Candidate Prognostic Biomarker Research Articles

Proteomic research in sarcomas - current status and future opportunities.

Sarcomas are a rare group of mesenchymal cancers comprising over 70 different histological subtypes. For the majority of these diseases, the molecular understanding of the basis of their initiation and progression remains unclear. As such, limited clinical progress in prognosis or therapeutic regimens have been made over the past few decades. Proteomics techniques are being increasingly utilised in the field of sarcoma research. Proteomic research efforts have thus far focused on histological subtype characterisation for the improvement of biological understanding, as well as for the identification of candidate diagnostic, predictive, and prognostic biomarkers for use in clinic. However, the field itself is in its infancy, and none of these proteomic research findings have been translated into the clinic. In this review, we provide a brief overview of the proteomic strategies that have been employed in sarcoma research. We evaluate key proteomic studies concerning several rare and ultra-rare sarcoma subtypes including, gastrointestinal stromal tumours, osteosarcoma, liposarcoma, leiomyosarcoma, malignant rhabdoid tumours, Ewing sarcoma, myxofibrosarcoma, and alveolar soft part sarcoma. Consequently, we illustrate how routine implementation of proteomics within sarcoma research, integration of proteomics with other molecular profiling data, and incorporation of proteomics into clinical trial studies has the potential to propel the biological and clinical understanding of this group of complex rare cancers moving forward.

Competing endogenous RNA (ceRNA) hypothetic model based on comprehensive analysis of long non-coding RNA expression in lung adenocarcinoma.

BackgroundNon-small cell lung cancer (NSCLC) is a major subtype of lung cancer with high malignancy and bad prognosis, consisted of lung adenocarcinomas (LUAD) and lung squamous cell carcinomas (LUSC) chiefly. Multiple studies have indicated that competing endogenous RNA (ceRNA) network centered long noncoding RNAs (lncRNAs) can regulate gene expression and the progression of various cancers. However, the research about lncRNAs-mediated ceRNA network in LUAD is still lacking.MethodsIn this study, we analyzed the RNA-seq database from The Cancer Genome Atlas (TCGA) and obtained dysregulated lncRNAs in NSCLC, then further identified survival associated lncRNAs through Kaplan–Meier analysis. Quantitative real time PCR (qRT-PCR) was performed to confirm their expression in LUAD tissues and cell lines. The ceRNA networks were constructed based on DIANA-TarBase and TargetScan databases and visualized with OmicShare tools. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to investigate the potential function of ceRNA networks.ResultsIn total, 1,437 and 1,699 lncRNAs were found to be up-regulated in LUAD and LUSC respectively with 895 lncRNAs overlapping (|log2FC| > 3, adjusted P value <0.01). Among which, 222 lncRNAs and 46 lncRNAs were associated with the overall survival (OS) of LUAD and LUSC, and 18 out of 222 up-regulated lncRNAs were found to have inverse correlation with LUAD patients’ OS (|log2FC| > 3, adjusted P value < 0.02). We selected 3 lncRNAs (CASC8, LINC01842 and VPS9D1-AS1) out of these 18 lncRNAs and confirmed their overexpression in lung cancer tissues and cells. CeRNA networks were further constructed centered CASC8, LINC01842 and VPS9D1-AS1 with 3 miRNAs and 100 mRNAs included respectively.ConclusionThrough comprehensively analyses of TCGA, our study identified specific lncRNAs as candidate diagnostic and prognostic biomarkers for LUAD. The novel ceRNA network we created provided more insights into the regulatory mechanisms underlying LUAD.

Slow skeletal muscle troponin T, titin and myosin light chain 3 are candidate prognostic biomarkers for Ewing's sarcoma.

