Integrated Bioinformatic Analysis of a Competing Endogenous RNA Network Reveals a Prognostic Signature in Endometrial Cancer

Body mass index stratified meta-analysis of genome-wide association studies of polycystic ovary syndrome in women of European ancestry

BACKGROUND

Polycystic ovary syndrome (PCOS) is a complex multifactorial disorder with a substantial genetic component. However, the clinical manifestations of PCOS are heterogeneous with notable differences between lean and obese women, implying a different pathophysiology manifesting in differential body mass index (BMI). We performed a meta-analysis of genome-wide association study (GWAS) data from six well-characterised cohorts, using a case-control study design stratified by BMI, aiming to identify genetic variants associated with lean and overweight/obese PCOS subtypes.

RESULTS

The study comprised 254,588 women (5,937 cases and 248,651 controls) from individual studies performed in Australia, Estonia, Finland, the Netherlands and United States of America, and separated according to three BMI stratifications (lean, overweight and obese). Genome-wide association analyses were performed for each stratification within each cohort, with the data for each BMI group meta-analysed using METAL software. Almost half of the total study population (47%, n = 119,584) were of lean BMI (≤ 25 kg/m2). Two genome-wide significant loci were identified for lean PCOS, led by rs12000707 within DENND1A (P = 1.55 × 10-12) and rs2228260 within XBP1 (P = 3.68 × 10-8). One additional locus, LINC02905, was highlighted as significantly associated with lean PCOS through gene-based analyses (P = 1.76 × 10-6). There were no significant loci observed for the overweight or obese sub-strata when analysed separately, however, when these strata were combined, an association signal led by rs569675099 within DENND1A reached genome-wide significance (P = 3.22 × 10-9) and a gene-based association was identified with ERBB4 (P = 1.59 × 10-6). Nineteen of 28 signals identified in previous GWAS, were replicated with consistent allelic effect in the lean stratum. There were less replicated signals in the overweight and obese groups, and only 4 SNPs were replicated in each of the three BMI strata.

CONCLUSIONS

Genetic variation at the XBP1, LINC02905 and ERBB4 loci were associated with PCOS within unique BMI strata, while DENND1A demonstrated associations across multiple strata, providing evidence of both distinct and shared genetic features between lean and overweight/obese PCOS-affected women. This study demonstrated that PCOS-affected women with contrasting body weight are not only phenotypically distinct but also show variation in genetic architecture; lean PCOS women typically display elevated gonadotrophin ratios, lower insulin resistance, higher androgen levels, including adrenal androgens, and more favourable lipid profiles. Overall, these findings add to the growing body of evidence supporting a genetic basis for PCOS as well as differences in genetic patterns relevant to PCOS BMI-subtype.

Identification of lncRNAs associated with uterine corpus endometrial cancer prognosis based on the competing endogenous RNA network

Uterine Corpus Endometrial Carcinoma (UCEC) is one of the major malignant tumors of the female reproductive system. However, there are limitations in the currently available diagnostic approaches for UCEC. Long non-coding RNAs (lncRNAs) play important roles in regulating biological processes as competitive endogenous RNA (ceRNA) in tumors. To study the potential of lncRNAs as non-invasive diagnostic tumor markers, RNA-sequencing dataset of UCEC patients from The Cancer Genome Atlas was used to identify differentially expressed genes. A lncRNA-miRNA-mRNA ceRNA network was constructed by differentially expressed lncRNAs, miRNAs and miRNAs. Pathway enrichment and functional analysis for the mRNAs in the constructed ceRNA network provide the direction of future research for UCEC by demonstrating the most affected processes and pathways. Seven potential lncRNA biomarkers (C20orf56, LOC100144604, LOC100190940, LOC151534, LOC727677, FLJ35390, LOC158572) were validated in UCEC patients by quantitative real-time PCR. Notably, LOC100190940 and LOC158572 were identified as novel RNA molecules with unknown functions. Receiver operating characteristic (ROC) curve analysis demonstrated that the combined 7 lncRNAs had a high diagnostic value for UCEC patients with area under curve (AUC) of 0.941 (95% CI: 0.875-0.947). Our study highlights the potential of the validated 7 lncRNAs panel as diagnostic biomarkers in UCEC, providing new insights into the UCEC pathogenesis.

