Derivation and Comprehensive Analysis of Aging Patterns in Patients with Bladder CancerRead the full article
Disease Markers publishes papers related to the identification of disease markers, the elucidation of their role and mechanism, as well as their application in the prognosis, diagnosis and treatment of diseases.
Chief Editor Paola Gazzaniga is an Associate Professor in the Department of Molecular Medicine at Sapienza University of Rome, Italy. Her core research focuses on liquid biopsies in patients with solid tumors.
Latest ArticlesMore articles
Identification and Validation of a Proliferation-Associated Score Model Predicting Survival in Lung Adenocarcinomas
Aim. This study is aimed at building a risk model based on the genes that significantly altered the proliferation of lung adenocarcinoma cells and exploring the underlying mechanisms. Methods. The data of 60 lung adenocarcinoma cell lines in the Cancer Dependency Map (Depmap) were used to identify the genes whose knockout led to dramatical acceleration or deacceleration of cell proliferation. Then, univariate Cox regression was performed using the survival data of 497 patients with lung adenocarcinoma in The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) model was used to construct a risk prediction score model. Patients with lung adenocarcinoma from TCGA were classified into high- or low-risk groups based on the scores. The differences in clinicopathologic, genomic, and immune characteristics between the two groups were analyzed. The prognosis of the genes in the model was verified with immunohistochemical staining in 100 samples from the Department of Thoracic Surgery, Zhongshan Hospital, and the alteration in the proliferation rate was checked after these genes were knocked down in lung adenocarcinoma cells (A549 and H358). Results. A total of 55 genes were found to be significantly related to survival by combined methods, which were crucial to tumor progression in functional enrichment analysis. A six-gene-based risk prediction score, including the proteasome subunit beta type-6 (PSMB6), the heat shock protein family A member 9 (HSPA9), the deoxyuridine triphosphatase (DUT), the cyclin-dependent kinase 7 (CDK7), the polo-like kinases 1 (PLK1), and the folate receptor beta 2 (FOLR2), was built using the LASSO method. The high-risk group classified with the score model was characterized by poor overall survival (OS), immune infiltration, and relatively higher mutation load. A total of 9864 differentially expressed genes and 138 differentially expressed miRNAs were found between the two groups. Also, a nomogram comparing score model, age, and the stage was built to predict OS for patients with lung adenocarcinoma. Using immunohistochemistry, the expression levels of PSMB6, HSPA9, DUT, CDK7, and PLK1 were found to be higher in lung adenocarcinoma tissues of patients, while the expression of FOLR2 was low, which was consistent with survival prediction. The knockdown of PSMB6 and HSPA9 by siRNA significantly downregulated the proliferation of A549 and H358 cells. Conclusion. The proposed score model may function as a promising risk prediction tool for patients with lung adenocarcinoma and provide insights into the molecular regulation mechanism of lung adenocarcinoma.
A Cell Cycle Progression-Derived Gene Signature to Predict Prognosis and Therapeutic Response in Hepatocellular Carcinoma
Objective. Dysregulation of cell cycle progression (CCP) is one of the hallmarks of cancer. Here, our study is aimed at developing a CCP-derived gene signature for predicting high-risk population of hepatocellular carcinoma (HCC). Methods. Our study retrospectively analyzed the transcriptome profiling and clinical information of HCC patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) projects. Uni- and multivariate cox regression models were conducted for identifying which hallmarks of cancer were risk factors of HCC. CCP-derived gene signature was developed with LASSO method. The predictive efficacy was verified by ROC curves and subgroup analyses. A nomogram was then generated and validated by ROC, calibration, and decisive curves. Immune cell infiltration was estimated with ssGSEA method. Potential small molecular compounds were predicted via CTRP and CMap analyses. The response to chemotherapeutic agents was evaluated based on the GDSC project. Results. Among hallmarks of cancer, CCP was identified as a dominant risk factor for HCC prognosis. CCP-derived gene signature displayed the favorable predictive efficacy in HCC prognosis independent of other clinicopathological parameters. A nomogram was generated for optimizing risk stratification and quantifying risk evaluation. CCP-derived signature was in relation to immune cell infiltration, HLA, and immune checkpoint expression. Combining CTRP and CMap analyses, fluvastatin was identified as a promising therapeutic agent against HCC. Furthermore, CCP-derived signature might be applied for predicting the response to doxorubicin and gemcitabine. Conclusion. Collectively, CCP-derived gene signature was a promising marker in prediction of survival outcomes and therapeutic responses for HCC patients.