Ewing's sarcoma (ES) is a common malignant bone tumor in children and adolescents. Although great efforts have been made to understand the pathogenesis and development of ES, the underlying molecular mechanism remains unclear. The present study aimed to identify new key genes as potential biomarkers for the diagnosis, targeted therapy or prognosis of ES. mRNA expression profile chip data sets GSE17674, GSE17679 and GSE45544 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened using the R software limma package, and functional and pathway enrichment analyses were performed using the enrichplot package and GSEA software. The NetworkAnalyst online tool, as well as Cytoscape and its plug-ins cytoHubba and NetworkAnalyzer, were used to construct a protein-protein interaction network (PPI) and conduct module analysis to screen key (hub) genes. LABSO COX regression and overall survival (OS) analysis of the Hub genes were performed. A total of 211 DEGs were obtained by integrating and analyzing the three data sets. The functions and pathways of the DEGs were mainly associated with the regulation of small-molecule metabolic processes, cofactor-binding, amino acid, proteasome and ribosome biosynthesis in eukaryotes, as well as the Rac1, cell cycle and P53 signaling pathways. A total of one important module and 20 hub genes were screened from the PPI network using the Maximum Correlation Criteria algorithm of cytoHubba. LASSO COX regression results revealed that titin (TTN), fast skeletal muscle troponin T, skeletal muscle actin α-actin, nebulin, troponin C type 2 (fast), myosin light-chain 3 (MYL3), slow skeletal muscle troponin T (TNNT1), myosin-binding protein C1 slow-type, tropomyosin 3 and myosin heavy-chain 7 were associated with prognosis in patients with ES. The Kaplan-Meier curves demonstrated that high mRNA expression levels of TNNT1 (P<0.001), TTN (P=0.049), titin-cap (P=0.04), tropomodulin 1 (P=0.011), troponin I2 fast skeletal type (P=0.021) and MYL3 (P=0.017) were associated with poor OS in patients with ES. In conclusion, the DEGs identified in the present study may be key genes in the pathogenesis of ES, three of which, namely TNNT1, TTN and MYL3, may be potential prognostic biomarkers for ES.

ENO1 Acts as a Prognostic Biomarker Candidate and Promotes Tumor Growth and Migration Ability Through the Regulation of Rab1A in Colorectal Cancer.

BackgroundColorectal carcinoma (CRC) is one of the most common malignancies with a dismal 5‐year survival rate. The glycolytic enzyme α-enolase (ENO1) is overexpressed in multiple cancers and is involved in tumor cell proliferation and metastasis. However, its clinical significance, biological role, and underlying molecular mechanisms in CRC are still unclear. The aim of the present study was to investigate the potential role of ENO1 in the initiation and development of CRC.Patients and methodsThe in situ expression of ENO1 in CRC and adjacent normal tissues was examined by immunohistochemistry. The effects of ENO1 on the in vitro proliferation and migration of CRC cell lines were investigated by MTT, colony formation, and Transwell assays. Finally, the in vivo tumorigenic capacity of ENO1 was assessed in a mouse model.ResultsENO1 was overexpressed in CRC tissues and significantly correlated with the clinicopathological parameters. Furthermore, Rab1A was also overexpressed in CRC tissues and was positively correlated to that of ENO1. The high expression levels of both ENO1 and Rab1A led to significantly worse prognosis of CRC patients compared to either alone. Furthermore, knockdown of ENO1 significantly inhibited CRC cells proliferation and migration in vitro and reduced xenograft growth in vivo via the concomitant downregulation of Rab1A.ConclusionThe ENO1/Rab1A signaling axis is involved in CRC progression and is a potential biomarker for the treatment of CRC.

Detection of a 5-circRNA signature to improve prognostic prediction in gastric cancer

Gastric cancer (GC) is a commonly diagnosed malignancy with a high mortality rate worldwide. Despite advances in therapeutic approaches, the 5-year survival rate of patients with GC remains poor. A...

Comprehensive Analysis of a Competing Endogenous RNA Network Identifies Seven-lncRNA Signature as a Prognostic Biomarker for Melanoma.

