.

BACKGROUND: Hepatocellular carcinoma (HCC) is an extensive heterogeneous disease where epigenetic factors contribute to its pathogenesis. Polycomb group (PcG) proteins are a group of subunits constituting various macro-molecular machines to regulate the epigenetic landscape, which contributes to cancer phenotype and has the potential to develop a molecular classification of HCC. RESULTS: Here, based on multi-omics data analysis of DNA methylation, mRNA expression, and copy number of PcG-related genes, we established an epigenetic classification system of HCC, which divides the HCC patients into two subgroups with significantly different outcomes. Comparing these two epigenetic subgroups, we identified different metabolic features, which were related to epigenetic regulation of polycomb-repressive complex 1/2 (PRC1/2). Furthermore, we experimentally proved that inhibition of PcG complexes enhanced the lipid metabolism and reduced the capacity of HCC cells against glucose shortage. In addition, we validated the low chemotherapy sensitivity of HCC in Group A and found inhibition of PRC1/2 promoted HCC cells' sensitivity to oxaliplatin in vitro and in vivo. Finally, we found that aberrant upregulation of CBX2 in Group A and upregulation of CBX2 were associated with poor prognosis in HCC patients. Furthermore, we found that manipulation of CBX2 affected the levels of H3K27me3 and H2AK119ub. CONTRIBUTIONS: Our study provided a novel molecular classification system based on PcG-related genes data and experimentally validated the biological features of HCC in two subgroups. Our founding supported the polycomb complex targeting strategy to inhibit HCC progression where CBX2 could be a feasible therapeutic target.

The HN1/HMGB1 axis promotes the proliferation and metastasis of hepatocellular carcinoma and attenuates the chemosensitivity to oxaliplatin.

Hematological and neurological expressed 1 (HN1) is closely associated with the proliferation and metastasis of various tumors. However, the physiological functions and clinical significance of HN1 in hepatocellular carcinoma (HCC) remain indistinct. In this study, we investigated the role of HN1 in the pathogenesis of HCC and the underlying mechanism using clinical data from HCC patients, in vitro experiments utilizing HCC cell lines and in vivo animal models. We demonstrated that the overexpressed HN1 in HCC was correlated with patients' adverse outcomes. The gain and loss of function experiments indicated that HN1 could promote the proliferation, migration, and invasion of HCC cells in vitro. Furthermore, we found that HN1 knockdown sensitized HCC cells to oxaliplatin. Mechanically, HN1 prevented HMGB1 protein from ubiquitination and degradation via the autophagy-lysosome pathway, which was related to the interaction between HN1 protein and TRIM28 protein. In the nucleus, the downregulation of HMGB1 followed by HN1 knockdown resulted in increased DNA damage and cell death in the oxaliplatin-treated HCC cells. In the cytoplasm, HN1 regulated autophagy via HMGB1. Furthermore, HN1 knockdown in combination with HMGB1 overexpression restored the aggressive phenotypes of HCC cells and the sensitivity of these cells to oxaliplatin. HN1 knockdown inhibited the tumor growth and metastasis, and promoted the anticancer efficiency of oxaliplatin in vivo. In conclusion, our data suggest that the HN1/HMGB1 axis plays an important role in the development/progression and chemotherapy of HCC. Our findings indicate that the HN1/HMGB1 axis may be a promising therapeutic target for HCC treatment.

Development of an Early Warning Model for Predicting the Death Risk of Coronavirus Disease 2019 Based on Data Immediately Available on Admission.

