Identification and Validation of a Novel Six-lncRNA-Based Prognostic Model for Lung Adenocarcinoma

ObjectiveThis study was conducted in order to establish a long non-coding RNA (lncRNA)-based model for predicting overall survival (OS) in patients with lung adenocarcinoma (LUAD).MethodsOriginal RNA-seq data of LUAD samples were extracted from The Cancer Genome Atlas (TCGA) database. Univariate Cox survival analysis was performed to select lncRNAs associated with OS. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox analysis were performed for building an OS-associated lncRNA prognostic model. Moreover, receiver operating characteristic (ROC) curves were generated to assess predictive values of the hub lncRNAs. Consequently, qRT-PCR was conducted to validate its prognostic value. The potential roles of these lncRNAs in immunotherapy and anti-angiogenic therapy were also investigated.ResultsThe lncRNA-associated risk score of OS (LARSO) was established based on the LASSO coefficient of six individual lncRNAs, including CTD-2124B20.2, CTD-2168K21.1, DEPDC1-AS1, RP1-290I10.3, RP11-454K7.3, and RP11-95M5.1. Kaplan–Meier analysis revealed that LUAD patients with higher LARSO values had a shorter OS. Furthermore, a new risk score (NRS), including LARSO, stage, and N stage, could better predict the prognosis of LUAD patients compared with LARSO alone. Evaluation of the prognostic model in our cohort demonstrated that patients with higher scores had a worse prognosis. In addition, correlation analysis between these six lncRNAs and immune checkpoints or anti-angiogenic targets suggested that LUAD patients with high LARSO might not be sensitive to immunotherapy or anti-angiogenic therapy.ConclusionsThis robust six-lncRNA prognostic signature may be used as a novel and powerful prognostic biomarker for lung adenocarcinoma.

Identification of Epithelial Mesenchymal Transition-Related lncRNAs Associated with Prognosis and Tumor Immune Microenvironment of Hepatocellular Carcinoma

Purpose. The long noncoding RNAs (lncRNAs) play the important role in tumor occurrence and progression, and the epithelial to mesenchymal transition (EMT) is the critical process for tumor migration. However, the role of EMT-related lncRNA in hepatocellular carcinoma (HCC) has not been elucidated. Methods. In this study, we selected the EMT-related lncRNAs in HCC by using data from The Cancer Genome Atlas database (TCGA). Two prognostic models of the overall survival (OS) and relapse-free survival (RFS) were constructed and validated through Cox regression model, Kaplan-Meier analysis, and the receiver-operating characteristic (ROC) curves. The unsupervised clustering analysis was utilized to investigate the association between EMT-lncRNAs with tumor immune microenvironment. ESTIMATE algorithm and gene set enrichment analysis (GSEA) were used to estimate tumor microenvironment and associated KEGG pathways. Results. Two EMT-related lncRNA prognostic models of OS and RFS were constructed. Kaplan-Meier curves showed the dismal prognosis of OS and RFS in the group with high-risk score. The ROC curves and AUC values in two prognostic models indicated the discriminative value in the training set and validation set. Patients with HCC were clustered into two subgroups according the unsupervised clustering analysis. Lnc-CCNY-1 was selected as the key lncRNA. GSVA analysis showed that lnc-CCNY-1 was negatively associated with peroxisome proliferator-activated receptor (PPAR) signaling pathway and positively correlated with CELL cycle pathway. Conclusion. Two EMT-related lncRNA prognostic models of OS and RFS were constructed to discriminate patients and predict prognosis of HCC. EMT-related lncRNAs may play a role on prognosis of HCC by influencing the immune microenvironment. Lnc-CCNY-1 was selected as the key EMT-related lncRNA for further exploration.

Identification of the most specific markers to differentiate primary pulmonary carcinoma from metastatic gastrointestinal carcinoma to the lung