Long non-coding RNAs (LncRNAs) can act as competing endogenous RNA (ceRNA) involving in tumor initiation and progression. Nevertheless, the prognostic roles of lncRNAs in lncRNA-related ceRNA network of melanoma remain elusive. In this study, RNA sequence profiles were downloaded from The Cancer Genome Atlas (TCGA) database, and there were 2020 differentially expressed messenger RNAs (DEmRNAs), 438 differentially expressed lncRNAs (DElncRNAs) and 65 differentially expressed microRNAs (DEmiRNAs) between primary and metastasis melanoma patients. A ceRNA regulatory network was constructed based on the DElncRNAs-DEmiRNAs and DEmiRNAs-DEmRNAs interactions, which contained 39 lncRNAs, 10 miRNAs, and 16 mRNAs. Furthermore, univariate and multivariate Cox regression analysis were carried out to establish a 7-lncRNA prognostic signature. Subsequently, the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve and the Kaplan-Meier risk survival analysis revealed the significant performance of this signature. Finally, pathway enrichment analyses implied that lncRNA MIR205HG and MIAT were associated with multiple cancer-related pathways, especially epidermis development and immune response. The current study provides novel insights into the lncRNA-related ceRNA network and the potential of lncRNAs to be candidate prognostic biomarkers and therapeutic targets in melanoma.

Weighted Gene Coexpression Network Analysis Identifies Cysteine-Rich Intestinal Protein 1 (CRIP1) as a Prognostic Gene Associated with Relapse in Patients with Acute Myeloid Leukemia.

BackgroundAcute myeloid leukemia (AML) is associated with a high relapse rate and poor prognosis. This study aimed to use weighted gene coexpression network analysis (WGCNA) of gene coexpression networks to identify candidate prognostic biomarker genes in patients with AML and to investigate the expression of these genes in the human U937 cell line in vitro.Material/MethodsRNA-seq data were retrieved from the Cancer Genome Atlas (TCGA) and included bone marrow samples and survival data of patients with AML (N=151), patients who did not relapse after treatment (N=119), and patients with relapse (N=40). Differentially expressed genes were identified, WGCNA was used to detect functional modules, and survival analysis was performed. The Cell Counting Kit-8 (CCK-8) assay investigated the proliferation of U937 cells transfected with short hairpin RNAs (shRNAs), shCRIP1, shHIST1H1C, and shHIST1H1E. RNA-seq analysis identified gene expression following CRIP1 knockdown.ResultsEighty-two genes were associated with both relapse and prognosis in patients with AML. There were two prognosis-related gene modules in the coexpression network. In the coexpression network, the histone cluster 1 H1 family member gene, HIST1H1C had the maximum relapse fold change, HIST1H1E had the lowest survival p-value, and the cysteine-rich intestinal protein 1 (CRIP1) gene had the most edge numbers and was significantly associated with poor prognosis (P=0.0165786). RNA-seq data showed that there was a significant difference in gene expression after CRIP1 knockdown in U937 cells.ConclusionsWGCNA of gene coexpression networks identified CRIP1 as a potential prognostic biomarker gene in patients with AML.

1956P - The biological implications of PDCD6 dysregulation in colorectal cancer

BackgroundProgrammed cell death protein 6 (PDCD6) mediates apoptosis via p53 dependent and independent pathways and is implicated in many cancer hallmarks. A multi-omics analysis of colorectal cancer (CRC) cell lines identified PDCD6 as a candidate prognostic and predictive biomarker. PDCD6 is dysregulated in different cancers, but the role of PDCD6 in CRC is still poorly understood. MethodsThe clinicopathological data on 483 CRC patients treated at Mater Dei Hospital Malta between January 2008 and December 2011 were collated and tissue microarrays constructed in triplicate to represent central and peripheral tumour areas. PDCD6 protein expression was quantified by multiplex immunofluorescence (mIF; QuPath) along with fifteen other protein targets representing important cancer signalling and immune pathways. Expression of 770 genes was assessed using the NanoString nCounter® PanCancer Pathways Panel in a subset of 34 patients with low/high PDCD6 expression. Associations with clinicopathological parameters including disease-free survival and overall survival (OS) were calculated. ResultsIntra-tumoural PDCD6 protein expression was heterogeneous, having a higher expression at the tumour edge (p=0.001). Invasive edge PDCD6 protein expression was significantly associated with TNM stage and tumour grade (p=0.04 and <0.001, respectively), with lower PDCD6 expression in higher stage and poorly differentiated CRCs. PDCD6 expression was also associated with 5-year OS (p=0.003), and higher PDCD6 expression revealed a trend towards longer OS. PDCD6 and Ki67 expression were negatively correlated (rs = - 0.192, p=0.002). Low and high PDCD6 expressers showed differential gene expression in several other cancer pathways. ConclusionsPDCD6 shows both inter- and intra-tumoural heterogeneity in CRC, with expression patterns consistent with a tumour suppressive function. Contrarily, wider literature suggests that PDCD6 might also have an intrinsic oncogenic role under certain circumstances, and further exploration of PDCD6 pleiotropism in CRC is warranted. Legal entity responsible for the studyRomina Briffa. FundingREACH HIGH Scholars Programme Part-financed by the European Union, Operational Programme II; Cohesion Policy 2014-2020 "Investing in human capital to create more opportunities and promote the wellbeing of society.". DisclosureAll authors have declared no conflicts of interest.

