An 11-lncRNA expression could be potential prognostic biomarkers in head and neck squamous cell carcinoma

The aim of our study is to construct the competing endogenous RNA (ceRNA) network of head and neck squamous cell carcinoma (HNSCC) and identify key long noncoding RNAs (lncRNAs) to predict prognosis. The genes whose expression were differentially in HNSCC and normal tissues were explored by the Cancer Genome Atlas database. The ceRNA network was constructed by the Cytoscape software. The lncRNAs which could estimate the overall survival were explored from Cox proportional hazards regression. There are 1997, 589, and 82 mRNAs, lncRNAs, and miRNAs whose expression were statistically significant different, respectively. Then, the network between miRNA and mRNA or miRNA and lncRNA was constructed by miRcode, miRDB, TargetScan, and miRanda. Five mRNAs, 10 lncRNAs, and 3 miRNAs were associated with overall survival. Then, 11-lncRNAs were found to be prognostic factors. Therefore, our research analyzed the potential signature of novel 11-lncRNA as candidate prognostic biomarker from the ceRNA network for patients with HNSCC.     

Integrated analysis of co‐expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer

Long non‐coding RNAs (lncRNAs), which competitively bind miRNAs to regulate target mRNA expression in the competing endogenous RNAs (ceRNAs) network, have attracted increasing attention in breast cancer research. We aim to find more effective therapeutic targets and prognostic markers for breast cancer. LncRNA, mRNA and miRNA expression profiles of breast cancer were downloaded from TCGA database. We screened the top 5000 lncRNAs, top 5000 mRNAs and all miRNAs to perform weighted gene co‐expression network analysis. The correlation between modules and clinical information of breast cancer was identified by Pearson's correlation coefficient. Based on the most relevant modules, we constructed a ceRNA network of breast cancer. Additionally, the standard Kaplan‐Meier univariate curve analysis was adopted to identify the prognosis of lncRNAs. Ultimately, a total of 23 and 5 modules were generated in the lncRNAs/mRNAs and miRNAs co‐expression network, respectively. According to the Green module of lncRNAs/mRNAs and Blue module of miRNAs, our constructed ceRNA network consisted of 52 lncRNAs, 17miRNAs and 79 mRNAs. Through survival analysis, 5 lncRNAs (AL117190.1, COL4A2‐AS1, LINC00184, MEG3 and MIR22HG) were identified as crucial prognostic factors for patients with breast cancer. Taken together, we have identified five novel lncRNAs related to prognosis of breast cancer. Our study has contributed to the deeper understanding of the molecular mechanism of breast cancer and provided novel insights into the use of breast cancer drugs and prognosis.     

CircRNA circPDSS1 promotes the gastric cancer progression by sponging miR-186-5p and modulating NEK2

The aim of this study is to investigate the regulatory mechanism of circPDSS1/miR-186-5p/NEK2 axis on the viability and proliferation in gastric cancer (GC) cell line. Differentially expressed circRNAs, miRNAs, and mRNAs in GC tissues and paracarcinoma tissues were analyzed using gene chips GSE83521, GSE89143, and GSE93415. Then, the expression of circPDSS1, miR-186-5p, and NEK2 was analyzed via quantitative real-time polymerase chain reaction (qRT-PCR). Survival analysis was adopted to explore the association between the circPDSS1 expression and the prognosis of GC. The effect of circPDSS1 on GC cell cycle and apoptosis was verified with the flow cytometry. Targeting relationships among circPDSS1, miR-186-5p, and NEK2 were predicted via bioinformatics analysis and demonstrated by the dual-luciferase reporter assay. Our results showed that circPDSS1 and NEK2 were high-expressed whereas miR-186-5p was low-expressed in GC tissues and cells. CircPDSS1 promoted GC cell cycle and inhibited apoptosis by sponging miR-186-5p, while miR-186-5p inhibited cell cycle and promoted apoptosis by targeting NEK2. Thus, circPDSS1 acts as a tumor promoter by regulating miR-186-5p and NEK2, which could be a potential biomarker and therapeutic target for the management of GC.     

