Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a major cause of cancer-related death due to early metastasis or recurrence. Tumor angiogenesis plays an essential role in the tumorigenesis of HCC. Accumulated studies have validated the crucial role of lncRNAs in tumor angiogenesis. Here, we established an angiogenesis-related multi-lncRNAs risk model based on the machine learning for HCC prognosis prediction. Firstly, a total of 348 differential expression angiogenesis-related lncRNAs were identified by correlation analysis. Then, 20 of these lncRNAs were selected through univariate cox analysis and used for in-depth study of machine learning. After 1,000 random sampling cycles calculating by random forest algorithm, four lncRNAs were found to be highly associated with HCC prognosis, namely LUCAT1, AC010761.1, AC006504.7 and MIR210HG. Subsequently, the results from both the training and validation sets revealed that the four lncRNAs-based risk model was suitable for predicting HCC recurrence. Moreover, the infiltration of macrophages and CD8 T cells were shown to be closely associated with risk score and promotion of immune escape. The reliability of this model was validated by exploring the biological functions of lncRNA MIR210HG in HCC cells. The results showed that MIR210HG silence inhibited HCC growth and migration through upregulating PFKFB4 and SPAG4. Taken together, this angiogenesis-related risk model could serve as a reliable and promising tool to predict the prognosis of HCC.

Novel Prognostic Signatures of Hepatocellular Carcinoma Based on Metabolic Pathway Phenotypes.

Hepatocellular carcinoma is a disastrous cancer with an aberrant metabolism. In this study, we aimed to assess the role of metabolism in the prognosis of hepatocellular carcinoma. Ten metabolism-related pathways were identified to classify the hepatocellular carcinoma into two clusters: Metabolism_H and Metabolism_L. Compared with Metabolism_L, patients in Metabolism_H had lower survival rates with more mutated TP53 genes and more immune infiltration. Moreover, risk scores for predicting overall survival based on eleven differentially expressed metabolic genes were developed by the least absolute shrinkage and selection operator (LASSO)-Cox regression model in The Cancer Genome Atlas (TCGA) dataset, which was validated in the International Cancer Genome Consortium (ICGC) dataset. The immunohistochemistry staining of liver cancer patient specimens also identified that the 11 genes were associated with the prognosis of liver cancer patients. Multivariate Cox regression analyses indicated that the differentially expressed metabolic gene-based risk score was also an independent prognostic factor for overall survival. Furthermore, the risk score (AUC = 0.767) outperformed other clinical variables in predicting overall survival. Therefore, the metabolism-related survival-predictor model may predict overall survival excellently for HCC patients.

Autophagy-Related Signatures as Prognostic Indicators for Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is the most common and deadly type of liver cancer. Autophagy is the process of transporting damaged or aging cellular components into lysosomes for digestion and degradation. Accumulating evidence implies that autophagy is a key factor in tumor progression. The aim of this study was to determine a panel of novel autophagy-related prognostic markers for liver cancer. Methods: We conducted a comprehensive analysis of autophagy-related gene (ARG) expression profiles and corresponding clinical information based on The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The univariate Cox proportional regression model was used to screen candidate autophagy-related prognostic genes. In addition, a multivariate Cox proportional regression model was used to identify five key prognostic autophagy-related genes (ATIC, BAX, BIRC5, CAPNS1, and FKBP1A), which were used to construct a prognostic signature. Real-time qPCR analysis was used to evaluate the expression levels of ARGs in 20 surgically resected HCC samples and matched tumor-adjacent normal tissue samples. In addition, the effect of FKBP1A on autophagy and tumor progression was determined by performing in vitro and in vivo experiments. Results: Based on the prognostic signature, patients with liver cancer were significantly divided into high-risk and low-risk groups in terms of overall survival (OS). A subsequent multivariate Cox regression analysis indicated that the prognostic signature remained an independent prognostic factor for OS. The prognostic signature possessing a better area under the curve (AUC) displayed better performance in predicting the survival of patients with HCC than other clinical parameters. Furthermore, FKBP1A was overexpressed in HCC tissues, and knockdown of FKBP1A impaired cell proliferation, migration, and invasion through the PI3K/AKT/mTOR signaling pathway. Conclusion: This study provides a prospective biomarker for monitoring outcomes of patients with HCC.

VX-765 prevents intestinal ischemia-reperfusion injury by inhibiting NLRP3 inflammasome.

