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.

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.

Multifunctional Composite Inverse Opal Film with Multiactives for Wound Healing.

A film with an elaborate microstructure and multifunctions is urgently needed in wound healing. Here, we present a multiactive encapsulated inverse opal film with a monitorable delivery system for chronic wound healing. The inverse opal film is prepared by using poly(lactic-co-glycolic acid) to negatively replicate a colloidal crystal template, which presents a high specific surface area and interconnected nanopores. It could be imparted with a potent antibacterial effect and promote angiogenesis by loading the vascular endothelial growth factor into the nanopores and encapsulating by chitosan. In addition, it is demonstrated that the structure color change of the film could intuitively reflect the drug release progress from the nanopores, which made the film a real-time drug monitoring system. In the affected wound model, the properties of the multifunctional film in promoting wound healing are certified by the faster healing speed, more granulation tissue, less inflammation, and even a distribution of new blood vessels and collagen. These results indicate that the resultant multifunctional film has a practical application value in clinical wound care.

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.

An immunogenomic signature for molecular classification in hepatocellular carcinoma.

Immunity plays an important role in tumor development. In this study, we aimed to investigate molecular classification and its prognostic value in hepatocellular carcinoma (HCC) based on immune signature. Gene set enrichment analysis (GSEA) was used to calculate scores of immune pathways for HCC and hierarchical clustering in two databases (The Cancer Genome Atlas [TCGA], Liver Cancer-RIKEN, JP [LIRI_JP]). The scores of the immune microenvironment and the proportions of 22 immune cells were also calculated. Single-sample GSEA (ssGSEA) was used to screen survival prognosis-related immune pathways and calculate the hazard radio of differentially expressed immune-related genes (IRGs), which were validated in clinical samples and multiple datasets. Based on the immune characteristics, we identified three HCC subtypes, namely immunity high (Immunity_H), immunity medium (Immunity_M), and immunity low (Immunity_L), and confirmed that the classification was reliable and predictable. Immunity_H with a higher immune and stromal score indicated better survival rate. Cox regression analysis showed that IL18RAP and IL7R were the protective genes. Immune risk score was the independent risk factor of overall survival in HCC patients. These results indicated that immunogenomic classification could distinguish HCC patients with different immune status, which could impact the prognosis of the patients with HCC.

Development of cancer prognostic signature based on pan-cancer proteomics.

Utilizing genomic data to predict cancer prognosis was insufficient. Proteomics can improve our understanding of the etiology and progression of cancer and improve the assessment of cancer prognosis. And the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has generated extensive proteomics data of the vast majority of tumors. Based on CPTAC, we can perform a proteomic pan-carcinoma analysis. We collected the proteomics data and clinical features of cancer patients from CPTAC. Then, we screened 69 differentially expressed proteins (DEPs) with R software in five cancers: hepatocellular carcinoma (HCC), children's brain tumor tissue consortium (CBTTC), clear cell renal cell carcinoma (CCRC), lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC). GO and KEGG analysis were performed to clarify the function of these proteins. We also identified their interactions. The DEPs-based prognostic model for predicting over survival was identified by least absolute shrinkage and selection operator (LASSO)-Cox regression model in training cohort. Then, we used the time-dependent receiver operating characteristics analysis to evaluate the ability of the prognostic model to predict overall survival and validated it in validation cohort. The results showed that the DEPs-based prognostic model could accurately and effectively predict the survival rate of most cancers.

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).

The Outcomes of Minimally Invasive versus Open Posterior Approach Spinal Fusion in Treatment of Lumbar Spondylolisthesis: The Current Evidence from Prospective Comparative Studies.