Long non coding RNA, C8orf49, a novel diagnostic and prognostic biomarker, enhances PTEN/FZD4-mediated cell growth and metastasis by sponging miR-1323 in endometriosis

Lack of sensitive biomarkers in the early stages of endometriosis (EMs) results in delayed diagnosis and intervention. Long non-coding RNAs (lncRNAs) have prognostic and diagnostic values in various diseases. However, the prognostic and diagnostic effects of lncRNAs on EMs have rarely been discussed in EMs. In this study, we found that lncRNA C8orf49 was stably overexpressed in EMs tissues/plasma, and its expression greatly influenced dysmenorrhea (p = 2.2605E-9) and the revised American Society for Reproductive Medicine stage (p = 0.040765) of EMs. Multivariate logistic regression results revealed that C8orf49 expression was an independent risk factor for EMs [p = 6.4997E-17, 95% confidence interval (CI) = 0.000559-0.023853]. In primary endometrial stromal cells (ESCs), inhibition of C8orf49 could impede the proliferation and metastasis of ESCs. C8orf49 influenced the expression of PTEN/FZD4 by absorbing miR-1323, thus controlling ESCs activity. The results of a subcutaneous endometriosis animal model showed that the inhibition of C8orf49 restrained endometrial growth. Overall, C8orf49 functioned as an activator of EMs pathogenesis via the C8orf49/miR-1323/PTEN/FZD4 axis.

Uncharacterized Proteins CxORFx: Subinteractome Analysis and Prognostic Significance in Cancers

Functions of about 10% of all the proteins and their associations with diseases are poorly annotated or not annotated at all. Among these proteins, there is a group of uncharacterized chromosome-specific open-reading frame genes (CxORFx) from the 'Tdark' category. The aim of the work was to reveal associations of CxORFx gene expression and ORF proteins' subinteractomes with cancer-driven cellular processes and molecular pathways. We performed systems biology and bioinformatic analysis of 219 differentially expressed CxORFx genes in cancers, an estimation of prognostic significance of novel transcriptomic signatures and analysis of subinteractome composition using several web servers (GEPIA2, KMplotter, ROC-plotter, TIMER, cBioPortal, DepMap, EnrichR, PepPSy, cProSite, WebGestalt, CancerGeneNet, PathwAX II and FunCoup). The subinteractome of each ORF protein was revealed using ten different data sources on physical protein-protein interactions (PPIs) to obtain representative datasets for the exploration of possible cellular functions of ORF proteins through a spectrum of neighboring annotated protein partners. A total of 42 out of 219 presumably cancer-associated ORF proteins and 30 cancer-dependent binary PPIs were found. Additionally, a bibliometric analysis of 204 publications allowed us to retrieve biomedical terms related to ORF genes. In spite of recent progress in functional studies of ORF genes, the current investigations aim at finding out the prognostic value of CxORFx expression patterns in cancers. The results obtained expand the understanding of the possible functions of the poorly annotated CxORFx in the cancer context.

Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis

Background

Uterine corpus endometrial carcinoma (UCEC) is a common malignant cancer type which affects the health of women worldwide. However, its molecular mechanism has not been elucidated.

Methods

To identify the hub modules and genes in UCEC associated with clinical phenotypes, the RNA sequencing data and clinical data of 543 UCEC samples were obtained from The Cancer Genome Atlas (TCGA) database and then subjected to weighted gene co-expression network analysis (WGCNA). To explore the potential biological function of the hub modules, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Genes differentially expressed in UCEC were screened according to TCGA data using the "gdcDEAnalysis" package in R (The R Foundation for Statistical Computing). After intersecting with hub genes, the shared genes were used for further survival analyses. The relationship between gene expression level and clinical phenotype was analyzed in the TCGA-UCEC cohort in The University of ALabama at Birmingham CANcer data analysis Portal and the Human Protein Atlas. The microarray data set GSE17025 was also analyzed to validate the gene expression profiles.