The mRNA–miRNA–lncRNA Regulatory Network and Factors Associated with Prognosis Prediction of Hepatocellular Carcinoma

The aim of this study was to identify novel prognostic mRNA and microRNA (miRNA) biomarkers for hepatocellular carcinoma (HCC) using methods in systems biology. Differentially expressed mRNAs, miRNAs, and long non-coding RNAs (lncRNAs) were compared between HCC tumor tissues and normal liver tissues in The Cancer Genome Atlas (TCGA) database. Subsequently, a prognosis-associated mRNA co-expression network, an mRNA–miRNA regulatory network, and an mRNA–miRNA–lncRNA regulatory network were constructed to identify prognostic biomarkers for HCC through Cox survival analysis. Seven prognosis-associated mRNA co-expression modules were obtained by analyzing these differentially expressed mRNAs. An expression module including 120 mRNAs was significantly correlated with HCC patient survival. Combined with patient survival data, several mRNAs and miRNAs, including CHST4, SLC22A8, STC2, hsa-miR-326, and hsa-miR-21 were identified from the network to predict HCC patient prognosis. Clinical significance was investigated using tissue microarray analysis of samples from 258 patients with HCC. Functional annotation of hsa-miR-326 and hsa-miR-21-5p indicated specific associations with several cancer-related pathways. The present study provides a bioinformatics method for biomarker screening, leading to the identification of an integrated mRNA–miRNA–lncRNA regulatory network and their co-expression patterns in relation to predicting HCC patient survival.     

Identification of functional lncRNAs in gastric cancer by integrative analysis of GEO and TCGA data

Gastric cancer (GC) is a prevalent malignant cancer of digestive system, identification of novel diagnostic and prognostic biomarkers for GC is urgently demanded. The aim of this study was to determine potential long noncoding RNAs (lncRNAs) associated with the pathogenesis and prognosis of GC. Raw noncoding RNA microarray data (GSE53137, GSE70880, and GSE99417) was downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes between GC and adjacent normal gastric tissue samples were screened by an integrated analysis of multiple gene expression profile after gene reannotation and batch normalization. Differentially expressed genes were further confirmed by The Cancer Genome Atlas (TCGA) database. Competing endogenous RNA (ceRNA) network, Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway, survival analysis were extensively applied to identify hub lncRNAs and discover potential biomarkers related to diagnosis and prognosis of GC. In total of 246 integrated differential genes including 15 lncRNAs and 241 messenger RNAs (mRNAs) were obtained after intersections of differential genes between GEO and TCGA database. ceRNA network comprised of three lncRNAs (UCA1, HOTTIP, and HMGA1P4), 26 microRNAs (miRNAs) and 72 mRNAs. Functional analysis revealed that three lncRNAs were mainly dominated in cell cycle and cellular senescence. Survival analysis showed that HMGA1P4 was statistically related to the overall survival rate. For the first time, we identified that HMGA1P4, a target of miR-301b/miR-508, is involved in cell cycle and senescence process by regulating CCNA2 in GC. Finally, the expression levels of three lncRNAs were validated to be upregulated in GC tissues. Thus, three lncRNAs including UCA1, HOTTIP, and HMGA1P4 may contribute to GC development and their potential functions might be associated with the prognosis of GC.     

Screening of differentially expressed miRNAs related to muscle strain and their target gene

Muscle strain is one of the most common muscle injuries seen in the office of a practicing physician. To get a better understanding of this injury, we identified the differentially expressed miRNAs in muscle stem cells collected from injured muscle tissues of mouse. In this study, we downloaded the gene expression microarray (GSE26780) from Gene Expression Omnibus database. The dataset contained a total of 12 samples (murine muscle stem cells), including normal controls and samples collected from tissues at different time points after the injury. Differentially expreesed miRNAs were identified by LIMMA package and target genes of mmu-miR-143 were found by TargetScan. Then, a protein-protein interaction (PPI) network was constructed for the products of these target genes by using KUPS. Finally, Cytoscape and its plugins were used to identify and analyze the modules in this network. According to the results, 121, 136 and 148 differentially expressed miRNAs were identified in injured samples at each time point, and among them, 60 miRNAs were overlapping between all three groups. The expression values of mmu-miR-143 were most significantly altered over time at 36–72 h after the injury. Therefore, 510 target genes of mmu-miR-143 were found and a PPI network for the products of these target genes was constructed. Moreover, two modules were identified in the PPI network. Together with the previous studies, we suppose that proteins in module B, most of which are collagens or integrins, most likely participate in healing of strain injuries through cell adhesion processes.     

