Comprehensive analysis of lncRNA expression profiles reveals a novel lncRNA signature to discriminate nonequivalent outcomes in patients with ovarian cancer

Identification of telomere-related lncRNAs and immunological analysis in ovarian cancer

Background

Ovarian cancer (OC) is a global malignancy characterized by metastatic invasiveness and recurrence. Long non-coding RNAs (lncRNAs) and Telomeres are closely connected with several cancers, but their potential as practical prognostic markers in OC is less well-defined.

Methods

Relevant mRNA and clinical data for OC were sourced from The Cancer Genome Atlas (TCGA) database. The telomere-related lncRNAs (TRLs) prognostic model was established by univariate/LASSO/multivariate regression analyses. The effectiveness of the TRLs model was evaluated and measured via the nomogram. Additionally, immune infiltration, tumor mutational load (TMB), and drug sensitivity were evaluated. We validated the expression levels of prognostic genes. Subsequently, PTPRD-AS1 knockdown was utilized to perform the CCK8 assay, colony formation assay, transwell assay, and wound healing assay of CAOV3 cells.

Results

A six-TRLs prognostic model (PTPRD-AS1, SPAG5-AS1, CHRM3-AS2, AC074286.1, FAM27E3, and AC018647.3) was established, which can effectively predict patient survival rates and was successfully validated using external datasets. According to the nomogram, the model could effectively predict prognosis. Furthermore, we detected the levels of regulatory T cells and M2 macrophages were comparatively higher in the high-risk TRLs group, but the levels of activated CD8 T cells and monocytes were the opposite. Finally, the low-risk group was more sensitive to anti-cancer drugs. The mRNA levels of PTPRD-AS1, SPAG5-AS1, FAM27E3, and AC018647.3 were significantly over-expressed in OC cell lines (SKOV3, A2780, CAOV3) in comparison to normal IOSE-80 cells. AC074286.1 were over-expressed in A2780 and CAOV3 cells and CHRM3-AS2 only in A2780 cells. PTPRD-AS1 knockdown decreased the proliferation, cloning, and migration of CAOV3 cells.

Conclusion

Our study identified potential biomarkers for the six-TRLs model related to the prognosis of OC.

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis

Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.

Construction of Ovarian Cancer Prognostic Model Based on the Investigation of Ferroptosis-Related lncRNA

(1) Background: Ovarian cancer (OV) has the high mortality rate among gynecological cancers worldwide. Inefficient early diagnosis and prognostic prediction of OV leads to poor survival in most patients. OV is associated with ferroptosis, an iron-dependent form of cell death. Ferroptosis, believed to be regulated by long non-coding RNAs (lncRNAs), may have potential applications in anti-cancer treatments. In this study, we aimed to identify ferroptosis-related lncRNA signatures and develop a novel model for predicting OV prognosis. (2) Methods: We downloaded data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, and Gene Expression Omnibus (GEO) databases. Prognostic lncRNAs were screened by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was constructed. The model's predictive ability was evaluated by Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) curves. The expression levels of these lncRNAs included in the model were examined in normal and OV cell lines using quantitative reverse transcriptase polymerase chain reaction. (3) Results: We constructed an 18 lncRNA prognostic prediction model for OV based on ferroptosis-related lncRNAs from TCGA patient samples. This model was validated using TCGA and GEO patient samples. KM analysis showed that the prognostic model was able to significantly distinguish between high- and low-risk groups, corresponding to worse and better prognoses. Based on the ROC curves, our model shows stronger prediction precision compared with other traditional clinical factors. Immune cell infiltration, immune checkpoint expression levels, and Tumor Immune Dysfunction and Exclusion analyses are also insightful for OV immunotherapy. (4) Conclusions: The prognostic model constructed in this study has potential for improving our understanding of ferroptosis-related lncRNAs and providing a new tool for prognosis and immune response prediction in patients with OV.

A Platinum Resistance-Related lncRNA Signature for Risk Classification and Prognosis Prediction in Patients with Serous Ovarian Cancer

Accurate risk stratification for patients with serous ovarian cancer (SOC) is pivotal for treatment decisions. In this study, we identified a lncRNA-based signature for predicting platinum resistance and prognosis stratification for SOC patients. We analyzed the RNA-sequencing data and the relevant clinical information of 295 SOC samples obtained from The Cancer Genome Atlas (TCGA) database and 180 normal ovarian tissues from the Genotype-Tissue Expression (GTEx) database. A total of 284 differentially expressed lncRNAs were screened out between platinum-sensitive and platinum-resistant groups by univariate Cox regression analysis. Then, a signature consisting of eight prognostic lncRNAs was used to construct a lncRNA score model by least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The ROC analysis showed that this signature had a good predictive performance for chemotherapy response in the training set (AUC = 0.8524) and the testing and whole sets with 0.8142 and 0.8393 of AUC, respectively. Dichotomized by the risk score of lncRNAs (lncScore), the high-risk patients showed significantly shorter progression-free survival (PFS) and overall survival (OS). Based on the final Cox model, a nomogram comprising the 8-lncRNA signature and 3 clinicopathological risk factors was then established for clinical application to predict the 1, 2, and 3-year PFS of SOC patients. The gene set enrichment analysis (GSEA) revealed that genes in the high-risk group were active in ATP synthesis, coupled electron transport, and mitochondrial respiratory chain complex assembly. Overall, our findings demonstrated the potential clinical significance of the 8-lncRNA-based classifier as a novel biomarker for outcome prediction and therapy decisions in SOC patients with platinum treatment.

LncRNA ALMS1-IT1 is a novel prognostic biomarker and correlated with immune infiltrates in colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most serious cancers. It is important to accurately predict prognosis and provide individualized treatment. Evidence suggests that clinicopathological features and immune status of the body are related to the occurrence and development of cancer. Expression of long non-coding RNA (LncRNA) ALMS1 intronic transcript 1 (ALMS1-IT1) is observed in some cancer types, and we believe that it may have the potential to serve as a marker of COAD. Therefore, we used the data obtained from the cancer genome atlas (TCGA) database to prove the relationship between ALMS1-IT1 and COAD. Wilcoxon rank sum test, Chi-square test, Fisher exact test and logistic regression were used to evaluate relationships between clinical-pathologic features and ALMS1-IT1 expression. Receiver operating characteristic curves were used to describe binary classifier value of ALMS1-IT1 using area under curve score. Kaplan-Meier method and Cox regression analysis were used to evaluate factors contributing to prognosis. Gene oncology (GO) and (Kyoto Encyclopedia of Genes and Genomes) KEGG enrichment analysis were used to predict the function of differentially expressed genes associated with ALMS1-IT1. Gene set enrichment analysis (GSEA) was used to predict canonical pathways associated with ALMS1-IT1.Immune infiltration analysis was performed to identify the significantly involved functions of ALMS1-IT1. Starbase database was used to predict miRNAs and RNA binding proteins (RBPs) that may interact with ALMS1-IT1. Increased ALMS1-IT1 expression in COAD was associated with N stage (P < .001), M stage (P = .003), Pathologic stage (P = .002), and Primary therapy outcome (P = .009). Receiver operating characteristic curve suggested the significant diagnostic and prognostic ability of ALMS1-IT1 (area under curve = 0.857). High ALMS1-IT1 expression predicted a poorer overall-survival (P = .005) and poorer progression-free interval (PFI) (P = .012), and ALMS1-IT1 expression was independently correlated with PFI in COAD patients (hazard ratio (HR) :1.468; 95% CI: 1.029-2.093; P =.034) (HR: 1.468; 95% CI: 1.029-2.093; P = .034). GO, KEGG, GSEA, and immune infiltration analysis showed that ALMS1-IT1 expression was correlated with regulating the function of DNA and some types of immune infiltrating cells. ALMS1-IT1 expression was significantly correlated with poor survival and immune infiltrations in COAD, and it may be a promising prognostic biomarker in COAD.