Results

There were 19 coexpression modules generated by WGCNA. Among them, 2 modules with 198 hub genes were highly correlated with clinical features (especially histologic grade and clinical stage). Meanwhile, 4,003 differentially expressed genes (DEGs) were screened out, and 164 DEGs overlapped with hub genes. Survival analyses revealed that high expression of GINS4 and low expression of ESR1 showed a trend of poor prognosis. Further analyses demonstrated that both messenger RNA (mRNA) and protein expression profiles of GINS4 and ESR1 were significantly associated with UCEC development and progression in TCGA and GSE17025 cohorts.

Conclusions

Based on the integrated bioinformatic analyses, our data indicated that GINS4 and ESR1 might serve as potential prognostic markers and targets for UCEC therapy.

Comprehensive bioinformatic analyses of lncRNA-mediated ceRNA network for uterine corpus endometrial carcinoma

Background

Given that long non-coding RNAs (lncRNAs) involved in the tumor initiation or progression of the endometrium and that competing endogenous RNA (ceRNA) plays an important role in increasingly more biological processes, lncRNA-mediated ceRNA is likely to function in the pathogenesis of uterine corpus endometrial carcinoma (UCEC). Our present study aimed to explore the potential molecular mechanisms for the prognosis of UCEC through a lncRNA-mediated ceRNA network.

Methods

The transcriptome profiles and corresponding clinical profiles of UCEC dataset were retrieved from Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) databases respectively. Differentially expressed genes (DEGs) in UCEC samples were identified via "Edge R" package. Then, an integrated bioinformatics analysis including functional enrichment analysis, tumor infiltrating immune cell (TIIC) analysis, Kaplan-Meier curve, Cox regression analysis were conducted to analyze the prognostic biomarkers.

Results

In the CPTAC dataset of UCEC, a ceRNA network comprised of 36 miRNAs, 123 lncRNAs and 124 targeted mRNAs was established, and 8 of 123 prognostic-related Differentially Expressed long noncoding RNAs (DElncRNAs) were identified. While in the TCGA dataset, a ceRNA network comprised of 38 miRNAs, 83 lncRNAs and 110 targeted mRNAs was established, and 2 of 83 prognostic-related DElncRNAs were identified. After filtered by risk grouping and Cox regression analysis, 10 prognostic-related lncRNAs including LINC00443, LINC00483, C2orf48, TRBV11-2, MEG-8 were identified. In addition, 33 survival-related Differentially Expressed messenger RNA (DEmRNAs) in two ceRNA networks were further validated in the Human Protein Atlas Portal (HPA) database. Finally, six lncRNA/miRNA/mRNA axes were established to elucidate prognostic regulatory roles in UCEC.

Conclusions

Several prognostic lncRNAs are identified and prognostic model of lncRNA-mediated ceRNA network is constructed, which promotes the understanding of UCEC development mechanisms and potential therapeutic targets.

Towards Personalized Medicine: Non-Coding RNAs and Endometrial Cancer

Endometrial cancer (EC) is the most frequent female cancer associated with excellent prognosis if diagnosed at an early stage. The risk factors on which clinical staging is based are constantly updated and genetic and epigenetic characteristics have recently been emerging as prognostic markers. The evidence shows that non-coding RNAs (ncRNAs) play a fundamental role in various biological processes associated with the pathogenesis of EC and many of them also have a prognosis prediction function, of remarkable importance in defining the therapeutic and surveillance path of EC patients. Personalized medicine focuses on the continuous updating of risk factors that are identifiable early during the EC staging to tailor treatments to patients. This review aims to show a summary of the current classification systems and to encourage the integration of various risk factors, introducing the prognostic role of non-coding RNAs, to avoid aggressive therapies where not necessary and to treat and strictly monitor subjects at greater risk of relapse.