The mRNA–miRNA–lncRNA Regulatory Network and Factors Associated with Prognosis Prediction of Hepatocellular Carcinoma

The aim of this study was to identify novel prognostic mRNA and microRNA (miRNA) biomarkers for hepatocellular carcinoma (HCC) using methods in systems biology. Differentially expressed mRNAs, miRNAs, and long non-coding RNAs (lncRNAs) were compared between HCC tumor tissues and normal liver tissues in The Cancer Genome Atlas (TCGA) database. Subsequently, a prognosis-associated mRNA co-expression network, an mRNA–miRNA regulatory network, and an mRNA–miRNA–lncRNA regulatory network were constructed to identify prognostic biomarkers for HCC through Cox survival analysis. Seven prognosis-associated mRNA co-expression modules were obtained by analyzing these differentially expressed mRNAs. An expression module including 120 mRNAs was significantly correlated with HCC patient survival. Combined with patient survival data, several mRNAs and miRNAs, including CHST4, SLC22A8, STC2, hsa-miR-326, and hsa-miR-21 were identified from the network to predict HCC patient prognosis. Clinical significance was investigated using tissue microarray analysis of samples from 258 patients with HCC. Functional annotation of hsa-miR-326 and hsa-miR-21-5p indicated specific associations with several cancer-related pathways. The present study provides a bioinformatics method for biomarker screening, leading to the identification of an integrated mRNA–miRNA–lncRNA regulatory network and their co-expression patterns in relation to predicting HCC patient survival.     

To construct a ceRNA regulatory network as prognostic biomarkers for bladder cancer

Emerging evidence demonstrates that competing endogenous RNA (ceRNA) hypothesis has played a role in molecular biological mechanisms of cancer occurrence and development. But the effect of ceRNA network in bladder cancer (BC), especially lncRNA‐miRNA‐mRNA regulatory network of BC, was not completely expounded. By means of The Cancer Genome Atlas (TCGA) database, we compared the expression of RNA sequencing (RNA‐Seq) data between 19 normal bladder tissue and 414 primary bladder tumours. Then, weighted gene co‐expression network analysis (WGCNA) was conducted to analyse the correlation between two sets of genes with traits. Interactions between miRNAs, lncRNAs and target mRNAs were predicted by MiRcode, miRDB, starBase, miRTarBase and TargetScan. Next, by univariate Cox regression and LASSO regression analysis, the 86 mRNAs obtained by prediction were used to construct a prognostic model which contained 4 mRNAs (ACTC1 + FAM129A + OSBPL10 + EPHA2). Then, by the 4 mRNAs in the prognostic model, a ceRNA regulatory network with 48 lncRNAs, 14 miRNAs and 4 mRNAs was constructed. To sum up, the ceRNA network can further explore gene regulation and predict the prognosis of BC patients.     

Retracted: Deep integrative analysis of microRNA-mRNA regulatory networks for biomarker and target discovery in chondrosarcoma