Background A number of biomarkers have the potential of differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract, however, a standardised panel for that purpose does not exist yet. We aimed to identify the smallest panel that is most sensitive and specific at differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract. Methods A total of 170 samples were collected, including 140 primary and 30 non-primary lung tumours and staining for CK7, Napsin-A, TTF1, CK20, CDX2, and SATB2 was performed via tissue microarray. The data was then analysed using univariate regression models and a combination of multivariate regression models and Receiver Operating Characteristic (ROC) curves. Results Univariate regression models confirmed the 6 biomarkers’ ability to independently predict the primary outcome (p < 0.001). Multivariate models of 2-biomarker combinations identified 11 combinations with statistically significant odds ratios (ORs) (p < 0.05), of which TTF1/CDX2 had the highest area under the curve (AUC) (0.983, 0.960–1.000 95% CI). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 75.7, 100, 100, and 37.5% respectively. Multivariate models of 3-biomarker combinations identified 4 combinations with statistically significant ORs (p < 0.05), of which CK7/CK20/SATB2 had the highest AUC (0.965, 0.930–1.000 95% CI). The sensitivity, specificity, PPV, and NPV were 85.1, 100, 100, and 41.7% respectively. Multivariate models of 4-biomarker combinations did not identify any combinations with statistically significant ORs (p < 0.05). Conclusions The analysis identified the combination of CK7/CK20/SATB2 to be the smallest panel with the highest sensitivity (85.1%) and specificity (100%) for predicting tumour origin with an ROC AUC of 0.965 (p < 0.001; SE: 0.018, 0.930–1.000 95% CI).

Serum D-Lactate, a Novel Serological Biomarker, Is Promising for the Diagnosis of Periprosthetic Joint Infection

Background: Although many markers are used for diagnosis of periprosthetic joint infection (PJI), serological screening and diagnosis for PJI are still challenging. We evaluated the performance of serum D-lactate and compared it with ESR, coagulation-related biomarkers and synovial D-lactate for the diagnosis of PJI.Methods: Consecutive patients with preoperative blood and intraoperative joint aspiration of a prosthetic hip or knee joint before revision arthroplasty were prospectively included. The diagnosis of PJI was based on the criteria of the Musculoskeletal Infection Society, and the diagnostic values of markers were estimated based on receiver operating characteristic (ROC) curves by maximizing sensitivity and specificity using optimal cutoff values.Results: Of 52 patients, 26 (50%) were diagnosed with PJI, and 26 (50%) were diagnosed with aseptic failure. ROC curves showed that serum D-lactate, fibrinogen (FIB) and ESR had equal areas under the curve (AUCs) of 0.80, followed by D-dimer and fibrin degradation product, which had AUCs of 0.67 and 0.69, respectively. Serum D-lactate had the highest sensitivity of 88.46% at the optimal threshold of 1.14 mmol/L, followed by FIB and ESR, with sensitivities of 80.77% and 73.08%, respectively, while there were no significant differences in specificity (73.08%, 73.08% and 76.92%, respectively). Conclusion: Serum D-lactate showed similar performance to FIB and ESR for diagnosis of PJI. The advantages of serum D-lactate are pathogen-specific, highly sensitive, minimally invasive and rapidly available making serum D-lactate useful as a point-of-care screening test for PJI.

Association between Phenotypic Age and Mortality in Patients with Multivessel Coronary Artery Disease

Background. Chronological age (CA) is not a perfect proxy for the true biological aging status of the body. A new biological aging measure, phenotypic age (PhenoAge), has been shown to capture morbidity and mortality risk in the general US population and diverse subpopulations. This study was aimed at evaluating the association between PhenoAge and long-term outcome of patients with multivessel coronary artery disease (CAD). Methods. A total of 609 multivessel CAD patients who received PCI attempt and with follow-up were enrolled. The clinical outcome was all-cause mortality on follow-up. PhenoAge was calculated using an equation constructed from CA and 9 clinical biomarkers. Cox proportional hazards regression models and receiver operating characteristic (ROC) curves were performed to evaluate the association between PhenoAge and mortality. Results. Overall, patients with more diseases had older PhenoAge and phenotypic age acceleration (PhenoAgeAccel). After a median follow-up of 33.5 months, those with positive PhenoAgeAccel had a significantly higher incidence of all-cause mortality ( P = 0.001 ). After adjusting for CA, Cox proportional hazards models showed that both PhenoAge and PhenoAgeAccel were significantly associated with all-cause mortality. Even after further adjusting for confounding factors, each 10-year increase in PhenoAge was also associated with a 51% increased mortality risk. ROC curves revealed that PhenoAge, with an area under the curve of 0.705, significantly outperformed CA, the individual clinical chemistry measure, and other risk factors. When reexamining the ROC curves using various combinations of variables, we found that PhenoAge provides additional predictive power to all models. Conclusions. In conclusion, PhenoAge was strongly associated with all-cause mortality even after adjusting for CA. Our findings suggest that PhenoAge measure may be complementary in predicting mortality risk for patients with multivessel CAD.