Long Noncoding RNA_Plasmacytoma Variant Translocation 1 (LncRNA_PVT1) Regulates the Biological Behaviors of Bone Marrow Mesenchymal Stem Cells (BMSC) to Promote Cervical Cancer Growth Through miR-266

To discuss the biological mechanism of lncRNA PVT1 on regulating BMSC and prompting the HPV growth through miR-266. The correlation between presentation of lncRNA PVT1 and HPV was analyzed. The HPV cells were transfected with lncRNA PVT1. The transfection efficiency was tested. These cells were detected through CCK8 test. The action of lncRNA PVT1 on the HPV cells’ cycle and apoptotic rate was tested with FCM. The correlation between lncRNA PVT1 and miR-266 was analyzed through Pearson correlation analysis. The presentation of mRNA was tested by RT-PCR. The presentation level of miR-266 in BMSC and HPV cells was analyzed through PCR. The presentation of lncRNA PVT1 in HPV tissue and cell was increased notably. The apoptotic rate was increased and proliferative rate was reduced through the transfection with lncRNA PVT1. The miR-266 was one kind of gene with differential presentation. It was reduced notably in HPV cells. There was negative correlation between lncRNA PVT1 and miR-266. The proliferation of HPV cells was related closely with the increasing of the presentation of lncRNA PVT1. The gene presentation could be affected through the targeted interaction with miR-266. It was conducive to BMSC growing into HPV. It could provide target spot for the prevention and control for HPV.

Identification of genomic instability related lncRNA signature with prognostic value and its role in cancer immunotherapy in pancreatic cancer

Background: Increasing evidence suggested the critical roles of lncRNAs in the maintenance of genomic stability. However, the identification of genomic instability-related lncRNA signature (GILncSig) and its role in pancreatic cancer (PC) remains largely unexplored.

Methods: In the present study, a systematic analysis of lncRNA expression profiles and somatic mutation profiles was performed in PC patients from The Cancer Genome Atlas (TCGA). We then develop a risk score model to describe the characteristics of the model and verify its prediction accuracy. ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and CIBERSORT analysis were employed to reveal the correlation between tumor immune microenvironment, immune infiltration, immune checkpoint blockade (ICB) therapy, and GILncSig in PC.

Results: We identified 206 GILnc, of which five were screened to develop a prognostic GInLncSig model. Multivariate Cox regression analysis and stratified analysis revealed that the prognostic value of the GILncSig was independent of other clinical variables. Receiver operating characteristic (ROC) analysis suggested that GILncSig is better than the existing lncRNA-related signatures in predicting survival. Additionally, the prognostic performance of the GILncSig was also found to be favorable in patients carrying wild-type KRAS, TP53, and SMAD4. Besides, a nomogram exhibited appreciable reliability for clinical application in predicting the prognosis of patients. Finally, the relationship between the GInLncSig model and the immune landscape in PC reflected its application value in clinical immunotherapy.

Conclusion: In summary, the GILncSig identified by us may serve as novel prognostic biomarkers, and could have a crucial role in immunotherapy decisions for PC patients.

An Immune-Related lncRNA Pairing Model for Predicting the Prognosis and Immune-Infiltrating Cell Condition in Human Ovarian Cancer

Ovarian cancer is the second common cancer among the gynecological tumors. It is difficult to be found and diagnosed in the early stage and easy to relapse due to chemoresistance and deficiency in choices of treatment. Therefore, future exploring the biomarkers for diagnosis, treatment, and prognosis prediction of ovarian cancer is significant to women in the world. We downloaded data from TCGA and GTEx and used R “limma” package for analyzing the differentially expressed immune-related lncRNA in ovarian cancer and finally got 7 downregulated and 171 upregulated lncRNA. Then, we paired the differentially expressed immune-related lncRNA and constructed a novel lncRNA pairing model containing 7 lncRNA pairs. Based on the cut-off point with the highest AUC value, 102 patients were selected in high-risk group and 272 in low-risk group. The KM analysis suggested that the patients in the low-risk group had a longer overall survival. Future analysis showed the correlations between risk scores and clinicopathological parameters and infiltrating immune cells. In conclusion, we identified an immune-related lncRNA pairing model for predicting the prognosis and immune-infiltrating cell condition in human ovarian cancer, which thus further can instruct immunotherapy.

A comprehensive analysis of avian lymphoid leukosis-like lymphoma transcriptomes including identification of LncRNAs and the expression profiles

Avian lymphoid leukosis-like (LL-like) lymphoma has been observed in some experimental and commercial lines of chickens that are free of exogenous avian leukosis virus. Reported cases of avian lymphoid leukosis-like lymphoma incidences in the susceptible chickens are relatively low, but the apathogenic subgroup E avian leukosis virus (ALV-E) and the Marek’s disease vaccine, SB-1, significantly escalate the disease incidence in the susceptible chickens. However, the underlying mechanism of tumorigenesis is poorly understood. In this study, we bioinformatically analyzed the deep RNA sequences of 6 lymphoid leukosis-like lymphoma samples, collected from susceptible chickens post both ALV-E and SB-1 inoculation, and identified a total of 1,692 novel long non-coding RNAs (lncRNAs). Thirty-nine of those novel lncRNAs were detected with altered expression in the LL-like tumors. In addition, 13 lncRNAs whose neighboring genes also showed differentially expression and 2 conserved novel lncRNAs, XLOC_001407 and XLOC_022595, may have previously un-appreciated roles in tumor development in human. Furthermore, 14 lncRNAs, especially XLOC_004542, exhibited strong potential as competing endogenous RNAs via sponging miRNAs. The analysis also showed that ALV subgroup E viral gene Gag/Gag-pol and the MD vaccine SB-1 viral gene R-LORF1 and ORF413 were particularly detectable in the LL-like tumor samples. In addition, we discovered 982 novel lncRNAs that were absent in the current annotation of chicken genome and 39 of them were aberrantly expressed in the tumors. This is the first time that lncRNA signature is identified in avian lymphoid leukosis-like lymphoma and suggests the epigenetic factor, lncRNA, is involved with the avian lymphoid leukosis-like lymphoma formation and development in susceptible chickens. Further studies to elucidate the genetic and epigenetic mechanisms underlying the avian lymphoid leukosis-like lymphoma is indeed warranted.

Identification of A Risk Signature Based on Lactic Acid Metabolism-Related LncRNAs in Patients With Esophageal Squamous Cell Carcinoma

Lactic acid, formerly thought of as a byproduct of glycolysis or a metabolic waste produced, has now been identified as a key regulator of cancer growth, maintenance, and progression. However, the results of investigations on lactic acid metabolism-related long non-coding RNAs (LRLs) in esophageal squamous cell carcinoma (ESCC) remain inconclusive. In this study, univariate Cox regression analysis was carried out in the TCGA cohort, and 9 lncRNAs were shown to be significantly associated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis were then used in the GEO cohort. 6 LRLs were identified as independent prognostic factors for ESCC patients used to construct a prognostic risk-related signature subsequently. Two groups were formed based on the middle value of risk scores: a low-risk group and a high-risk group. Following that, we conducted Kaplan-Meier survival analysis, which revealed that the high-risk group had a lower survival probability than the low-risk group in both GEO and TCGA cohorts. On multivariate Cox regression analysis, the prognostic signature was shown to be independent prognostic factor, and it was found to be a better predictor of the prognosis of ESCC patients than the currently widely used grading and staging approaches. The established nomogram can be conveniently applied in the clinic to predict the 1-, 3-, and 5- year survival rates of patients. There was a significant link found between the 6 LRLs-based prognostic signature and immune-cell infiltration, tumor microenvironment (TME), tumor somatic mutational status, and chemotherapeutic treatment sensitivity in the study population. Finally, we used GTEx RNA-seq data and qRT-PCR experiments to verify the expression levels of 6 LRLs. In conclusion, we constructed a prognostic signature which could predict the prognosis and immunotherapy response of ESCC patients.