BACKGROUND: Intestinal ischemia-reperfusion injury (IIRI) is a common clinical event that can cause serious consequences. The study aimed to investigated the effect of VX-765 in IIRI and its mechanism. METHODS: The hypoxia-reoxygenation (H/R) cell model and IIRI mouse model were generated to examine the in vitro and in vivo effects of VX-765 on IIRI. IIRI was evaluated by histological assessment. ELISA was performed to determine the levels of IL-6, TNF-α, IL-1ß, caspase-1, and GSDMD in intestinal tissues as well as the levels of MDA, SOD, CAT, caspase-1, and GSDMD in Caco-2 cells. Relative protein levels of NLRP3, ASC, IL-18, IL-1ß, cleaved Caspase1, and GSDMD-N were analyzed by Western blotting. CCK-8 Assay was conducted to determine the optimal concentration of VX-765 for the in vitro studies. Flow cytometry, fluorescence microscopy and real-time PCR (RT-PCR) were used to assess ROS levels and the mRNA levels of IL-18 and IL-1ß, respectively. Immunofluorescence staining was performed to examine the subcellular localization of P65 and NLRP3. RESULTS: VX-765 reduced IIRI-induced oxidative stress and inflammatory response both in vivo and in vitro, while it decreased the levels of TNF-α, IL-6, IL-1ß as well as the modified Park/Chiu scores. The optimal concentration of VX-765 for the in vitro studies was 10 µM. Moreover, VX-765 inhibited the nuclear translocation of P65, reduced oxidative stress and down-regulated the activation of NLRP3 inflammasome. CONCLUSION: VX-765 prevents IIRI presumably by inhibiting the activation of NLRP3 inflammasome.

Development and validation of a ferroptosis-related model for three digestive tract tumors based on a pan-cancer analysis.

Aims: To develop a ferroptosis gene-based survival-predictor model for predicting the prognosis of patients with digestive tract tumors, a pan-caner analysis was performed. Materials & methods: Based on unsupervised clustering and the expression levels of ferroptosis genes, patients with cancer were divided into two clusters. The least absolute shrinkage and selection operator method Cox regression analysis was used to establish the survival-predictor model. Results: Based on the pan-cancer analysis, a 20 gene-based survival-predictor model for predicting survival rates was developed, which was validated in patients with hepatocellular carcinoma. Conclusion: The survival-predictor model accurately predicted the prognosis of patients with digestive tract tumors.

Expression pattern and prognostic value of circadian clock genes in pancreatic adenocarcinoma.

Accumulating studies indicate that circadian clock genes are pivotal regulators of tumorigenesis and development of various cancers. Nevertheless, their implications in pancreatic adenocarcinoma (PAAD) remain poorly characterized. We investigated the expression pattern of circadian clock genes and evaluated their prognostic values in PAAD. Firstly, we systematically analyzed data from The Cancer Genome Atlas (TCGA) database pertaining to patient clinical information and gene expression data. We found that 19 of 20 circadian clock genes showed significantly different expression levels in comparisons between PAAD and normal tissues. In addition, 10 circadian clock genes with regression coefficients were selected to construct a new risk signature, which was then identified as an independent prognostic factor for PAAD. Mechanistically, circadian clock genes in PAAD may impact the basic state of cells and the composition of tumor-infiltrating immune cells, thus affecting disease prognosis. Finally, we construct a novel prognostic nomogram on the basis of histological nodes and risk score to precisely predict prognosis of patients with PAAD. In conclusion, our study uncovered the important role of circadian clock genes in PAAD and developed a risk signature as a promising prognostic biomarker for patients with PAAD.

Development and validation of the trans-omics model for pancreatic adenocarcinoma.

Aim: To develop a trans-omics-based molecular clinicopathological algorithm for predicting pancreatic adenocarcinoma prognosis, we performed a comprehensive analysis of the expression levels of mRNA, DNA methylation and DNA copy number in The Cancer Genome Atlas dataset. Materials & methods: Based on the least absolute shrinkage and selection operator method - COX regression analysis, a trans-omics-based classifier was established to predict overall survival. Nomogram was constructed by combining the classifier band clinical pathological characterization. Results: Based on trans-omics, we developed a 10-gene-based classifier and a molecular-clinicopathologic nomogram for predicting overall survival with satisfactory accuracy. Conclusion: Trans-omics-based classifier and molecule-clinicopathological nomogram based on the classifier can accurately predict the prognosis of pancreatic adenocarcinoma patients.