Introduction: COVID-19 has overloaded worldwide medical facilities, leaving some potentially high-risk patients trapped in outpatient clinics without sufficient treatment. However, there is still a lack of a simple and effective tool to identify these patients early. Methods: A retrospective cohort study was conducted to develop an early warning model for predicting the death risk of COVID-19. Seventy-five percent of the cases were used to construct the prediction model, and the remaining 25% were used to verify the prediction model based on data immediately available on admission. Results: From March 1, 2020, to April 16, 2020, a total of 4,711 COVID-19 patients were included in our study. The average age was 63.37 ± 16.70 years, of which 1,148 (24.37%) died. Finally, age, SpO2, body temperature (T), and mean arterial pressure (MAP) were selected for constructing the model by univariate analysis, multivariate analysis, and a review of the literature. We used five common methods for constructing the model and finally found that the full model had the best specificity and higher accuracy. The area under the ROC curve (AUC), specificity, sensitivity, and accuracy of full model in train cohort were, respectively, 0.798 (0.779, 0.816), 0.804, 0.656, and 0.768, and in the validation cohort were, respectively, 0.783 (0.751, 0.815), 0.800, 0.616, and 0.755. Visualization tools of the prediction model included a nomogram and an online dynamic nomogram (https://wanghai.shinyapps.io/dynnomapp/). Conclusion: We developed a prediction model that might aid in the early identification of COVID-19 patients with a high probability of mortality on admission. However, further research is required to determine whether this tool can be applied for outpatient or home-based COVID-19 patients.

Cellular senescence in hepatocellular carcinoma induced by a long non-coding RNA-encoded peptide PINT87aa by blocking FOXM1-mediated PHB2.

Rationale: Recently, long non-coding RNAs (lncRNAs), known to be involved in human cancer progression, have been shown to encode peptides with biological functions, but the role of lncRNA-encoded peptides in cellular senescence is largely unexplored. We previously reported the tumor-suppressive role of PINT87aa, a peptide encoded by the long intergenic non-protein coding RNA, p53 induced transcript (LINC-PINT). Here, we investigated PINT87aa's role in hepatocellular carcinoma (HCC) cellular senescence. Methods: We examined PINT87aa and truncated PINT87aa functions in vitro by monitoring cell proliferation and performed flow cytometry, senescence-associated ß-galactosidase staining, JC-1 staining indicative of mitochondrial membrane potential, the ratio of the overlapping area of light chain 3 beta (LC3B) and mitochondrial probes and the ratio of lysosomal associated membrane protein 1 (LAMP1) overlapping with cytochrome c oxidase subunit 4I1 (COXIV) denoting mitophagy. PINT87aa and truncated PINT87aa functions in vivo were verified by subcutaneously transplanted tumors in nude mice. The possible binding between PINT87aa and forkhead box M1 (FOXM1) was predicted through structural analysis and verified by co-immunoprecipitation and immunofluorescence co-localization. Rescue experiments were performed in vivo and in vitro following FOXM1 overexpression. Further, chromatin immunoprecipitation, polymerase chain reaction, and dual-luciferase reporter gene assay were conducted to validate FOXM1 binding to the prohibitin 2 (PHB2) promoter. Results: PINT87aa was significantly increased in the hydrogen peroxide-induced HCC cell senescence model. Overexpression of PINT87aa induced growth inhibition, cellular senescence, and decreased mitophagy in vitro and in vivo. In contrast, FOXM1 gain-of-function could partially reduce the proportion of senescent HCC cells and enhance mitophagy. PINT87aa overexpression did not affect the expression of FOXM1 itself but reduced that of its target genes involved in cell cycle and proliferation, especially PHB2, which was involved in mitophagy and transcribed by FOXM1. Structural analysis indicated that PINT87aa could bind to the DNA-binding domain of FOXM1, which was confirmed by co-immunoprecipitation and immunofluorescence co-localization. Furthermore, we demonstrated that the 2 to 39 amino acid truncated form of the peptide exerted effects similarly to the full form. Conclusion: Our study established the role of PINT87aa as a novel biomarker and a key regulator of cellular senescence in HCC and identified PINT87aa as a potential therapeutic target for HCC.

Seven-senescence-associated gene signature predicts overall survival for Asian patients with hepatocellular carcinoma.