LncRNA Neu Mediates Bone Marrow Mesenchymal Stem Cells (BMSCs) Growth and Promotes Cervical Cancer Progression Through Suppression of MicroRNA-625

The tumorigenesis mechanism of cervical cancer (CC) is complicated as several pathways deserve exploration. LncRNAs are recently highlighted to be involved in various biological processes. The role of bone marrow mesenchymal stem cells (BMSCs) in tumor regulation is recently investigated. Herein, we aimed to explore the interaction between lncRNA Neu and microRNA (miR)-625 and BMSCs in CC. Expression levels of lncRNA Neu and miR-625 in CC cells and BMSCs were determined by RT-qPCR. The relationship between lncRNA Neu and miR-625 was analyzed by Pearson correlation analysis. After cancer cells were transfected with siRNA-Neu, CCK-8 assay and clone formation assay were conducted to determine cell proliferation and viability. LncRNA Neu was highly expressed in CC cells and poorly expressed in BMSCs. Knockdown of lncRNA Neu attenuated cell viability and proliferation while increased miR-625 expression. MiR-625 expression was negatively correlated with expression of lncRNA Neu in CC cells. Overexpression of miR-625 resulted in weakened CC cell viability. Collectively, lncRNA Neu was highly expressed in CC and promoted the development of CC through stimulating the growth of BMSCs and suppressing miR-625 expression. These findings provide a novel insight into targeted therapy for CC.

LncRNA GACAT3: A Promising Biomarker and Therapeutic Target in Human Cancers

Long non-coding RNAs (lncRNAs) are a class of functional RNA molecules that do not encode proteins and are composed of more than 200 nucleotides. LncRNAs play important roles in epigenetic and gene expression regulation. The oncogenic lncRNA GACAT3 was recently discovered to be dysregulated in many tumors. Aberrant expression of GACAT3 contributes to clinical characteristics and regulates multiple oncogenic processes. The association of GACAT3 with a variety of tumors makes it a promising biomarker for diagnosis, prognosis, and targeted therapy. In this review, we integrate the current understanding of the pathological features, biological functions, and molecular mechanisms of GACAT3 in cancer. Additionally, we provide insight into the utility of GACAT3 as an effective diagnostic and prognostic marker for specific tumors, which offers novel opportunities for targeted therapeutic intervention.

Long Noncoding RNA LIFR-AS1: A New Player in Human Cancers

Emerging evidence has indicated that aberrantly expressed long noncoding RNAs (lncRNAs) play a vital role in various biological processes associated with tumorigenesis. Leukemia inhibitory factor receptor antisense RNA1 (LIFR-AS1) is a recently identified lncRNA transcribed in an antisense manner from the LIFR gene located on human chromosome 5p13.1. LIFR-AS1 regulates tumor proliferation, migration, invasion, apoptosis, and drug resistance through different mechanisms. Its expression level is related to the clinicopathological characteristics of tumors and plays a key role in tumor occurrence and development. In this review, we summarize the role of LIFR-AS1 in the development and progression of different cancers and highlight the potential for LIFR-AS1 to serve as a biomarker and therapeutic target for a variety of human cancers.

Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients

Background

Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV.

Methods

The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets.

Results

A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed.

Conclusion

Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.

A Newly Identified lncBCAS1-4_1 Associated With Vitamin D Signaling and EMT in Ovarian Cancer Cells

Long noncoding RNAs (lncRNAs) were identified rapidly due to their important role in many biological processes and human diseases including cancer. 1α,25-dihydroxyvitamin D3 [1α,25(OH)₂D₃] and its analogues are widely applied as preventative and therapeutic anticancer agents. However, the expression profile of lncRNAs regulated by 1α,25(OH)₂D₃ in ovarian cancer remains to be clarified. In the present study, we found 606 lncRNAs and 102 mRNAs that showed differential expression (DE) based on microarray data. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that the DE genes were mainly enriched in TGF-β, MAPK, Ras, PI3K-Akt, and Hippo signaling pathways, as well as the vitamin D-related pathway. We further assessed the potential lncRNAs that linked vitamin D signaling with EMT, and lncBCAS1-4_1 was identified in the first time. Moreover, we found that the most upregulated lncBCAS1-4_1 showed 75% same transcripts with CYP24A1 (metabolic enzyme of 1α,25(OH)₂D₃). Finally, the lncBCAS1-4_1 gain-of-function cell model was established, which demonstrated that the knockdown of lncBCAS1-4_1 inhibited the proliferation and migration of ovarian cancer cells. Furthermore, lncBCAS1-4_1 could resist the antitumor effect of 1α,25(OH)₂D₃, which was associated with upregulated ZEB1. These data provide new evidences that lncRNAs served as a target for the antitumor effect of 1α,25(OH)₂D₃.

A Potential Immune-Related Long Non-coding RNA Prognostic Signature for Ovarian Cancer

Ovarian cancer (OC), the most lethal gynecologic malignancy, ranks fifth in cancer deaths among women, largely because of late diagnosis. Recent studies suggest that the expression levels of immune-related long non-coding RNAs (lncRNAs) play a significant role in the prognosis of OC; however, the potential of immune-related lncRNAs as prognostic factors in OC remains unexplored. In this study, we aimed to identify a potential immune-related lncRNA prognostic signature for OC patients. We used RNA sequencing and clinical data from The Cancer Genome Atlas and the Gene Expression Omnibus database to identify immune-related lncRNAs that could serve as useful biomarkers for OC diagnosis and prognosis. Univariate Cox regression analysis was used to identify the immune-related lncRNAs with prognostic value. Functional annotation of the data was performed through the GenCLiP310 website. Seven differentially expressed lncRNAs (AC007406.4, AC008750.1, AL022341.2, AL133351.1, FAM74A7, LINC02229, and HOXB-AS2) were found to be independent prognostic factors for OC patients. The Kaplan-Meier curve indicated that patients in the high-risk group had a poorer survival outcome than those in the low-risk group. The receiver operating characteristic curve revealed that the predictive potential of the immune-related lncRNA signature for OC was robust. The prognostic signature of the seven lncRNAs was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the signature of the seven lncRNAs was an independent prognostic factor for OC patients. Finally, we constructed a nomogram model and a competing endogenous RNA network related to the lncRNA prognostic signature. In conclusion, our study reveals novel immune-related lncRNAs that may serve as independent prognostic factors in OC.

Identification and Analysis of An Epigenetically Regulated Five-lncRNA Signature Associated With Outcome and Chemotherapy Response in Ovarian Cancer

The deregulation of long non-coding RNAs (lncRNAs) by epigenetic alterations has been implicated in cancer initiation and progression. However, the epigenetically regulated lncRNAs and their association with clinical outcome and therapeutic response in ovarian cancer (OV) remain poorly investigated. This study performed an integrative analysis of DNA methylation data and transcriptome data and identified 419 lncRNAs as potential epigenetically regulated lncRNAs. Using machine-learning and multivariate Cox regression analysis methods, we identified and developed an epigenetically regulated lncRNA expression signature (EpiLncRNASig) consisting of five lncRNAs from the list of 17 epigenetically regulated lncRNAs significantly associated with outcome. The EpiLncRNASig could stratify patients into high-risk groups and low-risk groups with significantly different survival and chemotherapy response in different patient cohorts. Multivariate Cox regression analyses, after adjusted by other clinical features and treatment response, demonstrated the independence of the DEpiLncSig in predicting survival. Functional analysis for relevant protein-coding genes of the DEpiLncSig indicated enrichment of known immune-related or cancer-related biological pathways. Taken together, our study not only provides a promising prognostic biomarker for predicting outcome and chemotherapy response but also will improve our understanding of lncRNA epigenetic regulation mechanisms in OV.