Highly sensitive detection of carcinoembryonic antigen using copper-free click chemistry on the surface of azide cofunctionalized graphene oxide.

In this study, we reported a highly sensitive method for detecting carcinoembryonic antigen (CEA) based on an azide cofunctionalized graphene oxide (GO-N3) and carbon dot (CDs) biosensor system. Carbon dots-labeled DNA (CDs-DNA) combined with GO-N3 using copper-free click chemistry (CFCC), which quenched the fluorescence of the CDs via fluorescence resonance energy transfer (FRET). Upon the addition of CEA, fluorescence was recovered due to the combination of CEA and aptamer. Under optimal conditions, the relative fluorescence intensity was linear with CEA concentration in the range of 0.01-1 ng/mL (R2 = 0.9788), and the limit of detection (LOD) was 7.32 pg/mL (S/N = 3). This biosensor had a high sensitivity and good selectivity for CEA detection in serum samples, indicating that the novel sensor platform holds a great potential for CEA and other biomarkers in practical applications.

A novel molecular-clinicopathologic nomogram to improve prognosis prediction of hepatocellular carcinoma.

BACKGROUND: Emerging evidence suggests that long non-coding RNA (lncRNA) plays a crucial part in the development and progress of hepatocellular carcinoma (HCC). The objective was to develop novel molecular-clinicopathological prediction methods for overall survival (OS) and recurrence of HCC. RESULTS: An 8-lncRNA-based classifier for OS and a 14-lncRNA-based classifier for recurrence were developed by LASSO COX regression analysis, both of which had high accuracy. The tdROC of OS-nomogram and recurrence-nomogram indicates the satisfactory accuracy and predictive power. The classifiers and nomograms for predicting OS and recurrence of HCC were validated in the Test and GEO cohorts. CONCLUSIONS: These two lncRNA-based classifiers could be independent prognostic factors for OS and recurrence. The molecule-clinicopathological nomograms based on the classifiers could increase the prognostic value. METHODS: HCC lncRNA expression profiles from the cancer genome atlas (TCGA) were randomly divided into 1:1 training and test cohorts. Based on least absolute shrinkage and selection operator method (LASSO) COX regression model, lncRNA-based classifiers were established to predict OS and recurrence, respectively. OS-nomogram and recurrence-nomogram were developed by combining lncRNA-based classifiers and clinicopathological characterization to predict OS and recurrence, respectively. The prognostic value was accessed by the time-dependent receiver operating characteristic (tdROC) and the concordance index (C-index).

Correction to: Identification of a competing endogenous RNA network associated with prognosis of pancreatic adenocarcinoma.

[This corrects the article DOI: 10.1186/s12935-020-01243-6.].

Identification of a competing endogenous RNA network associated with prognosis of pancreatic adenocarcinoma.

BACKGROUND: Emerging evidence suggests that competing endogenous RNAs plays a crucial role in the development and progress of pancreatic adenocarcinoma (PAAD). The objective was to identify a new lncRNA-miRNA-mRNA network as prognostic markers, and develop and validate a multi-mRNAs-based classifier for predicting overall survival (OS) in PAAD. METHODS: Data on pancreatic RNA expression and clinical information of 445 PAAD patients and 328 normal subjects were downloaded from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Genotype-Tissue Expression (GTEx). The weighted correlation network analysis (WGCNA) was used to analyze long non-coding RNA (lncRNA) and mRNA, clustering genes with similar expression patterns. MiRcode was used to predict the sponge microRNAs (miRNAs) corresponding to lncRNAs. The downstream targeted mRNAs of miRNAs were identified by starBase, miRDB, miRTarBase and Targetscan. A multi-mRNAs-based classifier was develop using least absolute shrinkage and selection operator method (LASSO) COX regression model, which was tested in an independent validation cohort. RESULTS: A lncRNA-miRNA-mRNA co-expression network which consisted of 60 lncRNAs, 3 miRNAs and 3 mRNAs associated with the prognosis of patients with PAAD was established. In addition, we constructed a 14-mRNAs-based classifier based on a training cohort composed of 178 PAAD patients, of which the area under receiver operating characteristic (AUC) in predicting 1-year, 3-year, and 5-year OS was 0.719, 0.806 and 0.794, respectively. The classifier also shown good prediction function in independent verification cohorts, with the AUC of 0.604, 0.639 and 0.607, respectively. CONCLUSIONS: A novel competitive endogenous RNA (ceRNA) network associated with progression of PAAD could be used as a reference for future molecular biology research.