The novel immune-related genes predict the prognosis of patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the main causes of cancer deaths globally. Immunotherapy is becoming increasingly important in the cure of advanced HCC. Thus it is essential to identify biomarkers for treatment response and prognosis prediction. We searched publicly available databases and retrieved 465 samples of genes from The Cancer Genome Atlas (TCGA) database and 115 tumor samples from Gene Expression Omnibus (GEO). Meanwhile, we used the ImmPort database to determine the immune-related genes as well. Weighted gene correlation network analysis, Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to identify the key immune related genes (IRGs) which are closely related to prognosis. Gene set enrichment analysis (GSEA) was implemented to explore the difference of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway between Immune high- and low-risk score groups. Finally, we made a prognostic nomogram including Immune-Risk score and other clinicopathologic factors. A total of 318 genes from prognosis related modules were identified through weighted gene co-expression network analysis (WGCNA). 46 genes were strongly linked to prognosis after univariate Cox analysis. We constructed a seven genes prognostic signature which showed powerful prediction ability in both training cohort and testing cohort. 16 significant KEGG pathways were identified between high- and low- risk score groups using GSEA analysis. This study identified and verified seven immune-related prognostic biomarkers for the patients with HCC, which have potential value for immune modulatory and therapeutic targets.

Long non‑coding RNA LINC00491 promotes proliferation and inhibits apoptosis in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor in the human digestive system, which affects the physical and mental health of the patient. Long non‑coding (lnc)RNAs have been revealed to play an important role in human malignant tumors. Moreover, long intergenic non‑protein coding RNA 491 (LINC00491) is a newly discovered lncRNA that can affect the prognosis of cancer. The present study aimed to explore the expression of LINC00491 in ESCC tissues and cells. The reverse transcription‑quantitative PCR results suggested that LINC00491 was upregulated in ESCC tissues and cells. LINC00491 expression in esophageal squamous cell carcinoma cells were knocked down. Cell Counting Kit‑8, wound healing, Transwell and apoptosis assays were performed to detect the effects of LINC00491 knockdown on cell biological behavior. The results showed that lower expression of LINC00491 resulted in decreased cell proliferation and migration and increased the apoptosis rate. Therefore, the present results indicated that lncRNA LINC00491 promoted the biological processes of ESCC, and thus LINC00491 may be a potential therapeutic target for ESCC.

Non-Coding RNAs as Prognostic Markers for Endometrial Cancer

Endometrial cancer (EC) has been classified over the years, for prognostic and therapeutic purposes. In recent years, classification systems have been emerging not only based on EC clinical and pathological characteristics but also on its genetic and epigenetic features. Noncoding RNAs (ncRNAs) are emerging as promising markers in several cancer types, including EC, for which their prognostic value is currently under investigation and will likely integrate the present prognostic tools based on protein coding genes. This review aims to underline the importance of the genetic and epigenetic events in the EC tumorigenesis, by expounding upon the prognostic role of ncRNAs.

Identification of Functional lncRNAs Associated With Ovarian Endometriosis Based on a ceRNA Network

Background

Endometriosis is a common gynecological disease affecting women of reproductive age; however, the mechanisms underlying this condition are not fully clear. The aim of this study was to identify functional long non-coding RNAs (lncRNAs) associated with ovarian endometriosis for potential use as biomarkers and therapeutic targets.

Methods

RNA-seq profiles of paired ectopic (EC) and eutopic (EU) endometrial samples from patients with ovarian endometriosis were downloaded from the publicly available Gene Expression Omnibus (GEO) database. Bioinformatics algorithms were used to construct a network of ovarian endometriosis-related competing endogenous RNAs (ceRNAs) and to detect functional lncRNAs.

Results

A total of 4,213 mRNAs, 1,474 lncRNAs, and 221 miRNAs were identified as being differentially expressed between EC and EU samples, and an ovarian endometriosis-related ceRNA network was constructed through analysis of these differentially expressed RNAs. H19 and GS1-358P8.4 were identified as key ovarian endometriosis-related lncRNAs through topological feature analysis, and RP11-96D1.10 was identified using a random walk with restart algorithm.