Identification of LncRNA Prognostic Markers for Ovarian Cancer by Integration of Co-expression and CeRNA Network

Background

Ovarian cancer (OC), one of the most prevalent gynecological malignancies, is characterized by late detection and dismal prognosis. Recent studies show that long non-coding RNAs (lncRNAs) in competitive endogenous RNA (ceRNA) networks influence immune infiltration and cancer prognosis. However, the function of lncRNA in OC immune infiltration and prognosis remains unclear.

Methods

Transcriptomes of 378 OC samples and clinical data were retrieved from the TCGA repository. Modules related to immune cells were identified using weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis and survival analysis were then performed for the identification of immune-related lncRNAs in the brown module using Cox regression model. Finally, a ceRNA network was constructed by using the lncRNAs and mRNAs from the brown module.

Results

We found lncRNAs and mRNAs in the brown module to be significantly associated with immune cells in OC and identified 4 lncRNAs as potential OC prognostic markers. We further established that lncRNAs in the ceRNA network influence OC immune infiltration and prognosis by regulating miRNA, ultimately modulating mRNA levels.

Conclusion

We have identified 4 lncRNAs as independent immune prognostic factors for OC. Furthermore, our findings offer novel insight into lncRNAs as OC immune and prognostic biomarkers.

The lncRNA SNHG15/miR-18a-5p axis promotes cell proliferation in ovarian cancer through activating Akt/mTOR signaling pathway

Here, we report the expression pattern, function and regulatory mechanism of SNHG15 together with miR-18a-5p micro RNA in ovarian cancer (OC) for the first time. We recruited 20 patients and took normal ovarian tissues and ovarian tumor tissues from them. We used cell culture, transfection, in vivo tumor xenograft assay, and multiple types of detection assays to investigate the expression and regulation of long noncoding RNA (lncRNA) SNHG15/miR-18a-5p in ovarian tissues and cells. Results: We found that the messenger RNA expression level of SNHG15 was significantly higher and miR-18 was decreased in ovarian cancer tissues and in OC cells. Functional experiments showed that SNHG15 overexpression potentiated the migration and invasion of OC cells, while SNHG15 inhibition reduced the tumor proliferation, which was restored via overexpression of miR-18a. SNHG15 was found to directly target and suppress the expression of miR-18a. Our results illustrate the possible molecular mechanism of lncRNA SNHG15/miR-18a-5p functions in cell proliferation in OC. SNHG15/miR-18a promoted the progression of OC cells via the protein kinase B/mammalian target of rapamycin signaling pathway.

Advancing Pan-cancer Gene Expression Survial Analysis by Inclusion of Non-coding RNA

Non-coding RNAs occupy a significant fraction of the human genome. Their biological significance is backed up by a plethora of emerging evidence. One of the most robust approaches to demonstrate non-coding RNA's biological relevance is through their prognostic value. Using the rich gene expression data from The Cancer Genome Altas (TCGA), we designed Advanced Expression Survival Analysis (AESA), a web tool which provides several novel survival analysis approaches not offered by previous tools. In addition to the common single-gene approach, AESA computes the gene expression composite score of a set of genes for survival analysis and utilizes permutation test or cross-validation to assess the significance of log-rank statistic and the degree of over-fitting. AESA offers survival feature selection with post-selection inference and utilizes expanded TCGA clinical data including overall, disease-specific, disease-free, and progression-free survival information. Users can analyse either protein-coding or non-coding regions of the transcriptome. We demonstrated the effectiveness of AESA using several empirical examples. Our analyses showed that non-coding RNAs perform as well as messenger RNAs in predicting survival of cancer patients. These results reinforce the potential prognostic value of non-coding RNAs. AESA is developed as a module in the freely accessible analysis suite MutEx. Abbreviation: ACC: Adrenocortical Carcinoma (n = 92); BLCA: Bladder Urothelial Carcinoma (n = 412); BRCA: Breast Invasive Carcinoma (n = 1098); CESC: Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (n = 307); CHOL: Cholangiocarcinoma (n = 51); COAD: Colon Adenocarcinoma (n = 461); DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (n = 58); ESCA: Oesophageal Carcinoma (n = 185); GBM: Glioblastoma Multiforme (n = 617); HNSC: Head and Neck Squamous Cell Carcinoma (n = 528); KICH: Kidney Chromophobe (n = 113); KIRC: Kidney Renal Clear Cell Carcinoma (n = 537); KIRP: Kidney Renal Papillary Cell Carcinoma (n = 291); LAML: Acute Myeloid Leukaemia (n = 200); LGG: Brain Lower Grade Glioma (n = 516); LIHC: Liver Hepatocellular Carcinoma (n = 377); LUAD: Lung Adenocarcinoma (n = 585); LUSC: Lung Squamous Cell Carcinoma (n = 504); MESO: Mesothelioma (n = 87); OV: Ovarian Serous Cystadenocarcinoma (n = 608) PAAD: Pancreatic Adenocarcinoma (n = 185); PCPG: Pheochromocytoma and Paraganglioma (n = 179); PRAD: Prostate Adenocarcinoma (n = 500); READ: Rectum Adenocarcinoma (n = 172); SARC: Sarcoma (n = 261); SKCM: Skin Cutaneous Melanoma (n = 470); STAD: Stomach Adenocarcinoma (n = 443); TGCT: Testicular Germ Cell Tumours (n = 150); THCA: Thyroid Carcinoma (n = 507) THYM: Thymoma (n = 124); UCEC: Uterine Corpus Endometrial Carcinoma (n = 560); UCS: Uterine Carcinosarcoma (n = 57); UVM: Uveal Melanoma (n = 80).

lncRNAs-mRNAs Co-Expression Network Underlying Childhood B-Cell Acute Lymphoblastic Leukaemia: A Pilot Study

Simple Summary

Acute lymphoblastic leukemia (ALL) is one of the most common childhood cancers. The ALL onset involves abnormal proliferation and arrest of differentiation of B or T cell progenitors. Recently, long non–coding RNAs (lncRNAs) gained great interest in the B–ALL leukemogenesis, however, so far few “omic” studies investigate lncRNAs and protein–coding gene networks. In our retrospective study, we conceived an integrated bioinformatic approach, by using NGS platform, to discover lncRNAs strongly correlated with aberrantly expressed protein–coding genes. We provided dysregulated lncRNA–mRNA pairs potentially underlying B–ALL pathogenesis. Diagnosis incidence peak of ALL appears approximatively between 1 and 19 years old. lncRNAs may be of clinical utility as non–invasive biomarker for B–ALL onset or therapy response in support of precision medicine. The identification of lncRNA as key regulators in B–ALL could lead to the identification of the altered pathways able to sustain the leukemic growth.

Abstract

Long non–coding RNAs (lncRNAs) are emerging as key gene regulators in the pathogenesis and development of various cancers including B lymphoblastic leukaemia (B–ALL). In this pilot study, we used RNA–Seq transcriptomic data for identifying novel lncRNA–mRNA cooperative pairs involved in childhood B–ALL pathogenesis. We conceived a bioinformatic pipeline based on unsupervised PCA feature extraction approach and stringent statistical criteria to extract potential childhood B–ALL lncRNA signatures. We then constructed a co–expression network of the aberrantly expressed lncRNAs (30) and protein–coding genes (754). We cross–validated our in–silico findings on an independent dataset and assessed the expression levels of the most differentially expressed lncRNAs and their co–expressed mRNAs through ex vivo experiments. Using the guilt–by–association approach, we predicted lncRNA functions based on their perfectly co–expressed mRNAs (Spearman’s correlation) that resulted closely disease–associated. We shed light on 24 key lncRNAs and their co–expressed mRNAs which may play an important role in B–ALL pathogenesis. Our results may be of clinical utility for diagnostic and/or prognostic purposes in paediatric B–ALL management.