BACKGROUND: Cellular senescence is a recognized barrier for progression of chronic liver diseases to hepatocellular carcinoma (HCC). The expression of a cluster of genes is altered in response to environmental factors during senescence. However, it is questionable whether these genes could serve as biomarkers for HCC patients. AIM: To develop a signature of senescence-associated genes (SAGs) that predicts patients' overall survival (OS) to improve prognosis prediction of HCC. METHODS: SAGs were identified using two senescent cell models. Univariate COX regression analysis was performed to screen the candidate genes significantly associated with OS of HCC in a discovery cohort (GSE14520) for the least absolute shrinkage and selection operator modelling. Prognostic value of this seven-gene signature was evaluated using two independent cohorts retrieved from the GEO (GSE14520) and the Cancer Genome Atlas datasets, respectively. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to compare the predictive accuracy of the seven-SAG signature and serum α-fetoprotein (AFP). RESULTS: A total of 42 SAGs were screened and seven of them, including KIF18B, CEP55, CIT, MCM7, CDC45, EZH2, and MCM5, were used to construct a prognostic formula. All seven genes were significantly downregulated in senescent cells and upregulated in HCC tissues. Survival analysis indicated that our seven-SAG signature was strongly associated with OS, especially in Asian populations, both in discovery and validation cohorts. Moreover, time-dependent ROC curve analysis suggested the seven-gene signature had a better predictive accuracy than serum AFP in predicting HCC patients' 1-, 3-, and 5-year OS. CONCLUSION: We developed a seven-SAG signature, which could predict OS of Asian HCC patients. This risk model provides new clinical evidence for the accurate diagnosis and targeted treatment of HCC.

IDH1 as a frequently mutated gene has potential effect on exosomes releasement by epigenetically regulating P2RX7 in intrahepatic cholangiocarcinoma.

Biliary tract cancers (BTCs) was heterogeneous and characterized by late diagnosis and fatal outcome. To identify new biomarkers for BTCs, we performed Robust Rank Aggreg (RRA) analysis and identified that IDH1 mutation was common in ICC, while IDH1R132C was the most frequent type. Moreover, we identified P2RX7 and other 45 genes as downregulated genes with hypermethylation in IDH1R132C mutated cells. The WGCNA results predicted that P2RX7 could influence cholangiocarcinoma by exosomes related manners. Finally, we confirmed that P2RX7 was downregulated in IDH1R132C mutated cells as well as the expression of CD9 and CD81 by experiments. In conclusion, IDH1R132C mutation was relatively prevalent in ICC. P2RX7 might be a potential downstream gene and it might be related to exosomes releasement.

Hypomethylated gene NRP1 is co-expressed with PDGFRB and associated with poor overall survival in gastric cancer patients.

Gastric cancer (GC) has been an increasingly serious problem in public health. However, there is still a lack of efficient approach to diagnosis and treatment in time, especially in the field of targeted therapy. Increasing evidences demonstrated that DNA methylation plays an essential role in tumorigenesis and progression of GC. Thus the present study aims to identify DNA methylation-based prognostic biomarkers in GC. Two methylation array datasets (GSE25869 and GSE30601) and RNA-seq based gene profiling dataset (TCGA-STAD) were employed for exploring candidate DNA methylation-based biomarkers. Univariate Cox regression analysis was used to select the most efficient prognostic genes in GC patients. Weighted gene correlation network analysis (WGCNA) was performed to screen the cluster of co-expressed genes. As a result, our data proved that NRP1 was a hypomethylated / upregulated gene in GC tissues, and PDGFRB was strongly co-expressed with it. Both of them were significantly associated with the overall survival of patients. More importantly, high expression levels of NRP1 and PDGFRB were associated with malignant phenotypes in GC patients, including Laurén histological diffuse type and higher histological grade. Patients carrying high expression level of NRP1 and PDGFRB had a nearly two-fold increased death risk than others. In summary, the hypomethylated gene, NRP1, and its co-expressed gene, PDGFRB, were significantly correlated with tumor malignant phenotypes, which might serve as potential prognostic biomarkers for GC patients.

Three hypomethylated genes were associated with poor overall survival in pancreatic cancer patients.