Chondrosarcoma (CHS) is a common malignant bone sarcoma and its occurrence increases with age. microRNAs (miRNAs) are a class of noncoding RNAs that participate in various biological processes and disease pathogenesis by targeting functional messenger RNA (mRNA). However, the modulation of miRNAs in CHS remains largely unknown. In this study, we performed integrative analysis to explore the expression profiles of miRNAs and mRNAs, together with their interaction networks in human CHS tissues and cell lines by RNA-seq (miRNA and mRNA). A total of 133 and 796 differentially expressed miRNAs and mRNAs were identified (|Fold change| ≥ 2 and P-value ≤ 0.5). miRNA-mRNA regulatory interactions between 55 miRNAs and 242 mRNAs were screened by the Pearson correlation analysis and target prediction. mRNAs in the network were enriched to 145 Gene Ontology terms and 35 Kyoto Encyclopedia of Genes and Genomes pathways. Specifically, some key regulators (hub-miRNAs) in the network (miR-622, miR-4539, miR-145, miR-25, and miR-96) were suggested to play important regulatory roles in the pathogenesis of CHS. In addition, functional experiments validated that miR-622 regulated CHS cell proliferation by targeting bone morphogenetic protein 1 (BMP1).     

The clinical significance of miR-335, miR-124, miR-218 and miR-484 downregulation in gastric cancer

Gastric cancer (GC) is one of the leading types of malignancy worldwide, particularly in Asian populations. Although the exact molecular mechanism of GC development remains unknown, microRNA (miRNA) has recently been shown to be involved. The current study aims to investigate the expression levels of bioinformatically ranked miRNAs in gastric tissues. Using bioinformatics tools, we prioritized miRNAs thought to be implicated in GC. Furthermore, polyA-qPCR was used to validate bioinformatics findings in 40 GC, 31 normal gastric tissue (NG) and 45 gastric dysplasia (GD) samples. As identified by bioinformatics analysis, miR-335 was shown to be the top-ranked miRNA implicated in GC. Moreover, a significant downregulation of miR-335, miR-124, miR-218 and miR-484 was found in GC and GD compared to NG samples. We found bioinformatics to be an efficient approach to finding candidate miRNAs relevant to GC development. Finally, the findings show that downregulation of miRNAs such as miR-124 and miR-218 in gastric tissue can be a significant indicator for neoplastic transformation.     

Comprehensive analysis of a ceRNA network reveals potential prognostic cytoplasmic lncRNAs involved in HCC progression

The aberrant expression of long noncoding RNAs (lncRNAs) has drawn increasing attention in the field of hepatocellular carcinoma (HCC) biology. In the present study, we obtained the expression profiles of lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) in 371 HCC tissues and 50 normal tissues from The Cancer Genome Atlas (TCGA) and identified hepatocarcinogenesis-specific differentially expressed genes (DEGs, log fold change ≥ 2, FDR < 0.01), including 753 lncRNAs, 97 miRNAs, and 1,535 mRNAs. Because the specific functions of lncRNAs are closely related to their intracellular localizations and because the cytoplasm is the main location for competitive endogenous RNA (ceRNA) action, we analyzed not only the interactions among these DEGs but also the distributions of lncRNAs (cytoplasmic, nuclear or both). Then, an HCC-associated deregulated ceRNA network consisting of 37 lncRNAs, 10 miRNAs, and 26 mRNAs was constructed after excluding those lncRNAs located only in the nucleus. Survival analysis of this network demonstrated that 15 lncRNAs, 3 miRNAs, and 16 mRNAs were significantly correlated with the overall survival of HCC patients (p < 0.01). Through multivariate Cox regression and lasso analysis, a risk score system based on 13 lncRNAs was constructed, which showed good discrimination and predictive ability for HCC patient survival time. This ceRNA network-construction approach, based on lncRNA distribution, not only narrowed the scope of target lncRNAs but also provided specific candidate molecular biomarkers for evaluating the prognosis of HCC, which will help expand our understanding of the ceRNA mechanisms involved in the early development of HCC.     