Characterization of BRCA1/2-Directed ceRNA Network Identifies a Novel Three-lncRNA Signature to Predict Prognosis and Chemo-Response in Ovarian Cancer Patients With Wild-Type BRCA1/2

Long non-coding RNAs (lncRNAs) have been reported to interact with BRCA1/2 to regulate homologous recombination (HR) by diverse mechanisms in ovarian cancers (OvCa). However, genome-wide screening of BRCA1/2-related lncRNAs and their clinical significance is still unexplored. In this study, we constructed a global BRCA1/2-directed lncRNA-associated ceRNA network by integrating paired lncRNA expression profiles, miRNA expression profiles, and BRCA1/2 expression profiles in BRCA1/2 wild-type patients and identified 111 BRCA1/2-related lncRNAs. Using the stepwise regression and Cox regression analysis, we developed a BRCA1/2-directed lncRNA signature (BRCALncSig), composing of three lncRNAs (LINC01619, DLX6-AS1, and AC004943.2) from the list of 111 BRCA1/2-related lncRNAs, which was an independent prognostic factor and was able to classify the patients into high- and low-risk groups with significantly different survival in the training dataset (HR = 2.73, 95 CI 1.65–4.51, p < 0.001). The prognostic performance of the BRCALncSig was further validated in the testing dataset (HR = 1.9, 95 CI 1.21–2.99, p = 0.005) and entire TCGA dataset (HR = 2.17, 95 CI 1.56–3.01, p < 0.001). Furthermore, the BRCALncSig is associated with chemo-response and was also capable of discriminating nonequivalent outcomes for patients achieving complete response (CR) (log-rank p = 0.003). Functional analyses suggested that mRNAs co-expressed with the BRCALncSig were enriched in cancer-related or cell proliferation-related biological processes and pathways. In summary, our study highlighted the clinical implication of BRCA1/2-directed lncRNAs in the prognosis and treatment response of BRCA1/2 wild-type patients.

Long non‑coding RNA SNHG12 regulates cell proliferation, invasion and migration in endometrial cancer by targeting miR‑4429

Long non-coding RNA small nucleolar RNA host gene 12 (SNHG12) has been demonstrated to be oncogenic. The aim of the present study was to examine the effects of SNHG12 on the progression of endometrial cancer (EC). The expression levels of SNHG12 and microRNA (miR)-4429 were assessed in EC cell lines by reverse transcription-quantitative PCR. Plasmids, including SNHG12 short hairpin RNAs (shRNAs), shRNA negative control (NC), SNHG12 overexpression (OV), OV-NC, miR-4429 mimic and mimic-NC, were transfected into RL95-2 cells. Post-transfection, Cell Counting Kit-8, Transwell Matrigel and wound-healing assays were performed to assess cell proliferation, invasion and migration, respectively. Cell cycle phase distribution was assessed by flow cytometry. The protein expression levels of matrix metalloproteinase (MMP)2 and MMP9 were detected by western blotting. miR-4429 target genes were predicted by bioinformatics analysis using target prediction online tools; the findings of this analysis were verified using a dual-luciferase reporter system. Identified as a target of miR-4429, SNHG12 was overexpressed in EC cell lines with decreased expression of miR-4429. Further experiments demonstrated that SNHG12 silencing and overexpression of miR-4429 markedly suppressed proliferation, migration and invasion of RL95-2 cells, arrested cells in the G₁ phase, and markedly downregulated the expression of MMP2 and MMP9. The opposite effects were observed in miR-4429 mimic-transfected RL95-2 cells after SNHG12 was overexpressed. The findings of the present study established the role of SNHG12 and miR-4429 in EC. Therefore, targeting the SNHG12/miR-4429 axis could serve as a potential future therapeutic target for treatment of EC.

Non-coding RNAs in cancer: platforms and strategies for investigating the genomic "dark matter"

The discovery of the role of non-coding RNAs (ncRNAs) in the onset and progression of malignancies is a promising frontier of cancer genetics. It is clear that ncRNAs are candidates for therapeutic intervention, since they may act as biomarkers or key regulators of cancer gene network. Recently, profiling and sequencing of ncRNAs disclosed deep deregulation in human cancers mostly due to aberrant mechanisms of ncRNAs biogenesis, such as amplification, deletion, abnormal epigenetic or transcriptional regulation. Although dysregulated ncRNAs may promote hallmarks of cancer as oncogenes or antagonize them as tumor suppressors, the mechanisms behind these events remain to be clarified. The development of new bioinformatic tools as well as novel molecular technologies is a challenging opportunity to disclose the role of the "dark matter" of the genome. In this review, we focus on currently available platforms, computational analyses and experimental strategies to investigate ncRNAs in cancer. We highlight the differences among experimental approaches aimed to dissect miRNAs and lncRNAs, which are the most studied ncRNAs. These two classes indeed need different investigation taking into account their intrinsic characteristics, such as length, structures and also the interacting molecules. Finally, we discuss the relevance of ncRNAs in clinical practice by considering promises and challenges behind the bench to bedside translation.

A potential prognostic prediction model of colon adenocarcinoma with recurrence based on prognostic lncRNA signatures

BACKGROUND

Colon adenocarcinoma (COAD) is one of the common gastrointestinal malignant diseases, with high mortality rate and poor prognosis due to delayed diagnosis. This study aimed to construct a prognostic prediction model for patients with colon adenocarcinoma (COAD) recurrence.

METHODS

Differently expressed RNAs (DERs) between recurrence and non-recurrence COAD samples were identified based on expression profile data from the NCBI Gene Expression Omnibus (GEO) repository and The Cancer Genome Atlas (TCGA) database. Then, recurrent COAD discriminating classifier was established using SMV-RFE algorithm, and receiver operating characteristic curve was used to assess the predictive power of classifier. Furthermore, the prognostic prediction model was constructed based on univariate and multivariate Cox regression analysis, and Kaplan-Meier survival curve analysis was used to estimate this model. Furthermore, the co-expression network of DElncRNAs and DEmRNAs was constructed followed by GO and KEGG pathway enrichment analysis.

RESULTS

A total of 54 optimized signature DElncRNAs were screened and SMV classifier was constructed, which presented a high accuracy to distinguish recurrence and non-recurrence COAD samples. Furthermore, six independent prognostic lncRNAs signatures (LINC00852, ZNF667-AS1, FOXP1-IT1, LINC01560, TAF1A-AS1, and LINC00174) in COAD patients with recurrence were screened, and the prognostic prediction model for recurrent COAD was constructed, which possessed a relative satisfying predicted ability both in the training dataset and validation dataset. Furthermore, the DEmRNAs in the co-expression network were mainly enriched in glycan biosynthesis, cardiac muscle contraction, and colorectal cancer.

CONCLUSIONS

Our study revealed that six lncRNA signatures acted as an independent prognostic biomarker for patients with COAD recurrence.

Long Non-coding RNAs Involved in Resistance to Chemotherapy in Ovarian Cancer

Ovarian cancer (OC) accounts for more than 150,000 deaths worldwide every year. Patients are often diagnosed at an advanced stage with metastatic dissemination. Although platinum- and taxane-based chemotherapies are effective treatment options, they are rarely curative and eventually, the disease will progress due to acquired resistance. Emerging evidence suggests a crucial role of long non-coding RNAs (lncRNAs) in the response to therapy in OC. Transcriptome profiling studies using high throughput approaches have identified differential expression patterns of lncRNAs associated with disease recurrence. Furthermore, several aberrantly expressed lncRNAs in resistant OC cells have been related to increased cell division, improved DNA repair, up-regulation of drug transporters or reduced susceptibility to apoptotic stimuli, supporting their involvement in acquired resistance. In this review, we will discuss the key aspects of lncRNAs associated with the development of resistance to platinum- and taxane-based chemotherapy in OC. The molecular landscape of OC will be introduced, to provide a background for understanding the role of lncRNAs in the acquisition of malignant properties. We will focus on the interplay between lncRNAs and molecular pathways affecting drug response to evaluate their impact on treatment resistance. Additionally, we will discuss the prospects of using lncRNAs as biomarkers or targets for precision medicine in OC. Although there is still plenty to learn about lncRNAs and technical challenges to be solved, the evidence of their involvement in OC and the development of acquired resistance are compelling and warrant further investigation for clinical applications.

linc01088 promotes cell proliferation by scaffolding EZH2 and repressing p21 in human non-small cell lung cancer

AIMS

Non-small cell lung cancer (NSCLC), characterized by extensive metastasis and poor prognosis, is the most common type of lung cancer. Dysregulation of certain lncRNAs is known to be linked to the tumorigenesis of NSCLC. However, the specific roles in NSCLC for many other lncRNAs, such as linc01088, remain largely unknown.