Lipoxin A4 Ameliorates Acute Pancreatitis-Associated Acute Lung Injury through the Antioxidative and Anti-Inflammatory Effects of the Nrf2 Pathway.

Acute lung injury (ALI) is a critical event involved in the pathophysiological process of acute pancreatitis (AP). Many methods have been widely used for the treatment of AP-ALI, but few are useful during early inflammation. Lipoxin A4 (LXA4), a potent available anti-inflammatory and novel antioxidant mediator, has been extensively studied in AP-ALI, but its underlying mechanism as a protective mediator is not clear. This research was conducted to identify the possible targets and mechanisms involved in the anti-AP-ALI effect of LXA4. First, we confirmed that LXA4 strongly inhibited AP-ALI in mice. Next, using ELISA, PCR, and fluorescence detection to evaluate different parameters, LXA4 was shown to reduce the inflammatory cytokine production induced by AP and block reactive oxygen species (ROS) generation in vivo and in vitro. In addition, TNF-α treatment activated the nuclear factor E2-related factor 2 (Nrf2) signaling pathway and its downstream gene heme oxygenase-1 (HO-1) in human pulmonary microvascular endothelial cells (HPMECs), and LXA4 further promoted their expression. This study also provided evidence that LXA4 phosphorylates Ser40 and triggers its nuclear translocation to activate Nrf2. Moreover, when Nrf2-knockout (Nrf2-/-) mice and cells were used to further assess the effect of the Nrf2/HO-1 pathway, we found that Nrf2 expression knockdown partially eliminated the effect of LXA4 on the reductions in inflammatory factor levels while abrogating the inhibitory effect of LXA4 on the ROS generation stimulated by AP-ALI. Overall, LXA4 attenuated the resolution of AP-induced inflammation and ROS generation to mitigate ALI, perhaps by modulating the Nrf2/HO-1 pathway. These findings have laid a foundation for the treatment of AP-ALI.

The DNA methylation profile of non-coding RNAs improves prognosis prediction for pancreatic adenocarcinoma.

BACKGROUND: Compelling lines of evidence indicate that DNA methylation of non-coding RNAs (ncRNAs) plays critical roles in various tumour progression. In addition, the differential methylation of ncRNAs can predict prognosis of patients. However, little is known about the clear relationship between DNA methylation profile of ncRNAs and the prognosis of pancreatic adenocarcinoma (PAC) patients. METHODS: The data of DNA methylation, RNA-seq, miRNA-seq and clinical features of PAC patients were collected from TCGA database. The DNA methylation profile was obtained using the Infinium HumanMethylation450 BeadChip array. LASSO regression was performed to construct two methylation-based classifiers. The risk score of methylation-based classifiers was calculated for each patient, and the accuracy of the classifiers in predicting overall survival (OS) was examined by ROC curve analysis. In addition, Cox regression models were utilized to assess whether clinical variables and the classifiers were independent prognostic factors for OS. The targets of miRNA and the genes co-expressed with lncRNA were identified with DIANA microT-CDS and the Multi-Experiment Matrix (MEM), respectively. Moreover, DAVID Bioinformatics Resources were applied to analyse the functional enrichment of these targets and co-expressed genes. RESULTS: A total of 4004 CpG sites of miRNA and 11,259 CpG sites of lncRNA were screened. Among these CpG sites, 8 CpG sites of miRNA and 7 CpG sites of lncRNA were found with regression coefficients. By multiplying the sum of methylation degrees of the selected CpGs with these coefficients, two methylation-based classifiers were constructed. The classifiers have shown good performance in predicting the survival rate of PAC patients at varying follow-up times. Interestingly, both of these two classifiers were predominant and independent factors for OS. Furthermore, functional enrichment analysis demonstrated that aberrantly methylated miRNAs and lncRNAs are related to calcium ion transmembrane transport and MAPK, Ras and calcium signalling pathways. CONCLUSION: In the present study, we identified two methylation-based classifiers of ncRNA associated with OS in PAC patients through a comprehensive analysis of miRNA and lncRNA profiles. We are the first group to demonstrate a relationship between the aberrant DNA methylation of ncRNAs and the prognosis of PAC, and this relationship would contribute to individualized PAC therapy.