A comprehensive study of construction and analysis of competitive endogenous RNA networks in lung adenocarcinoma

BackgroundLong noncoding RNAs (lncRNAs) have gain increasing attention in lung adenocarcinoma. In this study, we aimed at constructing and analyzing the lncRNAs and the related proteins based competitive endogenous RNA (ceRNA) network.MethodsRNA expression data of lung adenocarcinoma were extracted from the TCGA database. Differentially expressed (DE) lncRNAs, messenger RNAs (mRNAs) and microRNAs (miRNAs) were identified and then a DElncRNA-DEmiRNA-DEmRNA ceRNA network was constructed for lung adenocarcinoma. We also analyzed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of the DEgenes. Kaplan-Meier survival curves were also been further utilized for exploring the prognostic factors.ResultsAfter compared and calculated lncRNA, mRNA and miRNA expression profiles between lung adenocarcinoma and normal samples, 1709 differential expressed lncRNAs, 2554 differential expressed mRNAs and 116 differential expressed miRNAs were finally identified. Afterwards, a lncRNA mediated ceRNA network was constructed, according to the interactions among 544 pairs of DElncRNA-DEmiRNA relationships and 47 pairs of DEmiRNA-DEmRNA relationships. As for the survival analyses, we found 10 DElncRNAs, 25 DEmRNAs and 7 miRNAs have statistically prognostic significance for overall survival, respectively.ConclusionsThis study provides meaningful information for deeper understanding the underlying molecular mechanism of lung adenocarcinoma and for evaluating prognosis, which could monitor recurrence, guide clinical treatment drugs and subsequent related researches.     

Genome-wide identification of a competing endogenous RNA network in cholangiocarcinoma

Cholangiocarcinoma (CCA) is the second widespread liver tumor with relatively poor survival. Increasing evidence in recent studies showed long noncoding RNAs (lncRNAs) exert a crucial impact on the development and progression of CCA based on the mechanism of competing endogenous RNAs (ceRNAs). However, functional roles and regulatory mechanisms of lncRNA-regulated ceRNA in CCA, are only partially understood. The expression profile of messenger RNAs (mRNAs), lncRNAs, and microRNAs (miRNAs) downloaded from The Cancer Genome Atlas were comprehensively investigated. Differential expression of these three types of RNA between CCA and corresponding precancerous tissues were screened out for further analysis. On the basis of interactive information generated from miRDB, miRTarBase, TargetScan, and miRcode public databases, we then constructed an mRNA-miRNA-lncRNA regulatory network. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were conducted to identify the biological function of the ceRNA network involved in CCA. As a result, 2883 mRNAs, 136 miRNAs, and 993 lncRNAs were screened out as differentially expressed RNAs in CCA. In addition, a ceRNA network in CCA was constructed, composing of 50 up and 27 downregulated lncRNAs, 14 up and 7 downregulated miRNAs, 29 up and 25 downregulated mRNAs. Finally, gene set enrichment and pathway analysis indicated our CCA-specific ceRNA network was related with cancer-related pathway and molecular function. In conclusion, our research identified a novel lncRNA-related ceRNA network in CCA, which might act as a potential therapeutic target for patients with CCA.     

Molecular characterization of lung cancer: A two-miRNA prognostic signature based on cancer stem-like cells related genes

Lung cancer is one of the deadliest cancers worldwide. To increase the survival rate of lung cancer, it is necessary to explore specific prognosis markers. More and more evidence finds that noncoding RNA is closely associated with the survival of lung cancer, and cancer stem cells (CSCs) also play a significant role in the progress of lung cancer. The objective of this study is to find CSLCs genes that affect the prognosis of lung cancer. The differential expression of long noncoding RNAs (lncRNAs), microRNAs (miRNAs), messenger RNAs (mRNAs) in the Cancer Genome Atlas (TCGA) database and differential expression data from microarray of CD326⁺ and CD326⁻ A549 cell are intersected to identify stable and consistent expression genes (2 lncRNAs, 15 miRNAs, and 134 mRNAs). The intersection of lncRNAs and miRNAs is analyzed by univariate and multivariate Cox regression to obtained prognostic genes. Two miRNAs (miR-30b-5p and miR-29c-3p) are significantly correlated with the overall survival rate. Then using these two miRNAs to construct a risk score model as a prognosis signature of lung cancer. Subsequently, we analyzed the association between two miRNAs and clinical information of lung cancer patients, of which T stage, Neoplasm cancer and risk score (P < .05) can be used as independent prognostic indicators of lung cancer. Finally, target genes of 2 miRNAs and 134 mRNAs were annotated with Gene Ontology and analyzed with Kyoto Encyclopedia of Genes and Genomes pathway, and verified with the GEO database. In summary, this study illustrates the role of miRNAs in the promotion of lung cancer by CSCs, which is important to find molecular biomarkers of lung cancer.     