MATERIALS AND METHODS

The expression patterns of linc01088, p21 and EZH2 were examined both in NSCLC tissues and cell lines using RT-qPCR assay. CCK-8, colony formation, immunofluorescence staining, and flow cytometry assays were employed to evaluate the effects of linc01088 on NSCLC cell proliferation properties. RNA immunoprecipitation (RIP) assay was performed to determine the direct binding relationship between linc01088 and zeste homolog 2 (EZH2). Western blot and RT-qPCR analysis were performed to assess p21 level within knockdown of either linc01088 or EZH2. Nude mouse subcutaneous NSCLC models were constructed for further validating the effects and mechanisms of linc01088 in vivo.

KEY FINDINGS

linc01088 and EZH2 were highly expressed both in NSCLC tissues and cell lines. Knockdown of linc01088 suppressed the proliferation of NSCLC cells, and prolonged the G1 phase while shortened S and G2-M phases. RIP assay revealed the direct binding relationship between linc01088 and EZH2. Knockdown of either linc01088 or EZH2 induced up-regulation of p21 expression, which subsequently inhibited the tumor growth.

SIGNIFICANCE

We demonstrated that linc01088 could promote cell proliferation via binding with EZH2 to repress p21, which aggravates the tumorigenesis of NSCLC. Therefore, linc01088 might be a potential oncogene and target for novel anti-tumor therapies.

Regulation of lncRNA PVT1 on miR-125 in metastasis of gastric cancer cells

Regulation of long non-coding RNA (lncRNA) PVT1 on miR-125 affecting the metastasis of gastric cancer cells and the mechanism were investigated. qPCR was used to detect the expression of PVT1 and miR-125 in gastric cancer and paracancerous tissues, and the relationship between PVT1 and clinicopathological parameters of gastric cancer patients. Dual luciferase reporter gene was used to detect the mutual effect between PVT1 and miR-125. The clone formation assay was used to detect changes of proliferation behavior of gastric cancer cells after inhibition of PVT1. Transwell invasion assay was used to detect the changes of invasion ability of gastric cancer cells after inhibition of PVT1. Subcutaneous tumor formation in nude mice inhibited the effect of PVT1 on the tumor size and volume of gastric cancer cells. Compared with paracancerous tissues, the expression of PVT1 and miR-125 was significantly increased in gastric cancer tissues. There were no significant differences in the expression level of PVT1 between gastric cancer patients of different genders and ages. The higher the gastric cancer staging was, the more obvious the expression level of PVT1 in the tissues of patients with gastric cancer was, and the more obvious the expression of PVT1 in the tissues of patients with gastric lymph node metastasis was. PVT1 specifically bound to the 3′UTR of miR-125, whereas the inhibition of PVT1 inhibited the ability of proliferation and invasion of gastric cancer cells. In vitro experiment of tumor formation in nude mice showed that the tumor volume and weight of the tumor-bearing mice in the si-PVT1 group were significantly reduced. PVT1 plays an important role in the occurrence and development of gastric cancer, it can regulate the proliferation and invasion of gastric cancer cells by targeting miR-125 activity.

Large-scale integrated analysis of ovarian cancer tumors and cell lines identifies an individualized gene expression signature for predicting response to platinum-based chemotherapy

Heterogeneity in chemotherapeutic response is directly associated with prognosis and disease recurrence in patients with ovarian cancer (OvCa). Despite the significant clinical need, a credible gene signature for predicting response to platinum-based chemotherapy and for guiding the selection of personalized chemotherapy regimens has not yet been identified. The present study used an integrated approach involving both OvCa tumors and cell lines to identify an individualized gene expression signature, denoted as IndividCRS, consisting of 16 robust chemotherapy-responsive genes for predicting intrinsic or acquired chemotherapy response in the meta-discovery dataset. The robust performance of this signature was subsequently validated in 25 independent tumor datasets comprising 2215 patients and one independent cell line dataset, across different technical platforms. The IndividCRS was significantly correlated with the response to platinum therapy and predicted the improved outcome. Moreover, the IndividCRS correlated with homologous recombination deficiency (HRD) and was also capable of discriminating HR-deficient tumors with or without platinum-sensitivity for guiding HRD-targeted clinical trials. Our results reveal the universality and simplicity of the IndividCRS as a promising individualized genomic tool to rapidly monitor response to chemotherapy and predict the outcome of patients with OvCa.

Identification and validation of potential long non-coding RNA biomarkers in predicting survival of patients with head and neck squamous cell carcinoma

Long non-coding RNAs (lncRNAs) are frequently dysregulated in cancer and their aberrant expression has been associated with cancer diagnosis and prognosis, which suggests that they may be promising molecular biomarkers. However, understanding of the expression pattern of lncRNAs and their prognostic roles in head and neck squamous cell carcinoma (HNSCC) is relatively limited. In the current study, the prognostic value of lncRNA expression profiles in predicting the OS of patients with HNSCC was investigated by integrating clinical and profiling data from The Cancer Genome Atlas. A total of ten lncRNAs closely associated with the prognosis of patients with HNSCC were identified and may serve as novel biomarkers. This 10-lncRNA signature was used to classify patients into 2 groups with significantly different overall survival (OS) times (median OS time, 1.65 vs. 13.04 years; P<0.0001). This lncRNA signature was validated in an independent testing cohort. The results of multivariable Cox regression and stratification analyses revealed that the prognostic value of the 10-lncRNA signature was independent of other clinical and pathological factors for the survival of patients with HNSCC. Functional analysis demonstrated that lncRNA expression-based risk scoring may reflect the basic status of the immune response in the tumor microenvironment. The presented study demonstrated the value of a lncRNA signature as a potential biomarker to improve the clinical prognosis of patients with HNSCC.

A novel risk score system for assessment of ovarian cancer based on co-expression network analysis and expression level of five lncRNAs

Background

Ovarian cancer (OC) is the most deadly gynaecological cancer, contributing significantly to female cancer-related deaths worldwide. Improving the outlook for OC patients depends on the identification of more reliable prognostic biomarkers for early diagnosis and survival prediction. The various roles of long non-coding RNAs (lncRNAs) in OC have attracted increasing attention. This study aimed to identify a lncRNA-based signature for survival prediction in OC patients.

Methods

RNA expression data and clinical information from a large number of OC patients were downloaded from a public database. These data were regarded as a training set to construct a weighed gene co-expression network analysis (WGCNA) network, mine stable modules, and screen differentially expressed lncRNAs. The prognostic lncRNAs were screened using univariate Cox regression analysis and the optimal prognosis lncRNA combination was screened using a Cox-PH model. The finalised lncRNA combination was used to construct the risk score system, which was validated and assessed for effectiveness using other independent datasets. Further functional pathway enrichment was performed using gene set enrichment analysis (GSEA).

Results

A co-expression network was constructed and four stable modules with OC-related biological functions were obtained. A total of 19 lncRNAs significantly related to prognosis of ovarian cancer were obtained using univariate Cox regression analysis, and the 5 prognostic signature lncRNAs GAS5, HCP5, PART1, SNHG11, and SNHG5 were used to establish a risk assessment system. The reliability of the prognostic scoring system was further confirmed using validation sets, which indicated that the risk assessment system could be used as an independent prognostic factor. Pathway enrichment analysis revealed that the network modules related to the above five prognostic genes were significantly associated with cell local adhesion, cancer signaling pathways, JAK-STAT signalling, and endogenous cell receptor interaction.