Circulating Exosomal circRNAs Contribute to Potential Diagnostic Value of Large Artery Atherosclerotic Stroke

Large artery atherosclerotic (LAA) stroke is closely associated with atherosclerosis, characterized by the accumulation of immune cells. Early recognition of LAA stroke is crucial. Circulating exosomal circRNAs profiling represents a promising, noninvasive approach for the detection of LAA stroke. Exosomal circRNA sequencing was used to identify differentially expressed circRNAs between LAA stroke and normal controls. From a further validation stage, the results were validated using RT-qPCR. We then built logistic regression models of exosomal circRNAs based on a large replication stage, and receiver operating characteristic (ROC) curves were constructed to assess the diagnostic efficacy. Using exosomal circRNA sequencing, large sample validation, and diagnostic model construction revealed that exosomal circ_0043837 and circ_ 0001801were independent predictive factors for LAA stroke, and had better diagnostic efficacy than plasma circRNAs. In the atherosclerotic group (AS), we developed a nomogram for clinical use that integrated the two-circRNA-based risk factors to predict which patients might have the risk of plaque rupture. Circulating exosomal circRNAs profiling identifies novel predictive biomarkers for the LAA stroke and plaque rupture, with superior diagnostic value than plasma circRNAs. It might facilitate the prevention and better management of this disease.

Predicting Prognosis In Primary Pulmonary Sarcoma Using A Clinical Nomogram Based On SEER Database

Background: Primary pulmonary sarcoma (PPS) accounts for less than 1.1% of all pulmonary tumors. Few data outcomes are reported. This study aims to clarify the predictive value of clinicopathologic features on the overall survival (OS) of PPS patients.Methods: Patients with primary pulmonary sarcoma (PPS) were collected from the Surveillance, Epidemiology, and End Results (SEER) database (from 2000 to 2015) and divided randomly into training and validation cohorts at a ratio of 1:1. Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) were implemented to identify prognostic factors related to overall survival of primary pulmonary sarcoma patients. Then, we performed multivariate Cox regression to establish a prognostic factors signature. The Kaplan- Meier (K-M) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted to estimate the prognostic power of the signature. In addition, multivariate Cox regression screened out independent prognostic factors and constructed a nomogram. Results: PPS patients with training group were divided into low- and high-risk group based on risk score, and high-risk group had a shorter survival time. The validation group got the same result. (P<0.001). On multivariate analysis of the training cohort, independent factors for survival were marriage, age, sex, grade, operation, metastasis and tumor size, which were all selected into the nomogram. The calibration curve and ROC plots for probability of 3-year and 5-year survival were in accord with prediction by nomogram and actual observation. And the C-index of the nomogram for predicting survival was 0.77 (95% CI, 0.74 to 0.80, P<0.05), which was statistically significant. Conclusion: We constructed a risk prognosis model based on PPS patients from SEER database. In addition, the construction of nomogram provides one more idea for clinical treatment.

Geometrical and Hemodynamic Variables for Thrombotic Risk Stratification in Kawasaki Disease

Objectives Thrombosis is a major adverse outcome for coronary artery aneurysms (CAA) in Kawasaki disease (KD). We investigated the geometric and hemodynamic abnormalities in patients with CAA and identified the risk factors for thrombosis by computational fluid dynamics (CFD) simulation. Methods We retrospectively studied 27 KD patients with 77 CAAs, including 20 CAAs with thrombosis in 12 patients. Patient-specific anatomic models obtained from cardiac magnetic resonance imaging (CMRI) were constructed to perform a CFD simulation. From the simulation results, we produced local hemodynamic parameters comprising of time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI) and relative resident time (RRT). The CAA’s maximum diameter (Dmax) and Z-score were measured on CMRI. Results Giant CAAs tended to present with more severe hemodynamic abnormalities. Thrombosed CAAs exhibited lower TAWSS (1.551 ± 1.535 vs. 4.235 ± 4.640dynes/cm2, p = 0.002), higher Dmax (10.905 ± 4.125 vs. 5.791 ± 2.826mm, p = 0.008), Z-score (28.301 ± 13.558 vs. 13.045 ± 8.394, p = 0.002), OSI (0.129 ± 0.132 vs. 0.046 ± 0.080, p = 0.01), and RRT (16.780 ± 11.982s vs. 9.123 ± 11.770s, p = 0.399) than the non-thrombosed group. An ROC analysis for thrombotic risk proved that all of the five parameters had area under the ROC curves (AUC) above 0.7, with Dmax delineating the highest AUC (AUCDmax = 0.871) and a 90% sensitivity, followed by Z-score (AUCZ−score = 0.849). Conclusions It is reasonable to combine the geometric index with hemodynamic information to establish a severity classification for KD cases.