DNA methylation profiling analysis identifies a DNA methylation signature for predicting prognosis and recurrence of lung adenocarcinoma.

The aim of the present study was to identify candidate prognostic DNA methylation biomarkers for lung adenocarcinoma (LUAD), since the modern precise medicine for the treatment of LUAD requires more biomarkers and novel therapeutic targets of interest. DNA methylation profiling data of LUAD were downloaded from The Cancer Gene Atlas portal. Differentially methylated genes (DMGs) were screened to differentiate between samples designated as good and bad prognosis. LUAD-associated methylation modules were obtained with the weighted correlation network analysis (WGCNA) package, followed by function enrichment analysis. Optimal prognostic DMGs were selected using the LASSO estimation-based Cox-PH approach and were used to construct a prognostic risk scoring system. The training set was dichotomized by risk score, into high- and low-risk groups. The differences in overall survival (OS) time or recurrence-free survival (RFS) time between the two groups were evaluated using a Kaplan-Meier curve. A total of 742 DMG samples were screened for good and bad prognosis. WGCNA identified three LUAD-associated modules, which were primarily associated with cytoskeleton organization, transcription and apoptosis. A nine-gene prognostic methylation signature was determined, which included C20orf56, BTG2, C13orf16, DNASE1L1, ZDHHC3, FHDC1, ARF6, ITGB3 and ICAM4. A risk score-based methylation signature classified the patients in the training set into high- and low-risk groups with significantly different OS or RFS times. The prognostic value of the methylation signature was successfully verified in a validation set. In conclusion, the present study identified a nine-gene methylation signature for the prediction of survival and recurrence in patients with LUAD and improved the understanding of the alterations in DNA methylation in LUAD.

Highly Sensitive Single Molecular Array Immunoassay Measurement of t-Tau, NF-L, GFAP, and UCH-L1 Biomarkers in Acute Concussion/Mild Traumatic Brain Injury (mTBI) Patient Serum and Saliva Samples

ObjectiveTo determine if t-Tau, NF-L, GFAP and UCH-L1 protein biomarkers are elevated in early time points of acute concussion/mild traumatic brain injury patient serum and saliva, relative to control samples.Backgroundt-Tau, NF-L, GFAP and UCH-L1 levels have been reported to increase in cerebral spinal fluid (CSF) and blood following head trauma within 24 hours or longer, and are candidate diagnostic and prognostic biomarkers of concussion and mild to moderate TBI. However, limited information exists on the relationship between these biomarkers at short time points post-injury, and detectability in saliva of mTBI patients.Design/MethodsBiomarker analysis of serum from a total of 120 participants, derived from two independent sample groups consisting of 60 concussion/mTBI patients each, with blood collected within 1-4 hr and 8-16 hr post-injury, respectively, was compared with 30 healthy control sera. Saliva samples were collected after 8-16 hr post-injury from a n = 30 subset of the same patients. Quanterix Simoa 4-plex immunoassay was used for highly sensitive measurements of these biomarkers.ResultsMedian levels of NF-L, GFAP and UCH-L1 were significantly higher in independent sets of patient serum samples (n = 60 each), both at early (1–4 hr) and later (8–16 hr) time points post-mTBI/concussion, relative to control samples (n = 30) (p &lt; 0.0001, = 0.0001, &lt;0.0001, respectively). Low levels of t-Tau are detected, but are significantly elevated post-concussion relative to controls (p = 0.0001). Significant correlations were observed between levels of t-Tau and UCH-L1, NF-L and GFAP, and t-Tau and GFAP in both post-injury time-point groups, and between NF-L and UCH-L1 levels in the 8-16 hr group. The four biomarkers were detected in saliva from concussion/mTBI patients (n = 30).ConclusionsThis study supports the utility of ultra-sensitive multiplex immunoassays to detect increases in CNS proteins at high sensitivity in serum and saliva within 1-4 and 8-16 hr of concussion/mTBI.