Conclusions

The risk score system established in this study could provide a novel reliable method to identify individuals at high risk of OC. In addition, the five prognostic lncRNAs identified here are promising potential prognostic biomarkers that could help to elucidate the pathogenesis of OC.

Overexpression of CASC11 in ovarian squamous cell carcinoma mediates the development of cancer cell resistance to chemotherapy

LncRNA CASC11 promotes gastric cancer and colon cancer. Our study analyzed the role of CASC11 in ovarian squamous cell carcinoma (OSCC). In the present study we showed that plasma CASC11 was upregulated in OSCC, and the upregulation of CASC11 distinguished OSCC patients from control group. Plasma levels of CASC11 were further increased after chemotherapy. Treatment with oxaliplatin, tetraplatin, cisplatin, and carboplatin mediated the upregulation of CASC11 in cells of OSCC cell line. In addition, overexpression of CASC11 led to increased cancer cell viability under oxaliplatin, tetraplatin, cisplatin, and carboplatin treatment, while CASC11 siRNA silencing played an opposite role. Therefore, overexpression of CASC11 in OSCC mediated the development of cancer cell resistance to chemotherapy.

LncRNA Profile Study Reveals a Three-LncRNA Signature Associated With the Pathological Complete Response Following Neoadjuvant Chemotherapy in Breast Cancer

Background

The purpose of this study is to develop an effective but concise long non-coding RNA (lncRNA) expression signature that can predict response to neoadjuvant chemotherapy for breast cancer (BC) patients.

Methods

lncRNA expression profiling in 1102 BC patients from Gene Expression Omnibus datasets was analyzed using lncRNA-mining approach. The association between lncRNA signature and pathological complete response (pCR) was analyzed using logistic regression model in the training set (GSE25066, n = 488). Validation was performed in independent testing datasets, GSE20194, GSE20271, GSE22093, and GSE23988 (n = 614). Bonferroni method was employed for multiple testing corrections. Cell proliferation assay and Western blot assay were performed to evaluate the cell viability and protein expression level, respectively.

Results

Three lncRNAs (AK291479, U79293, and BC032585) have been identified to be significantly associated with pCR in the training dataset (Bonferroni p-value < 0.05). Expression signature with these lncRNAs was predictive of pCR in the training (AUC = 0.74) and testing (AUC = 0.72) datasets. Weighted gene co-expression network analysis and gene functional annotation suggest that the three lncRNAs were involved in cell cycle process. To confirm the functional significance of the identified lncRNAs, BC032585 was selectively silenced using RNA interference. Knockdown of BC032585 lncRNA significantly promoted cell resistance to multiple anticancer-drugs through upregulating MDR1 expression in breast cancer cells.

Conclusion

These results suggest that lncRNAs such as BC032585 might be involved in chemotherapeutic response in breast cancer patients, and the three-lncRNA signature identified in the present study may serve as a useful biomarker for the selection of responsive breast cancer patients in neoadjuvant chemotherapy.

LncRNA CASC2 inhibits proliferation and migration of adenocarcinoma cells via miR-4735-3p and mTOR

Lung adenocarcinoma is a major form of non-small-cell lung cancer that frequently strikes nonsmokers. The disease is often diagnosed at a late stage and the 5-year survival rate is very low. Although previous studies found many somatic alterations associated with lung adenocarcinoma, the molecular basis of the development and progression of the disease is not well understood. We found that long noncoding RNA (lncRNA) cancer susceptibility candidate 2 (CASC2), a putative tumor suppressor, was downregulated in both patient adenocarcinoma tissues and cultured lung cancer cells. Its tumor suppression function seemed to be dependent on its binding to miR-4735-5p. Changing the levels of CASC2 and miR-4735-3p in the cultured adenocarcinoma cells could affect the malignant phenotypes as well as growth of tumors derived from the cells injected into nude mice. Furthermore, the lncRNA and miR-4735-3p interplay likely the suppressed tumor growth through the downstream mammalian target of rapamycin signaling pathway. The results have revealed molecular details that may be critical for the development of lung adenocarcinoma, opening opportunities for the development of novel, and therapeutic tools.

Analysis of microRNA expression profiles reveals a 5‑microRNA prognostic signature for predicting overall survival time in patients with gastric adenocarcinoma

There is growing evidence supporting dysregulated microRNAs (miRNAs) as potential prognostic biomarkers in cancer. The present study aimed to identify an miRNA model set with prognostic power for patients with gastric adenocarcinoma. miRNA‑seq data from 155 patients and 37 controls were downloaded from The Cancer Genome Atlas (TCGA) database for a comprehensive analysis of miRNA expression profiles and were used as training data. A total of 5 prognostic miRNAs, which have not been previously reported, were identified using univariate and multivariate Cox regression analyses. A separate 155‑patient TCGA cohort was used as a validation set for evaluation of the risk model. Patients in the training set were assigned into high‑ and low‑risk groups according to the 5‑miRNA signature risk scores. Kaplan‑Meier survival analyses demonstrated that patients with high risk scores had significantly shorter survival times than those with low risk scores. The risk model validation confirmed the prognostic ability of this 5‑miRNA signature in predicting the risk status of patients. Stratification analysis for clinical prognostic variables demonstrated recurrence and age were significant prognostic factors in the low‑ and high‑risk groups, respectively. In conclusion, the present 5‑miRNA signature is a potential independent risk factor for patient outcomes. The risk model based on the 5‑miRNA signature performed well in predicting overall survival time in patients with gastric adenocarcinoma.

Overexpression of the lncRNA FER1L4 inhibits paclitaxel tolerance of ovarian cancer cells via the regulation of the MAPK signaling pathway

To determine how the lncRNA FER1L4 in ovarian cancer cells influences paclitaxel (PTX) resistance, we examined the expression level of FER1L4 in human ovarian epithelial cell lines IOSE80 and HOSEpiC and human ovarian cancer cell lines OVCAR-3, Caov-3, and SKOV3 through RNA isolation and quantitative polymerase chain reaction (qRT-PCR). SKOV3 cell lines were treated with PTX. The cell survival rate and apoptosis rate of SKOV3 and SKOV3-PR at different PTX dose levels were evaluated. Next, qRT-PCR was performed to detect the expression of FER1L4 in SKOV3 and SKOV3-PR cell lines. SKOV3-PR cell lines were transfected with pcDNA3.1 as the control group (SKOV3-PR/pcDNA3.1) or pcDNA3.1-FER1L4 to upregulate the expression level of FER1L4 (SKOV3-PR/pcDNA3.1-FER1L4). The level of cell survival, apoptosis, and colony formation were compared between the two groups using MTT, flow cytometry analysis, and colony formation assay. To reveal the molecular mechanism, we measured the relative protein phosphorylation level of ERK and MAPK in SKOV3, SKOV3-PR, SKOV3-PR/pcDNA3.1, and SKOV3-PR/pcDNA3.1-FER1L4 groups using an enzyme-linked immunosorbent assay. The effects of SB203580 (a p38 MAPK inhibitor) on PTX were also investigated to reveal the function of the MAPK pathway on the PTX tolerance of SKOV3. In comparison with normal ovarian epithelial cells, FER1L4 was downregulated. The FER1L4 level was decreased in human ovarian cancer cells with drug resistance than in common ovarian cancer cells. The upregulation of FER1L4 could promote the PTX sensitivity of ovarian cancer cells. The increased level of FER1L4 could suppress the PTX resistance of ovarian cancer cells through the inhibition of the MAPK signaling pathway.