Neuropeptides as the Shared Genetic Crosstalks Linking Periodontitis and Major Depression Disorder
Background. The aim of this study was at investigating the association between major depressive disorder (MDD) and periodontitis based on crosstalk genes and neuropeptides. Methods. Datasets for periodontitis (GSE10334, GSE16134, and GSE23586) and MDD (GSE38206 and GSE39653) were downloaded from GEO. Following batch correction, a differential expression analysis was applied (MDD: and periodontitis , ). The neuropeptide data were downloaded from NeuroPep and NeuroPedia. Intersected genes were potential crosstalk genes. The correlation between neuropeptides and crosstalk genes in MDD and periodontitis was analyzed with Pearson correlation coefficient. Subsequently, regression analysis was performed to calculate the differentially regulated link. Cytoscape was used to map the pathways of crosstalk genes and neuropeptides and to construct the protein-protein interaction network. Lasso regression was applied to screen neuropeptides, whereby boxplots were created, and receiver operating curve (ROC) analysis was conducted. Results. The MDD dataset contained 30 case and 33 control samples, and the periodontitis dataset contained 430 case and 139 control samples. 35 crosstalk genes were obtained. A total of 102 neuropeptides were extracted from the database, which were not differentially expressed in MDD and periodontitis and had no intersection with crosstalk genes. Through lasso regression, 9 neuropeptides in MDD and 43 neuropeptides in periodontitis were obtained. Four intersected neuropeptide genes were obtained, i.e., ADM, IGF2, PDYN, and RETN. The results of ROC analysis showed that IGF2 was highly predictive in MDD and periodontitis. ADM was better than the other three genes in predicting MDD disease. A total of 13 crosstalk genes were differentially coexpressed with four neuropeptides, whereby FOSB was highly expressed in MDD and periodontitis. Conclusion. The neuropeptide genes ADM, IGF2, PDYN, and RETN were intersected between periodontitis and MDD, and FOSB was a crosstalk gene related to these neuropeptides on the transcriptomic level. These results are a basis for future research in the field, needing further validation.
Association of Myocardial Enzyme Abnormality with Clinical Outcomes of Patients with COVID-19: A Retrospective Study
Background. It has been observed that COVID-19 may cause myocardial damage, but there are few detailed reports on myocardial enzyme abnormalities. Methods. In this retrospective study, we analyzed data from 157 consecutive laboratory-confirmed and hospitalized COVID-19 patients from Wuhan. We collected information on demographic and clinical characteristics, laboratory findings, and clinical outcomes. Logistic regression analysis was used to explore the risk factors associated with the severity of COVID-19. The association between myocardial enzyme abnormalities and the mortality was also investigated. Results. The mortality in abnormal myocardial enzyme group was obviously higher than the normal group (). The majority of patients (, 97.3%) with normal cardiac enzyme group were of the common novel coronavirus pneumonia (NCP) type, whereas half of the patients with cardiac enzyme abnormalities (, 48.2%) developed critical and severe NCP type. The multivariable logistic regression analysis indicated that COVID-19 patients with increasing age (), higher levels of CRP (), and TNI () were associated with increased death than other patients. Conclusions. Myocardial enzyme abnormality and myocardial injury were associated with the severity and fatal outcomes of COVID-19. Clinicians should pay attention to the markers of myocardial injury in COVID-19 patients, especially those with older age, comorbidities, and inflammation.