Pancreatic cancer (PC) is a highly malignant cancer with poor prognosis and high mortality. Aberrant DNA methylation plays a critical role in the occurrence, progression and prognosis of malignant tumors. In this study, we employed multiple datasets from APGI, TCGA and GEO to perform Multi-Omics analysis, including DNA methylation and expression profiling analysis. Three differentially expressed genes (SULT1E1, IGF2BP3, MAP4K4) with altered status of DNA methylation were identified and then enrolled into prognostic risk score model using LASSO regression. Univariate cox regression analysis indicated that high risk score was significantly associated with poor prognosis. Multivariate cox regression analysis proved the risk score was an independent prognostic factor for PC. In addition, time-dependent ROC curves indicated good performance of our model in predicting the 1-, 3- and 5-year survival of PC patients. Besides, stratified survival analysis revealed that the risk score model had greater prognostic value for patients of late stage with T3/T4 and N+. Pathway enrichment analysis suggested that these three genes might promote tumor progression by affecting signaling by Rho GTPases and chromosome segregation. In summary, three hypomethylated gene signature were significantly associated with patients' overall survival, which might serve as potential prognostic biomarkers for PC patients.

Six-long non-coding RNA signature predicts recurrence-free survival in hepatocellular carcinoma.

BACKGROUND: Recent evidence shows that long non-coding RNAs (lncRNAs) are closely related to hepatogenesis and a few aggressive features of hepatocellular carcinoma (HCC). Increasing studies demonstrate that lncRNAs are potential prognostic factors for HCC. Moreover, several studies reported the combination of lncRNAs for predicting the overall survival (OS) of HCC, but the results varied. Thus, more effort including more accurate statistical approaches is needed for exploring the prognostic value of lncRNAs in HCC. AIM: To develop a robust lncRNA signature associated with HCC recurrence to improve prognosis prediction of HCC. METHODS: Univariate COX regression analysis was performed to screen the lncRNAs significantly associated with recurrence-free survival (RFS) of HCC in GSE76427 for the least absolute shrinkage and selection operator (LASSO) modelling. The established lncRNA signature was validated and developed in The Cancer Genome Atlas (TCGA) series using Kaplan-Meier curves. The expression values of the identified lncRNAs were compared between the tumor and non-tumor tissues. Pathway enrichment of these lncRNAs was conducted based on the significantly co-expressed genes. A prognostic nomogram combining the lncRNA signature and clinical characteristics was constructed. RESULTS: The lncRNA signature consisted of six lncRNAs: MSC-AS1, POLR2J4, EIF3J-AS1, SERHL, RMST, and PVT1. This risk model was significantly associated with the RFS of HCC in the TCGA cohort with a hazard ratio (HR) being 1.807 (95%CI [confidence interval]: 1.329-2.457) and log-rank P-value being less than 0.001. The best candidates of the six-lncRNA signature were younger male patients with HBV infection in relatively early tumor-stage and better physical condition but with higher preoperative alpha-fetoprotein. All the lncRNAs were significantly upregulated in tumor samples compared to non-tumor samples (P < 0.05). The most significantly enriched pathways of the lncRNAs were TGF-ß signaling pathway, cellular apoptosis-associated pathways, etc. The nomogram showed great utility of the lncRNA signature in HCC recurrence risk stratification. CONCLUSION: We have constructed a six-lncRNA signature for prognosis prediction of HCC. This risk model provides new clinical evidence for the accurate diagnosis and targeted treatment of HCC.

Combined eight-long noncoding RNA signature: a new risk score predicting prognosis in elderly non-small cell lung cancer patients.

The elderly are the majority of patients with non-small cell lung cancer (NSCLC). Compared to the overall population's predictive guidance, an effective predictive guidance for elderly patients can better guide patients' postoperative treatment and improve overall survival (OS) and disease-free survival (DFS). Recently, the long non-coding RNAs (lncRNAs) have been found to play an important role in predicting tumor prognosis. To identify potential lncRNAs to predict survival in elderly patients with NSCLC, in the present study, we chose 456 elderly patients with NSCLC and analyzed differentially expressed lncRNAs from four Gene Expression Omnibus (GEO) datasets (GSE30219, GSE31546, GSE37745 and GSE50081). We then constructed an eight-lncRNA formula to predict elderly patients' prognosis in NSCLC. Furthermore, we validated the prognostic values of the new risk model in two independent datasets, TCGA (n=670) and GSE31210 (n=130). Our data suggested a significant association between risk model and patients' prognosis. Finally, stratification analysis further revealed the eight-lncRNA signature was an independent factor to predict OS and DFS in stage I elderly patients from both the discovery and validation groups. Functional prediction revealed that 8 lncRNAs have potential effects on tumor immune processes such as lymphocyte activation and TNF production in NSCLC. In summary, our data provides evidence that the eight-lncRNA signature could serve as an independent biomarker to predict prognosis in elderly patients with NSCLC especially in elderly stage I patients.