Nomogram for predicting gastric cancer recurrence using biomarker gene expression

BackgroundRecently, researchers have tried to predict patient prognosis using biomarker expression in cancer patients. The aim of this study was to develop a nomogram predicting the 5-year recurrence-free probability (RFP) of gastric cancer patients using prognostic biomarker gene expression. MethodsWe enrolled 360 patients in the training data set to develop the predictive model and nomogram. We analyzed the patients’ general variables and the gene expression levels of 10 prognostic biomarker candidates between the nonrecurrence and recurrence groups. We also performed external validation using 420 patients from the validation data set. ResultsThe final nomogram was composed of age, sex, and the expression levels of CAPZA, PPase, OCT-1, PRDX4, gamma-enolase, and c-Myc. The five-year RFPs were 89%, 75%, 54% and 32% for the patients in the low-risk, intermediate-risk, high-risk and very-high-risk groups in the development cohort, respectively. In the external validation cohort, the 5-year RFPs were 89%, 75%, 63% and 60%, respectively. The areas under the curve were 0.718 (95% CI, 0.65–0.78) and 0.640 (95% CI, 0.57–0.70) for the training and validation data sets, respectively. The RFP Kaplan-Meier curves were significantly different among the 4 groups in the training and validation data sets (p < 0.0001). ConclusionThis newly developed nomogram using gene expression can predict the 5-year RFP for gastric cancer patients after surgical treatment. We hope that this nomogram will help in the therapeutic decision between endoscopic treatment and gastrectomy.

Ventilatory compensation during the incremental exercise test is inversely correlated with air trapping in COPD

Background: Air trapping and gas exchange abnormalities are major causes of exercise limitation in chronic obstructive pulmonary disease (COPD). During incremental cardiopulmonary exercise testing, ventilatory equivalents for carbon dioxide (VE/VCO2) and oxygen (VE/VO2) may be difficult to identify in COPD patients because of limited ventilatory compensation capacity. Therefore, we aimed to detect a possible correlation between the magnitude of ventilation augmentation, as manifested by increments in ventilatory equivalents from nadir to peak effort values and air trapping, detected with static testing. Methods: In this observational study, we studied data obtained previously from 20 COPD patients who, during routine follow-up, underwent a symptom-limited incremental exercise test and in whom a plethysmography was obtained concurrently. Air trapping at rest was assessed by measurement of the residual volume (RV) to total lung capacity (TLC) ratio (RV/TLC). Gas exchange data collected during the symptom-limited incremental cardiopulmonary exercise test allowed determination of the nadir and peak effort values of VE/VCO2 and VE/VO2, thus enabling calculation of the difference between peak effort value and nadir values of VE/VCO2 and VE/VO2, designated ΔVE/VCO2 and ΔVE/VO2, respectively. Results: We found a statistically significant inverse correlation between both ΔVE/VCO2 (r = -0. 5058, 95% CI -0.7750 to -0.08149, p = 0.0234) and ΔVE/VO2 (r = -0.5588, 95% CI -0.8029 to -0.1545, p = 0.0104) and the degree of air trapping (RV/TLC). There was no correlation between ΔVE/VCO2 and peak oxygen consumption, forced expiratory volume in the first second, or body mass index. Conclusions: The ventilatory equivalents increment to compensate for acidosis during incremental exercise testing was inversely correlated with air trapping (RV/TLC) and may be a candidate prognostic biomarker.

Screening and Identification of Potential Prognostic Biomarkers in Adrenocortical Carcinoma.