Comprehensive Analysis of Hub Genes Associated With Competing Endogenous RNA Networks in Stroke Using Bioinformatics Analysis

Background: Acute ischemic stroke (AIS) is the second leading cause of death and the third leading cause of disability worldwide. Long noncoding RNAs (lncRNAs) are promising biomarkers for the early diagnosis of AIS and closely participate in the mechanism of stroke onset. However, studies focusing on lncRNAs functioning as microRNA (miRNA) sponges to regulate the mRNA expression are rare and superficial.Methods: In this study, we systematically analyzed the expression profiles of lncRNA, mRNA (GSE58294), and miRNA (GSE110993) from the GEO database. Gene ontology (GO) analysis was performed to reveal the functions of differentially expressed genes (DEGs), and we used weighted gene co-expression network analysis (WGCNA) to investigate the relationships between clinical features and expression profiles and the co-expression of miRNA and lncRNA. Finally, we constructed a lncRNA–miRNA–mRNA competing endogenous RNA (ceRNA) network with selected DEGs using bioinformatics methods and obtained ROC curves to assess the diagnostic efficacy of differentially expressed lncRNAs (DElncRNAs) and differentially expressed mRNAs (DEmRNAs) in our network. The GSE22255 dataset was used to confirm the diagnostic value of candidate genes.Results: In total, 199 DElncRNAs, 2068 DEmRNAs, and 96 differentially expressed miRNAs were detected. The GO analysis revealed that DEmRNAs primarily participate in neutrophil activation, neutrophil degranulation, vacuolar transport, and lysosomal transport. WGCNA screened out 16 lncRNAs and 195 mRNAs from DEGs, and only eight DElncRNAs maintained an area under the curve higher than 0.9. By investigating the relationships between lncRNAs and mRNAs, a ceRNA network containing three lncRNAs, three miRNAs, and seven mRNAs was constructed. GSE22255 confirmed that RP1-193H18.2 is more advantageous for diagnosing stroke, whereas no mRNA showed realistic diagnostic efficacy.Conclusion: The ceRNA network may broaden our understanding of AIS pathology, and the candidate lncRNA from the ceRNA network is assumed to be a promising therapeutic target and diagnostic biomarker for AIS.

Role of a Pyroptosis-Related lncRNA Signature in Risk Stratification and Immunotherapy of Ovarian Cancer

Background: Pyroptosis is a newly recognized form of cell death. Emerging evidence has suggested the crucial role of long non-coding RNAs (lncRNAs) in the tumorigenesis and progression of ovarian cancer (OC). However, there is still poor understanding of pyroptosis-related lncRNAs in OC.Methods: The TCGA database was accessed for gene expression and clinical data of 377 patients with OC. Two cohorts for training and validation were established by random allocation. Correlation analysis and Cox regression analysis were performed to identify pyroptosis-related lncRNAs and construct a risk model.Results: Six pyroptosis-related lncRNAs were included in the final signature with unfavorable survival data. Subsequent ROC curves showed promising predictive value of patient prognosis. Further multivariate regression analyses confirmed the signature as an independent risk factor in the training (HR: 2.242, 95% CI: 1.598–3.145) and validation (HR: 1.884, 95% CI: 1.204–2.95) cohorts. A signature-based nomogram was also established with a C-index of.684 (95% CI: 0.662–0.705). Involvement of the identified signature in multiple immune-related pathways was revealed by functional analysis. Moreover, the signature was also associated with higher expression of three immune checkpoints (PD-1, B7-H3, and VSIR), suggesting the potential of the signature as an indicator for OC immunotherapies.Conclusion: This study suggests that the identified pyroptosis-related lncRNA signature and signature-based nomogram may serve as methods for risk stratification of OC. The signature is also associated with the tumor immune microenvironment, potentially providing an indicator for patient selection of immunotherapy in OC.