Conclusion

Based on bioinformatics analysis of a ceRNA network, we identified the lncRNAs H19, GS1-358P8.4, and RP11-96D1.10 as being strongly associated with ovarian endometriosis. These three lncRNAs hold potential as targets for medical therapy and as diagnostic biomarkers. Further studies are needed to elucidate the detailed biological function of these lncRNAs in the pathogenesis of endometriosis.

A novel risk score system of immune genes associated with prognosis in endometrial cancer

Background

Endometrial cancer was the commonest gynecological malignancy in developed countries. Despite striking advances in multimodality management, however, for patients in advanced stage, targeted therapy still remained a challenge. Our study aimed to investigate new biomarkers for endometrial cancer and establish a novel risk score system of immune genes in endometrial cancer.

Methods

The clinicopathological characteristics and gene expression data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) of immune genes between tumors and normal tissues were identified. Protein–protein interaction (PPI) network of immune genes and transcriptional factors was integrated and visualized in Cytoscape. Univariate and multivariate analysis were employed for key genes to establish a new risk score system. Receiver operating characteristic (ROC) curve and survival analysis were performed to investigate the prognostic value of the model. Association between clinical characteristics and the model was analyzed by logistic regression. For validation, we identified 34 patients with endometrial cancer from Fudan University Shanghai Cancer Center (FUSCC). We detected 14-genes mRNA expression and calculated the risk scores of each patients and we performed survival analysis between the high-risk group and the low-risk group.

Results

23 normal tissues and 552 tumor tissues were obtained from TCGA database. 410 immune-related DEGs was identified by difference analysis and correlation analysis. KEGG and GO analysis revealed these DEGs were enriched in cell adhesion, chemotaxis, MAPK pathways and PI3K-Akt signaling pathway, which might regulate tumor progression and migration. All genes were screened for risk model construction and 14 hub immune-related genes (HTR3E, CBLC, TNF, PSMC4, TRAV30, PDIA3, FGF8, PDGFRA, ESRRA, SBDS, CRHR1, LTA, NR2F1, TNFRSF18) were prognostic in endometrial cancer. The area under the curve (AUC) was 0.787 and the high-risk group estimated by the model possessed worse outcome (P < 0.001). Multivariate analysis suggested that the model was indeed an independent prognostic factor (high-risk vs. low-risk, HR = 1.14, P < 0.001). Meanwhile, the high-risk group was prone to have higher grade (P = 0.002) and advanced clinical stage (P = 0.018). In FUSCC validation set, the high-risk group had worse survival than the low-risk group (P < 0.001).

Conclusions

In conclusion, the novel risk model of immune genes had some merits in predicting the prognosis of endometrial cancer and had strong correlation with clinical outcomes. Furthermore, it might provide new biomarkers for targeted therapy in endometrial cancer.

Construction of a metastasis-associated ceRNA network reveals a prognostic signature in lung cancer

Background

Lung cancer is the most common cancer worldwide, and metastasis is the leading cause of lung cancer related death. However, the molecular network involved in lung cancer metastasis remains incompletely described. Here, we aimed to construct a metastasis-associated ceRNA network and identify a lncRNA prognostic signature in lung cancer.

Methods

RNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and gene set enrichment analysis (GSEA) were performed to investigate the function of these genes. Using Cox regression analysis, we found that a 6 lncRNA signature may serve as a candidate prognostic factor in lung cancer. Finally, we used Transwell assays with lung cancer cell lines to verify that LINC01010 acts as a tumor suppressor.

Results

We identified 1249 differentially expressed (DE) mRNAs, 440 DE lncRNAs and 26 DE miRNAs between nonmetastatic and metastatic lung cancer tissues. GO and KEGG analyses confirmed that the identified DE mRNAs are involved in lung cancer metastasis. Using bioinformatics tools, we constructed a metastasis-associated ceRNA network for lung cancer that includes 117 mRNAs, 23 lncRNAs and 22 miRNAs. We then identified a 6 lncRNA signature (LINC01287, SNAP25-AS1, LINC00470, AC104809.2, LINC00645 and LINC01010) that had the greatest prognostic value for lung cancer. Furthermore, we found that suppression of LINC01010 promoted lung cancer cell migration and invasion.