Objective: Adrenocortical carcinoma (ACC) is a rare but aggressive malignant cancer that has been attracting growing attention over recent decades. This study aims to integrate protein interaction networks with gene expression profiles to identify potential biomarkers with prognostic value in silico. Methods: Three microarray data sets were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) according to the normalization annotation information. Enrichment analyses were utilized to describe biological functions. A protein–protein interaction network (PPI) of the DEGs was developed, and the modules were analyzed using STRING and Cytoscape. LASSO Cox regression was used to identify independent prognostic factors. The Kaplan–Meier method for the integrated expression score was applied to analyze survival outcomes. A receiver operating characteristic (ROC) curve was constructed with area under curve (AUC) analysis to determine the diagnostic ability of the candidate biomarkers. Results: A total of 150 DEGs and 24 significant hub genes with functional enrichment were identified as candidate prognostic biomarkers. LASSO Cox regression suggested that ZWINT, PRC1, CDKN3, CDK1 and CCNA2 were independent prognostic factors in ACC. In multivariate Cox analysis, the integrated expression scores of the modules showed statistical significance in predicting disease-free survival (DFS, P = 0.019) and overall survival (OS, P < 0.001). Meanwhile, ROC curves were generated to validate the ability of the Cox model to predict prognosis. The AUC index for the integrated genes scores was 0.861 (P < 0.0001). Conclusion: In conclusion, the present study identifies DEGs and hub genes that may be involved in poor prognosis and early recurrence of ACC. The expression levels of ZWINT, PRC1, CDKN3, CDK1 and CCNA2 are of high prognostic value, and may help us understand better the underlying carcinogenesis or progression of ACC. Further studies are required to elucidate molecular pathogenesis and alteration in signaling pathways for these genes in ACC.

Overexpression of immunoproteasome low-molecular-mass polypeptide 7 and inhibiting role of next-generation proteasome inhibitor ONX 0912 on cell growth in glioma.

The aim of this study was to determine the expression level of immunoproteasome and its clinical significance in glioma preliminarily. Furthermore, we studied the function and molecular mechanism of proteasome inhibitor ONX 0912 on glioma cell. The expression of immunoproteasome in glioma and tumor-adjacent brain tissues was detected by western blot. Immunohistochemical technique was used to detect the expression of low-molecular-mass polypeptide 7 in 55 cases of glioma tissues and 6 cases of tumor-adjacent brain tissues. Chi-square test was used to analyze the relationship between the expression level of low-molecular-mass polypeptide 7 and clinical characteristics. Kaplan-Meier method and Cox regression analysis were applied to analyze the correlation between low-molecular-mass polypeptide 7 expression and prognosis of patients. 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2Htetrazolium) (MTS) proliferation assay was introduced to detect the impact of ONX 0912 on proliferation of glioma cells. Western blot was used to detect the apoptosis- and autophagy-related protein in glioma cell treated with ONX 0912. Our results showed that only low-molecular-mass polypeptide 7 expression was notably upregulated in gliomas in comparison with tumor-adjacent brain tissues and further increased in malignant gliomas compared with benign gliomas (P < 0.01). In the multivariate Cox proportional regression analyses, it was evident that low-molecular-mass polypeptide 7 was an independent unfavorable prognostic factor (P < 0.05). The results of MTS assay showed that ONX 0912 could inhibit the proliferation of glioma cell. Besides, we found that ONX 0912 could prompt apoptosis and autophagosome accumulation, which may be responsible for inhibiting glioma cell proliferation. In conclusion, our results indicated that low-molecular-mass polypeptide 7 might be a candidate prognostic biomarker, and proteasome inhibitor ONX 0912 might act as a potential treatment agent for glioma.

Cancer‑related long noncoding RNAs show aberrant expression profiles and competing endogenous RNA potential in esophageal adenocarcinoma