A new risk score based on twelve hepatocellular carcinoma-specific gene expression can predict the patients' prognosis.

A large panel of molecular biomarkers have been identified to predict the prognosis of hepatocellular carcinoma (HCC), yet with limited clinical application due to difficult extrapolation. We here generated a genetic risk score system comprised of 12 HCC-specific genes to better predict the prognosis of HCC patients. Four genomics profiling datasets (GSE5851, GSE28691, GSE15765 and GSE14323) were searched to seek HCC-specific genes by comparisons between cancer samples and normal liver tissues and between different subtypes of hepatic neoplasms. Univariate survival analysis screened HCC-specific genes associated with overall survival (OS) in the training dataset for next-step risk model construction. The prognostic value of the constructed HCC risk score system was then validated in the TCGA dataset. Stratified analysis indicated this scoring system showed better performance in elderly male patients with HBV infection and preoperative lower levels of creatinine, alpha-fetoprotein and platelet and higher level of albumin. Functional annotation of this risk model in high-risk patients revealed that pathways associated with cell cycle, cell migration and inflammation were significantly enriched. In summary, our constructed HCC-specific gene risk model demonstrated robustness and potentiality in predicting the prognosis of HCC patients, especially among elderly male patients with HBV infection and relatively better general conditions.

A seven-long noncoding RNA signature predicts overall survival for patients with early stage non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the most common cancer and cause of cancer-related mortality globally. Increasing evidence suggested that the long non-coding RNAs (lncRNAs) were involved in cancer-related death. To explore the possible prognostic lncRNA biomarkers for NSCLC patients, in the present study, we conducted a comprehensive lncRNA profiling analysis based on 1902 patients from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets. In the discovery phase, we employed 682 patients from the combination of four GEO datasets (GSE30219, GSE31546, GSE33745 and GSE50081) and conducted a seven-lncRNA formula to predict overall survival (OS). Next, we validated our risk-score formula in two independent datasets, TCGA (n=994) and GSE31210 (n=226). Stratified analysis revealed that the seven-lncRNA signature was significantly associated with OS in stage I patients from both discovery and validation groups (all P<0.001). Additionally, the prognostic value of the seven-lncRNA signature was also found to be favorable in patients carrying wild-type KRAS or EGFR. Bioinformatical analysis suggested that the seven-lncRNA signature affected patients' prognosis by influencing cell cycle-related pathways. In summary, our findings revealed a seven-lncRNA signature that predicted OS of NSCLC patients, especially in those with early tumor stage and carrying wild-type KRAS or EGFR.

A three-long non-coding RNA-expression-based risk score system can better predict both overall and recurrence-free survival in patients with small hepatocellular carcinoma.

Growing evidence indicates that long non-coding RNAs (lncRNAs) may be potential biomarkers and therapeutic targets for many disease conditions, including cancer. In this study, we constructed a risk score system of three lncRNAs (LOC101927051, LINC00667 and NSUN5P2) for predicting the prognosis of small hepatocellular carcinoma (sHCC) (maximum tumor diameter ≤5 cm). The prognostic value of this sHCC risk model was confirmed in TCGA HCC samples (TNM stage I and II). Stratified survival analysis revealed that the suitable patient groups of the sHCC lncRNA-signature included HBV-infected and cirrhotic patients with better physical conditions yet lower levels of albumin and higher levels of alpha-fetoprotein preoperatively. Besides, Asian patients with no family history of HCC or history of alcohol consumption can be predicted more precisely. Molecular functional analysis indicated that PYK2 pathway was significantly enriched in the high-risk patients. Pathway enrichment analysis indicated that the two lncRNAs (LINC00667 and NSUN5P2) associated with poor prognosis were closely related to cell cycle. The nomogram based on the lncRNA-signature for RFS prediction in sHCC patients exhibited good performance in recurrence risk stratification. In conclusion, we identified a novel three-lncRNA-expression-based risk model for predicting the prognosis of sHCC.