Purpose. To investigate the evidence of minimally invasive (MI) versus open (OP) posterior lumbar fusion in treatment of lumbar spondylolisthesis from current prospective literatures. Methods. The electronic literature database of Pubmed, Embase, and Cochrane library was searched at April 2016. The data of operative time, estimated blood loss and length of hospital stay, visual analog scale (VAS) of both lower back pain and leg pain, Oswestry disability index (ODI), SF-36 PCS (physical component scores) and SF-36 MCS (mental component scores), complications, fusion rate, and secondary surgery were extracted and analyzed by STATA 12.0 software. Results. Five nonrandom prospective comparative studies were included in this meta-analysis. The meta-analysis showed that the MI group had a significantly longer operative time than OP group, less blood loss, and shorter hospital stay. No significant difference was found in back pain, leg pain, ODI, SF-36 PCS, SF-36 MCS, complications, fusion rate, and secondary surgery between MI and OP groups. Conclusion. The prospective evidence suggested that MI posterior fusion for spondylolisthesis had less EBL and hospital stay than OP fusion; however it took more operative time. Both MI and OP fusion had similar results in pain and functional outcomes, complication, fusion rate, and secondary surgery.

The accuracy of a method for printing three-dimensional spinal models.

BACKGROUND: To study the morphology of the human spine and new spinal fixation methods, scientists require cadaveric specimens, which are dependent on donation. However, in most countries, the number of people willing to donate their body is low. A 3D printed model could be an alternative method for morphology research, but the accuracy of the morphology of a 3D printed model has not been determined. METHODS: Forty-five computed tomography (CT) scans of cervical, thoracic and lumbar spines were obtained, and 44 parameters of the cervical spine, 120 parameters of the thoracic spine, and 50 parameters of the lumbar spine were measured. The CT scan data in DICOM format were imported into Mimics software v10.01 for 3D reconstruction, and the data were saved in .STL format and imported to Cura software. After a 3D digital model was formed, it was saved in Gcode format and exported to a 3D printer for printing. After the 3D printed models were obtained, the above-referenced parameters were measured again. RESULTS: Paired t-tests were used to determine the significance, set to P<0.05, of all parameter data from the radiographic images and 3D printed models. Furthermore, 88.6% of all parameters of the cervical spine, 90% of all parameters of the thoracic spine, and 94% of all parameters of the lumbar spine had Intraclass Correlation Coefficient (ICC) values >0.800. The other ICC values were <0.800 and >0.600; none were <0.600. CONCLUSION: In this study, we provide a protocol for printing accurate 3D spinal models for surgeons and researchers. The resulting 3D printed model is inexpensive and easily obtained for spinal fixation research.

The radiological feature of anterior occiput-to-axis screw fixation as it guides the screw trajectory on 3D printed models: a feasibility study on 3D images and 3D printed models.

Anterior occiput-to-axis screw fixation is more suitable than a posterior approach for some patients with a history of posterior surgery. The complex osseous anatomy between the occiput and the axis causes a high risk of injury to neurological and vascular structures, and it is important to have an accurate screw trajectory to guide anterior occiput-to-axis screw fixation. Thirty computed tomography (CT) scans of upper cervical spines were obtained for three-dimensional (3D) reconstruction. Cylinders (1.75 mm radius) were drawn to simulate the trajectory of an anterior occiput-to-axis screw. The imitation screw was adjusted to 4 different angles and measured, as were the values of the maximized anteroposterior width and the left-right width of the occiput (C0) to the C1 and C1 to C2 joints. Then, the 3D models were printed, and an angle guide device was used to introduce the screws into the 3D models referring to the angles calculated from the 3D images. We found the screw angle ranged from α1 (left: 4.99±4.59°; right: 4.28±5.45°) to α2 (left: 20.22±3.61°; right: 19.63±4.94°); on the lateral view, the screw angle ranged from ß1 (left: 13.13±4.93°; right: 11.82±5.64°) to ß2 (left: 34.86±6.00°; right: 35.01±5.77°). No statistically significant difference was found between the data of the left and right sides. On the 3D printed models, all of the anterior occiput-to-axis screws were successfully introduced, and none of them penetrated outside of the cortex; the mean α4 was 12.00±4.11 (left) and 12.25±4.05 (right), and the mean ß4 was 23.44±4.21 (left) and 22.75±4.41 (right). No significant difference was found between α4 and ß4 on the 3D printed models and α3 and ß3 calculated from the 3D digital images of the left and right sides. Aided with the angle guide device, we could achieve an optimal screw trajectory for anterior occiput-to-axis screw fixation on 3D printed C0 to C2 models.