Long non-coding RNAs (lncRNAs) govern gene expression by competitively binding to microRNA response elements (MREs). Although they were initially considered as transcriptional noise, lncRNAs have attracted increased attention in oncology. Dysregulation of lncRNAs occurs in various types of human tumor, including esophageal adenocarcinoma (EAC). However, the functions of these cancer-associated lncRNAs and of their related competitive endogenous RNA (ceRNA) network in EAC remains unknown. To determine the relevant potential mechanisms, the present study analyzed the transcriptome sequencing data and clinical information of 79 patients with EAC, including 79 tumor samples and 11 normal samples, which were obtained from The Cancer Genome Atlas esophageal cancer project. The edgeR v3.25.0 software was used for differential gene expression analysis. The results exhibited 561 cancer-associated lncRNAs with a >2.0-fold change and a false discovery rate-adjusted P<0.01. Among these lncRNAs, 26 were significantly associated with patient overall survival. According to data from bioinformatics databases and differentially expressed RNAs, an lncRNA-regulated ceRNA network for EAC was constructed. The results demonstrated that the aberrantly expressed lncRNA-associated ceRNA network included 37 EAC cancer-associated lncRNAs, five miRNAs and 13 mRNAs. In conclusion, the present study identified novel lncRNAs as candidate prognostic biomarkers and revealed a potential regulatory network of gene expression in EAC.

An eight-long non-coding RNA signature as a candidate prognostic biomarker for bladder cancer.

Backgroud: Bladder cancer (BLCA) is one of the most fatal types of cancer worldwide. However, there are limited methods for us to provide a prognostic prediction of BLCA patients. Therefore, we aimed at developing a lncRNA signature to improve the prognosis prediction of BLCA.Results: An eight-lncRNA signature was significantly associated with recurrence free survival in BLCA patients from both discovery and validation groups. Furthermore, genes involved in the signature were enriched in extracellular matrix organization pathway. Finally, functional experiments demonstrated that six out of the eight lncRNAs significantly regulated the invasion of BLCA cells.Method: A total of 343 BLCA patients from The Cancer Genome Atlas (TCGA) were employed and randomly divided into training (n=172) and validating (n=171) groups. The lncRNA expression profiles of BLCA patients were screened and a risk-score formula were created and validated according to the Cox regression analysis. Next, WGCNA method was employed to cluster genes that highly correlated with the risk scores based on the profiling data of TCGA dataset and transwell assay was also performed to further investigate the role of these lncRNAs.Conclusions: Our results suggested that the eight-lncRNA signature was a candidate prognostic biomarker for predicting tumor recurrence of patients with BLCA.

Repression of metastasis-associated protein 2 for inhibiting metastasis of human oral cancer cells by promoting the p-cofilin-1/ LC3-II expression.

The overexpression of metastasis-associated protein 2 (MTA2) contributes to human tumor progression and metastasis in various tumor cells. However, the role of MTA2 in human oral cancer progression remains unknown. MTA2 expression in human oral tumor tissues and cell lines was measured by immunohistochemistry and Western blotting. Cell proliferation and cell cycle were analyzed using MTT assay and flow cytometry. The effects of MTA2 on oral cell migration and invasion were investigated using migration and invasion assays. The expression of MTA2, p-cofilin-1, and MTA2-induced LC3-II levels were measured using Western blotting and an immunofluorescence assay. Based on the human oral cancer tissue array and TCGA database, we found that MTA2 was increased in oral cancer tissues than in non-tumor oral tissues (P<.01). Moreover, MTA2 is significantly associated with tumor grade (P<.01) and the overall survival rate of patients with grade III tumor (P<.05). MTA2 expression in oral cancer cells was markedly higher than that in normal oral cells. Cell proliferation and cell cycle were not significantly changed in the cells inhibited by MTA2. MTA2 knockdown can inhibit cell migration and invasion of human oral cancer cells. Furthermore, we suggest that MTA2 inhibition enhances p-cofilin and LC3-II expression, and the knockdown of LC3-II expression in cells inhibited by MTA2 had the opposite effect. These results indicate that MTA2 may serve as a candidate prognostic biomarker and that targeting autophagy is a potential therapeutic strategy for treating human oral cancer.

Tissue and serum metabolomic phenotyping for diagnosis and prognosis of hepatocellular carcinoma.