MicroRNA-135b/CAMK2D Axis Contribute to Malignant Progression of Gastric Cancer through EMT Process Remodeling

There is a continued need for investigating the roles of microRNAs (miRNAs) and their targets on the progression of gastric cancer (GC), especially metastasis. Here, we performed an integrated study to identify dysregulated miRNAs critical for GC development and progression. miR-135b was determined as a promising biomarker for GC. The expression level of miR-135b was increased among GC cell lines, patient tumor tissues, serum samples, and correlation with aggravation of the GC patients. The in vitro functional assays demonstrated overexpression of miR-135b promoted cell proliferation, migration and invasion in GC, while miR-135b inhibition led to the opposite results. CAMK2D was found to be the direct target of miR-135b, serving as a tumor suppressor in GC cells. Based on our and public datasets, we confirmed the attenuation of CAMK2D expression in GC tissues. And, the expression levels of miR-135b and CAMK2D were closely associated with prognosis of GC patients. Ectopic expression of miR-135b resulted in the down-regulation of CAMK2D. Additionally, CAMK2D was a prerequisite for miR-135b to promote GC cells proliferation and migration by regulating the EMT process, which was confirmed by the in vivo experiments. Importantly, in vivo injection of miR-135b antagomir significantly repressed the tumor growth and metastasis of xenograft models, which suggested that the miR-135b antagomir were promising for clinical applications. Taken together, these results indicate that miR-135b/CAMK2D axis drives GC progression by EMT process remodeling, suggesting that miR-135b may be utilized as a new therapeutic target and prognostic marker for GC patients.     

Integrated analysis of ceRNA network and tumor-infiltrating immune cells in esophageal cancer

Background: Esophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. The present study aimed at analyzing the competing endogenous RNA (ceRNA) network and tumor-infiltrating immune cells in ESCA.Methods: The expression profiles of mRNAs, lncRNAs, and miRNAs were downloaded from the Cancer Genome Atlas database. A ceRNA network was established based on the differentially expressed RNAs by Cytoscape. CIBERSORT was applied to estimate the proportion of immune cells in ESCA. Prognosis-associated genes and immune cells were applied to establish prognostic models basing on Lasso and multivariate Cox analyses. The survival curves were constructed with Kaplan–Meier method. The predictive efficacy of the prognostic models was evaluated by the receiver operating characteristic (ROC) curves.Results: The differentially expressed mRNAs, lncRNAs, and miRNAs were identified. We constructed the ceRNA network including 23 lncRNAs, 19 miRNAs, and 147 mRNAs. Five key molecules (HMGB3, HOXC8, HSPA1B, KLHL15, and RUNX3) were identified from the ceRNA network and five significant immune cells (plasma cells, T cells follicular helper, monocytes, dendritic cells activated, and neutrophils) were selected via CIBERSORT. The ROC curves based on key genes and significant immune cells all showed good sensitivity (AUC of 3-year survival: 0.739, AUC of 5-year survival: 0.899, AUC of 3-year survival: 0.824, AUC of 5-year survival: 0.876). There was certain correlation between five immune cells and five key molecules.Conclusion: The present study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis.     