Conclusions

This study might provide insight into the identification of potential lncRNA biomarkers for diagnosis and prognosis in lung cancer.

LncRNA LINC00483 promotes gastric cancer development through regulating MAPK1 expression by sponging miR-490-3p

Background

Previous studies have shown that long noncoding RNA (lncRNA) LINC00483 was aberrantly expressed in human cancers, including gastric cancer. However, the regulatory mechanism of this lncRNA in gastric cancer remains largely unknown. The present study aimed to investigate the effect of LINC00483 on gastric cancer development and explore the potential regulatory network of LINC00483/microRNA (miR)-490-3p/mitogen-activated protein kinase 1 (MAPK1).

Methods

Thirty patients with gastric cancer were recruited for tissues collection. The expression levels of LINC00483, miR-490-3p and MAPK1 were detected by quantitative real-time polymerase chain reaction or western blot. Cell viability, apoptosis, migration and invasion were determined by MTT, flow cytometry, transwell assays and western blot, respectively. The target association between miR-490-3p and LINC00483 or MAPK1 was confirmed by luciferase reporter assay. Xenograft model was established to assess the function of LINC00483 in vivo.

Results

LINC00483 and MAPK1 levels were increased in gastric cancer tissues and cells. Knockdown of LINC00483 or MAPK1 inhibited cells viability, migration and invasion but promoted apoptosis in gastric cancer cells. Moreover, MAPK1 overexpression attenuated the effect of LINC00483 knockdown on gastric cancer development. LINC00483 could increase MAPK1 expression by competitively sponging miR-490-3p. miR-490-3p overexpression suppressed gastric cancer development, which was abated by introduction of LINC00483. Besides, inhibition of LINC00483 decreased xenograft tumor growth by regulating miR-490-3p/MAPK1 axis.

Conclusion

Knockdown of LINC00483 inhibited gastric cancer development in vitro and in vivo by increasing miR-490-3p and decreasing MAPK1, elucidating a novel mechanism for understanding the development of gastric cancer.

[A comprehensive analysis of potential prognostic biomarkers for MYCN-amplified neuroblastoma]

OBJECTIVE

To study the differentially expressed mRNAs between MYCN-amplified neuroblastoma (NB) and non-amplified NB, to screen out the genes which can be used to predict the prognosis of MYCN-amplified NB, and to analyze their value in predicting prognosis.

METHODS

NB transcriptome data and the clinical data of children were obtained from the TARGET database. According to the presence or absence of MYCN amplification, the children were divided into two groups: MYCN amplification (n=33) and non-MYCN amplification (n=121). The expression of mRNAs was compared between the two groups to obtain differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analysis was performed to investigate the main functions of DEGs. The Cox proportional-hazards regression model analysis was used to investigate the genes influencing the prognosis of MYCN-amplified NB. The children were divided into a high-risk group (n=77) and a low-risk group (n=77) based on the median of risk score. A survival analysis was used to compare survival rate between the two groups. The receiver operating characteristic (ROC) curve was used to investigate the value of risk score in predicting the prognosis of children with MYCN-amplified NB.

RESULTS

A total of 582 DEGs were screened out, and they were involved in important biological functions such as ribosome composition, expression of cell adhesion molecules, and activity of membrane receptor protein. The multivariate Cox regression model analysis showed that FLVCR2, SCN7A, PRSS12, NTRK1, and XAGE1A genes had a marked influence on the prognosis of the children with NB in the MYCN amplification group (P<0.05). The survival analysis showed that the high-risk group had a significantly lower overall survival rate than the low-risk group (P<0.05). The ROC curve analysis showed that risk score had a certain value in predicting the prognosis of the children with NB in the MYCN amplification group (P<0.05), with an area under the ROC curve of 0.729, an optimal cut-off value of 1.316, a sensitivity of 53.2%, and a specificity of 84.4%.

CONCLUSIONS

The mRNA expression of FLVCR2, SCN7A, PRSS12, NTRK1, and XAGE1A genes can be used as biomarkers to predict the prognosis of MYCN-amplified NB, which can help to refine clinical risk stratification.