More than two-thirds of patients with hepatocellular carcinoma (HCC) cannot receive curative therapy and have poor survival due to late diagnosis and few prognostic directions. In our study, nontargeted and targeted metabolomics analyses were conducted by liquid chromatography-mass spectrometry to characterize metabolic features of HCC and identify diagnostic and prognostic biomarker candidate incorporating liver tissue and serum metabolites. A total of 552 subjects, including 432 with liver tissue and 120 with serum specimens, were recruited in China. In the discovery cohort, a series of 138 metabolites were identified to discriminate HCC tissues from matched nontumor tissues. Retinol presented with the highest area under the curve (AUC) of 0.991 and associated with Edmondson grade. In the validation cohort, all metabolites in retinol metabolism pathway were examined and the levels of retinol and retinal in tumor tissue and serum decreased in the order of normal to cirrhosis to HCC of Edmondson Grades I to IV. Retinol and retinal levels could also differentiate between HCC and cirrhosis, with AUCs of 0.996 and 0.994, respectively, in tissue and 0.812 and 0.744, respectively, in serum. The AUC of the combined retinol and retinal panel in serum was 0.852. Univariate and multivariate Cox regression identified this panel as an independent predictor for HCC and showed that low expression of retinol and retinal correlated with decreased survival time. In conclusion, the retinol metabolic signature had considerable diagnostic and prognostic value for identifying HCC patients who would benefit from prompt therapy and optimal prognostic direction.

Identification of AUNIP as a candidate diagnostic and prognostic biomarker for oral squamous cell carcinoma.

BackgroundOral squamous cell carcinoma (OSCC) is one of the most common malignant tumors worldwide. Patients with poorly differentiated OSCC often exhibit a poor prognosis. AUNIP (Aurora Kinase A and Ninein Interacting Protein), also known as AIBp, plays a key role in cell cycle and DNA damage repair. However, the function of AUNIP in OSCC remains elusive.MethodsThe differentially expressed genes (DEGs) were obtained using R language. Receiver operating characteristic curve analysis was performed to identify diagnostic markers for OSCC. The effectiveness of AUNIP in diagnosing OSCC was evaluated by machine learning. AUNIP expression was analyzed in publicly available databases and clinical specimens. Bioinformatics analysis and in vitro experiments were conducted to explore biological functions and prognostic value of AUNIP in OSCC.FindingsThe gene integration analysis revealed 90 upregulated DEGs. One candidate biomarker, AUNIP, for the diagnosis of OSCC was detected, and its expression gradually increased along with malignant differentiation of OSCC. Bioinformatics analysis demonstrated that AUNIP could be associated with tumor microenvironment, human papillomavirus infection, and cell cycle in OSCC. The suppression of AUNIP inhibited OSCC cell proliferation and resulted in G0/G1 phase arrest in OSCC cells. The survival analysis showed that AUNIP overexpression predicted poor prognosis of OSCC patients.Interpretation: AUNIP could serve as a candidate diagnostic and prognostic biomarker for OSCC and suppression of AUNIP may be a potential approach to preventing and treating OSCC.FundTaishan Scholars Project in Shandong Province (ts201511106) and the National Natural Science Foundation of China (Nos. 61603218).

Competing Endogenous RNA and Coexpression Network Analysis for Identification of Potential Biomarkers and Therapeutics in association with Metastasis Risk and Progression of Prostate Cancer.

Prostate cancer (PCa) is the most frequently diagnosed malignant neoplasm in men. Despite the high incidence, the underlying pathogenic mechanisms of PCa are still largely unknown, which limits the therapeutic options and leads to poor prognosis. Herein, based on the expression profiles from The Cancer Genome Atlas (TCGA) database, we investigated the interactions between long noncoding RNA (lncRNA) and mRNA by constructing a competing endogenous RNA network. Several competing endogenous RNAs could participate in the tumorigenesis of PCa. Six lncRNA signatures were identified as potential candidates associated with stage progression by the Kolmogorov-Smirnov test. In addition, 32 signatures from the coexpression network had potential diagnostic value for PCa lymphatic metastasis using machine learning algorithms. By targeting the coexpression network, the antifungal compound econazole was screened out for PCa treatment. Econazole could induce growth restraint, arrest the cell cycle, lead to apoptosis, inhibit migration, invasion, and adhesion in PC3 and DU145 cell lines, and inhibit the growth of prostate xenografts in nude mice. This systematic characterization of lncRNAs, microRNAs, and mRNAs in the risk of metastasis and progression of PCa will aid in the identification of candidate prognostic biomarkers and potential therapeutic drugs.