Identification of Epithelial-Mesenchymal Transition- (EMT-) Related LncRNA for Prognostic Prediction and Risk Stratification in Esophageal Squamous Cell Carcinoma
Background. Epithelial-mesenchymal transition (EMT) is significantly associated with the invasion and development of esophageal squamous cell carcinoma (ESCC). However, the importance of EMT-related long noncoding RNA (lncRNA) is little known in ESCC. Methods. GSE53624 () and GSE53622 () datasets retrieved from the Gene Expression Omnibus (GEO) database were used as training and external validation cohorts, respectively. GSE53624 and GSE53622 datasets were all sampled from China. Then, the prognostic value of EMT-related lncRNA was comprehensively investigated by weighted coexpression network analysis (WGCNA) and COX regression model. Results. High expression of PLA2G4E-AS1, AC063976.1, and LINC01592 significantly correlated with the favorable overall survival (OS) of ESCC patients, and LINC01592 had the greatest contribution to OS. Importantly, ESCC patients were divided into low- and high-risk groups based on the optimal cut-off value of risk score estimated by the multivariate COX regression model of these three lncRNA. Patients with high risk had a shorter OS rate and restricted mean survival time (RMST) than those with low risk. Moreover, univariate and multivariate COX regression revealed that risk stratification, age, and TNM were independent prognostic predictors, which were used to construct a nomogram model for individualized and visualized prognosis prediction of ESCC patients. The calibration curves and time-dependent ROC curves in the training and validation cohorts suggested that the nomogram model had a good performance. Interestingly, clear trends indicated that risk score positively correlated with tumor microenvironment (TME) scores and immune checkpoints TIGIT, CTLA4, and BTLA. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that PLA2G4E-AS1, AC063976.1, and LINC01592 were primarily associated with TNF signaling pathway, NF-kappa B signaling pathway, and ECM-receptor interaction. Conclusion. We developed EMT-related lncRNA PLA2G4E-AS1, AC063976.1, and LINC01592 for prognostic prediction and risk stratification of Chinese ESCC patients, which might provide deep insight for personalized prognosis prediction in Chinese ESCC patients and be potential biomarkers for designing novel therapy.
Urinary Matrix Metalloproteinase-9 and Nephrin in Idiopathic Membranous Nephropathy: A Cross-Sectional Study
Aim. Idiopathic membranous nephropathy (IMN) has a varied clinical course that requires accurate prediction as a prerequisite for treatment administration. Currently, its prognosis relies on proteinuria, a clinical parameter whose onset lags behind kidney injury. Increased urinary excretion of matrix metalloproteinase-9 (MMP-9) and nephrin has been reported in a number of IMN-like glomerular diseases in which they reflected disease severity. However, little or nothing is known of the importance of these biomarkers in IMN, a major cause of adult nephrotic syndrome. To highlight their potential, we measured both biomarkers and assessed their relationships with key parameters of renal function in IMN. Methods. We quantified urinary MMP-9 and nephrin in 107 biopsy-proven IMN patients and 70 healthy subjects by enzyme-linked immunosorbent assay (ELISA). We then compared biomarker levels between patients and healthy subjects and among patients with different clinical features. We also determined the relationship of each biomarker with proteinuria and the estimated glomerular filtration rate (eGFR). Results. Urinary MMP-9 and nephrin were significantly higher in IMN compared to healthy controls. Unlike nephrin, MMP-9 correlated significantly with proteinuria and was significantly higher among patients with nephrotic range proteinuria. Both biomarkers were correlated with eGFR, but only MMP-9 was significantly higher in patients with eGFR less than 90 ml/min/1.73 m2. Conclusion. Our findings suggest that urinary MMP-9 holds a greater potential than urinary nephrin in monitoring the severity of IMN.