[A comprehensive analysis of potential prognostic biomarkers for MYCN-amplified neuroblastoma]

OBJECTIVE

To study the differentially expressed mRNAs between MYCN-amplified neuroblastoma (NB) and non-amplified NB, to screen out the genes which can be used to predict the prognosis of MYCN-amplified NB, and to analyze their value in predicting prognosis.

METHODS

NB transcriptome data and the clinical data of children were obtained from the TARGET database. According to the presence or absence of MYCN amplification, the children were divided into two groups: MYCN amplification (n=33) and non-MYCN amplification (n=121). The expression of mRNAs was compared between the two groups to obtain differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analysis was performed to investigate the main functions of DEGs. The Cox proportional-hazards regression model analysis was used to investigate the genes influencing the prognosis of MYCN-amplified NB. The children were divided into a high-risk group (n=77) and a low-risk group (n=77) based on the median of risk score. A survival analysis was used to compare survival rate between the two groups. The receiver operating characteristic (ROC) curve was used to investigate the value of risk score in predicting the prognosis of children with MYCN-amplified NB.

RESULTS

A total of 582 DEGs were screened out, and they were involved in important biological functions such as ribosome composition, expression of cell adhesion molecules, and activity of membrane receptor protein. The multivariate Cox regression model analysis showed that FLVCR2, SCN7A, PRSS12, NTRK1, and XAGE1A genes had a marked influence on the prognosis of the children with NB in the MYCN amplification group (P<0.05). The survival analysis showed that the high-risk group had a significantly lower overall survival rate than the low-risk group (P<0.05). The ROC curve analysis showed that risk score had a certain value in predicting the prognosis of the children with NB in the MYCN amplification group (P<0.05), with an area under the ROC curve of 0.729, an optimal cut-off value of 1.316, a sensitivity of 53.2%, and a specificity of 84.4%.

CONCLUSIONS

The mRNA expression of FLVCR2, SCN7A, PRSS12, NTRK1, and XAGE1A genes can be used as biomarkers to predict the prognosis of MYCN-amplified NB, which can help to refine clinical risk stratification.

Identification of a Multi-RNA-Type-Based Signature for Recurrence-Free Survival Prediction in Patients with Uterine Corpus Endometrial Carcinoma

Uterine corpus endometrial carcinoma (UCEC) is one of the leading causes of death from gynecological cancer due to the high recurrence rate. A recent study indicated that molecular biomarkers can enhance the recurrence prediction power if they were integrated with clinical information. In this study, we attempted to identify a new multi-RNA-type-based molecular biomarker for predicting the recurrence risk and recurrence-free survival (RFS). Matched mRNA (including lncRNA) and miRNA RNA-sequencing data from 463 UCEC patients (n = 75, recurrent; n = 388, non-recurrent) were downloaded from The Cancer Genome Atlas database. LASSO (least absolute shrinkage and selection operator) analysis was used to screen the optimal combination of prognostic RNAs and then the risk score model was constructed. Moreover, the molecular mechanisms of prognostic RNAs were explored by establishing various interaction networks based on corresponding predictive databases. A multi-RNA-type-based signature (including three miRNAs: hsa-miR-6511b, hsa-miR-184, hsa-miR-4461; three lncRNAs: ENO1-IT1, MCCC1-AS1, AATBC; and 7 mRNAs: EPPK1, ASB9, BDNF, CYP11A1, ECEL1, EN2, F13A1) was developed for the prediction of RFS. The risk scoring system established by these signature genes was effective for the discrimination of the 5-year RFS in the high-risk from low-risk patients in the training [an area under the receiver operating characteristic curve (AUC) = 0.960], validation (AUC = 0.863), and entire datasets (AUC = 0.873). This risk score model was also proved to be a more excellent, independent prognostic discriminator than the single-RNA-type (overall AUC: 0.947 vs. 0.677, lncRNAs; 0.709, miRNAs; 0.899, mRNAs) and clinical staging (overall AUC: 0.947 vs. 0.517). Furthermore, the downstream mechanisms for some prognostic miRNAs or lncRNAs (HAND2-AS1-hsa-miR-6511b-APC2, PAX8-AS1-hsa-miR-4461-TNIK and MCCC1-AS1/ENO1-IT1-TNIK) were newly predicted based on the coexpression or competitive endogenous RNA theories. In conclusion, our findings may provide novel biomarkers for recurrence prediction and targets for treatment of UCEC.