Prediction of Blood miRNA-mRNA Regulatory Network in Gastric Cancer

Background:The aim of the study was to suggest a high specific and sensitive blood biomarker for early GC diagnosis.Methods:the expression data of miRNAs and mRNAs were collected from the blood samples of the GC patients based on literature mining. Bioinformatics tools and databases (PANTHER, TargetScan, miRTarBase, miRDB, STRING, and Cytoscape) were used to predict the regulatory relationship. Subsequently, expression level of the selected miRNA was evaluated in the blood samples of gastritis patients to recognize the common miRNA between the GC and gastritis patients.Results:Analysis of 40 target genes by MCODE (installed in Cytoscape software) indicated 4 hub genes (WWP1, SKP2, KLHL42, and FBXO11) as a significant cluster in the PPI network related to miR-21, with Node Score Cutoff: 0.2, Degree Cutoff: 2 and K-Core: 2. In addition, the miRNA RT-qPCR results showed that, the expression level of miR-21 was significantly higher in gastritis group compared to the healthy group (p< 0.05).Conclusion:the present study clearly demonstrated the increasing level of blood miR-21 among the gastritis patients infected by H. pylori. Therefore, the altered miRNAs, especially overexpression of onco-miRs, may identify a potential link between miRNAs and pathogenesis of the H. pylori–related complications.Key Words: Blood Profiling, Gastric Cancer, H. pylori, Mir-21, Regulatory Network     

Cyclin D1 Serves as a Poor Prognostic Biomarker in Stage I Gastric Cancer

TNM stage still serves as the best prognostic marker in gastric cancer (GC). The next step is to find prognostic biomarkers that detect subgroups with different prognoses in the same TNM stage. In this study, the expression levels of epidermal growth factor receptor (EGFR) and cyclin D1 were assessed in 96 tissue samples, including non-tumorous tissue, adenoma, and carcinoma. Then, the prognostic impact of EGFR and cyclin D1 was retrospectively investigated in 316 patients who underwent R0 resection for GC. EGFR positivity increased as gastric tissue became malignant, and cyclin D1 positivity was increased in all the tumorous tissues. However, there was no survival difference caused by the EGFR positivity, while the cyclin D1-postive group had worse overall survival (OS) than the cyclin D1-negative group in stage I GC (10-year survival rate (10-YSR): 62.8% vs. 86.5%, p = 0.010). In subgroup analyses for the propensity score-matched (PSM) cohort, there were also significant differences in the OS according to the cyclin D1 positivity in stage I GC but not in stage II and III GC. Upon multivariate analysis, cyclin D1 positivity was an independent prognostic factor in stage I GC. In conclusion, cyclin D1 may be a useful biomarker for predicting prognosis in stage I GC.     

Lysosomal-Associated Protein Transmembrane 4 Beta-35 Overexpression Is a Novel Independent Prognostic Marker for Gastric Carcinoma

ObjectiveThe purpose of this work was to analyze the relationships between the expression status of Lysosomal-associated protein transmembrane-4 beta 35 (LAPTM4B-35) in cancerous tissues and clinicopathological characteristics and prognosis of the patients with gastric carcinoma (GC).MethodsThe GC samples from 157 patients in a discovery cohort and 148 patients in a testing cohort with follow-up data were used to validate the feasibility of expression of LAPTM4B-35 protein in predicting GC prognosis. Immunohistochemical staining was used to determine the expression of LAPTM4B-35 protein in precancerous gastric lesions and gastric carcinomas. The correlation between the expression of LAPTM4B-35 and clinicopathologic characteristics of patients with gastric carcinoma was analyzed using chi-square test. Univariate and multivariate analyses were performed to determine the association between LAPTM4B-35 expression and prognosis.ResultsLAPTM4B-35 expression was increased steadily in sequential stages of precancerous gastric lesions. Positive LAPTM4B-35 expression was more frequently detected in patients with distant metastasis (P = 0.023) and III+IV TNM stages (P = 0.042) in the discovery cohort. Kaplan-Meier survival curves and univariate analysis showed that expression of LAPTM4B-35 had a significant impact on overall survival of patients with gastric carcinoma in discovery cohort (P<0.001) and testing cohort (P = 0.001). LAPTM4B-35 expression was an independent prognostic indicator for the overall survival of patients with gastric carcinoma in both cohorts.ConclusionsThe present research demonstrated that LAPTM4B-35 over-expression was an independent factor in gastric carcinoma prognosis. LAPTM4B gene may be a useful target of interventions slowing the progression of precancerous gastric lesions and a new therapy method to improve the prognosis of gastric carcinoma.