The long noncoding RNA LINC00483 promotes lung adenocarcinoma progression by sponging miR-204-3p

BACKGROUND

The expression of the long noncoding RNA LINC00483 is upregulated in lung adenocarcinoma (LUAD). However, its role in the progression of LUAD and the underlying mechanisms remain elusive.

METHODS

The expressions of LINC00483 and miR-204-3p were determined using quantitative real-time PCR. The correlation between the clinicopathological characteristics of LUAD patients and LINC00483 expression was analyzed using Pearson's χ2 test. A549 and PC-9 cells were transfected with small interfering RNA (siRNA) that specially targeting LINC00483 to assess the impact of its knockdown. Cell proliferation was assessed using the Cell Counting Kit-8 and clone forming assays. Cell migration and cell invasion were evaluated using a transwell assay. The levels of Snail, E-cadherin, N-cadherin and ETS1 proteins were determined via western blotting. The interaction between LINC00483 and miR-204-3p was analyzed using dual-luciferase, fluorescence in situ hybridization and RNA immunoprecipitation.

RESULTS

LINC00483 was upregulated in LUAD tissues and cell lines. Higher LINC00483 levels closely correlated to shorter survival times, advanced TNM stage, larger tumor size and positive lymph node metastasis. Cell proliferation, migration and invasion were suppressed after LINC00483 knockdown. LINC00483 mainly localized in the cytoplasm, where it acted as a sponge of miR-204-3p. ETS1 was validated as a downstream target of miR-204-3p and is thus regulated by LINC00483.

CONCLUSION

This study demonstrated that LINC00483 facilitates the proliferation, migration and invasion of LUAD cells by acting as a sponge for miR-204-3p, which in turn regulates ETS1.

Identification and Function Analysis of a Five-Long Noncoding RNA Prognostic Signature for Endometrial Cancer Patients

This study aimed to construct a long noncoding RNA (lncRNA)-based prognostic signature to improve the survival prediction for endometrial cancer (EC) patients and guide individualized treatments. mRNA and miRNA sequencing and clinical data of 526 patients with EC (randomized to training or validation set, n = 263) were collected from The Cancer Genome Atlas database. Differentially expressed genes (DEGs), differentially expressed lncRNAs (DELs), and differentially expressed miRNAs (DEMs) were identified between 263 EC samples and 33 normal controls. Univariate and multivariate Cox regression analyses identified five DELs (LINC00475, LINC01352, MIR503HG, KCNMB2-AS1, and LINC01143) that were overall survival related. The Kaplan-Meier curve showed that the risk score model established by these five DELs can significantly distinguish the survival ratio of patients at high risk from those at low risk. The receiver operating characteristic curve indicated that this risk score exhibited good survival prediction performance, with the area under the curve of 0.978. In addition, this risk score was independent of other clinical factors. Stratification analysis based on two independent prognostic clinical factors (histologic grade and recurrence status) demonstrated that the high-risk score was still a poor prognostic factor for patients with histologic grade 3, recurrence or nonrecurrence status. In nomogram model, the risk score was one of the main contributions to survival rates, and its Harrell's concordance index was higher than the other two independent clinical factors, although all lower than the combined. Furthermore, mechanism analyses showed that these lncRNAs functioned by coexpressing with DEGs (i.e., LINC00475-PTGDR, LINC01352/MIR503HG-BACH2, KCNMB2-AS1-PCSK9, LINC01143-NUF2/PTTG1) or as a competing endogenous RNA of DEMs to regulate DEGs (LINC00475-miR-4728-PTGDR, MIR503HG-miR-3170-BACH2). In conclusion, our novel risk score system may be a promising prognostic biomarker to guide personalized treatment for EC patients and it can add prognostic value for current clinical system.