LncRNA AWPPH promotes the proliferation, migration and invasion of ovarian carcinoma cells via activation of the Wnt/β‑catenin signaling pathway

The oncogenic role of the long noncoding RNA associated with poor prognosis of hepatocellular carcinoma (lncRNA AWPPH) was reported in various types of malignancies; however, its involvement in ovarian carcinoma (OC) remains unknown. Thus, the present study investigated the role of AWPPH in OC. The expression of AWPPH in tissues and serum acquired from patients with OC, and healthy controls, was determined via reverse transcription‑quantitative polymerase chain reaction. The diagnostic value of serum AWPPH expression was evaluated by receiver operating characteristic curve analysis. Additionally, survival curve analysis was performed to determine the prognostic value of AWPPH for OC. An AWPPH overexpression vector was transfected into OC cell lines. Cell proliferation, migration and invasion were analyzed via Cell Counting Kit‑8, Transwell migration and invasion assays, respectively. The expression of β‑catenin was investigated via western blotting. It was revealed that the expression levels of AWPPH were significantly upregulated in OC tissues and serum compared with healthy controls. The serum levels of AWPPH were able to effectively diagnose and predict the prognosis of patients with OC. AWPPH overexpression promoted the proliferation, migration and invasion of OC cells, and upregulated β‑catenin expression. Treatment with a Wnt agonist markedly altered AWPPH expression; however, inhibition of Wnt suppressed the effects of AWPPH overexpression on proliferation, migration and invasion of OC cells. Therefore, it was revealed that AWPPH may promote OC via activation of the Wnt/β‑catenin signaling pathway.

Loss of ARID1A Expression Correlates With Tumor Differentiation and Tumor Progression Stage in Pancreatic Ductal Adenocarcinoma

Mutations in the AT-rich interactive domain 1A gene, which encodes a subunit of the Switch/Sucrose nonfermentable chromatin remodeling complex, can result in loss of protein expression and are associated with different cancers. Here, we used immunohistochemistry to investigate the significance of AT-rich interactive domain 1A loss in 73 pancreatic ductal adenocarcinoma cases with paired paracancerous normal pancreatic tissues. The relationship between levels of the AT-rich interactive domain 1A protein product, BAF250a, and clinicopathological parameters in the 73 pancreatic cancer specimens was also analyzed. We found that the expression of AT-rich interactive domain 1A in normal pancreatic tissue was higher than that in tumor tissue. Loss of AT-rich interactive domain 1A expression in pancreatic tumors was associated with tumor differentiation (P = .002) and tumor stage (P = .048). Meanwhile, BAF250a protein levels were not related to lymph node metastasis, distant metastasis, sex, or age and were not associated with survival. Transfection of the pancreatic cancer cell lines AsPC-1 and PANC-1 with small-interfering RNA specific for AT-rich interactive domain 1A resulted in elevated messenger RNA and protein expression levels of B-cell lymphoma-2 (Bcl-2), CyclinD1, and Kirsten rat sarcoma viral oncogene (KRAS). The AT-rich interactive domain 1A expression level in the cells was increased following microRNA-31 (miR-31) inhibitor transfection. Our data provide additional evidence that AT-rich interactive domain 1A might function as a tumor suppressor gene in pancreatic carcinogenesis.

LINK-A Long Non-Coding RNA (lncRNA) Participates in Metastasis of Ovarian Carcinoma and Upregulates Hypoxia-Inducible Factor 1 (HIF1α)

Background

The functionality of LINK-A lncRNA as an oncogenic lncRNA has only been characterized in triple-negative breast cancer. The present study investigated the possible involvement of LINK-A in ovarian carcinoma.

Material/Methods

We enrolled 76 patients with ovarian carcinoma. Quantitative real-time PCR (qRT-PCR) was performed to analyze LINK-A expression in ovarian biopsies. Effects of LINK-A overexpression on HIF1α and cancer cell migration and invasion were assessed.

Results

We found that LINK-A was significantly higher in ovarian carcinoma patients than in healthy controls in ovarian biopsies and in serum. LINK-A expression effectively distinguished patients with metastatic ovarian carcinoma from healthy controls. LINK-A overexpression promoted cancer cell migration and invasion, and also upregulated the expression of HIF1α.

Conclusions

LINK-A lncRNA may be involved in the metastasis of ovarian carcinoma by upregulating HIF1α.

In silico detection of potential prognostic circRNAs through a re-annotation strategy in ovarian cancer

Ovarian cancer (OC) is the most common and lethal gynecologic malignancy. The pathophysiology of OC tumor development is complex and involves numerous biological pathways. Previous studies suggest that circular (circ)RNAs serve important roles in OC tumor pathology. In the present study, a re-annotation strategy was performed to evaluate the expression level of circRNAs based on a microarray dataset obtained from the Gene Expression Omnibus database. Univariate and multivariate Cox regression analyses were performed to evaluate the association between survival and expression of circRNAs in each OC cohort. An expression-based risk score model was constructed to extrapolate the prognostic efficacy of this signature. In the GSE9891 dataset, the 278 OC patients were randomly divided into training and validating groups. A six-circRNA signature was significantly associated with overall survival in the training and validating datasets. The risk score model was further validated in GSE63885 and GSE26193 datasets. The six-circRNA signature was also significantly associated with patient progression-free survival and disease-free survival. Further investigation revealed that the signature had higher area under the curve values than the existing clinical and other molecular signatures in predicting survival. In conclusion, the present study revealed that the six-circRNA signature may serve as a potential prognostic biomarker of OC.

Identification of potential prognostic TF-associated lncRNAs for predicting survival in ovarian cancer

The pathophysiology of ovarian cancer (OV) is complex and depends on multiple biological processes and pathways. Therefore, there is an urgent need to identify reliable prognostic biomarkers for predicting clinical outcomes and helping personalize treatment of OV. A long non-coding RNA (lncRNA)-based risk score model was constructed to infer the prognostic efficacy of transcription factors (TFs) based on the OV dataset from The Cancer Genome Atlas. The risk score model was further validated in other independent cohorts from Gene Expression Omnibus. Time-dependent receiver operating characteristic curves were used to evaluate the survival prediction performance in comparison with other clinical and molecular variables. Our results revealed that the top-ranked TF-associating lncRNAs were significantly associated with overall survival, progression-free survival and disease-free survival. Stratification analysis according to clinical variables indicated the prognostic independence of POLR2A-associating lncRNAs. In comparison, the signature of POLR2A-associating lncRNAs was more sensitive and specific than existing clinical and molecular signatures. Functional and experimental analysis suggested that POLR2A-associating lncRNAs may be involved in known biological processes and pathways of OV. Our findings revealed that the lncRNA-based risk score model can provide helpful information on OV prognosis stratification and discovery of therapeutic biomarkers.

Identification of novel long non-coding RNA in diffuse intrinsic pontine gliomas by expression profile analysis

Diffuse intrinsic pontine glioma (DIPG) is one of the most devastating types of pediatric cancer. Accumulating evidence suggests that the dysregulated expression of long non-coding (lnc)-RNAs is associated with various pathologies of the CNS. However, the expression patterns and prognostic roles of lncRNAs in DIPG have not yet been systematically determined. In the present study, lncRNA expression profiles were obtained from the Gene Expression Omnibus (GEO) database using the lncRNA-mining approach and a differential expression analysis for lncRNAs was performed between DIPG and low-grade brainstem glioma and DIPG and normal pediatric brainstem tissue. Using a two-tailed t-test, 58 and 197 lncRNAs were found to be significantly deferentially expressed (Fold change >2 or <0.5, FDR adjusted P<0.05). To identify the prognostic value of these 255 differentially expressed lncRNAs, univariate and multivariate Cox proportional hazards regression analysis were performed and a 9-lncRNA signature as a potential biomarker for predicting the prognosis of DIPG was constructed. Kaplan-Meier curve analysis showed that patients in the high-risk group exhibited a reduced survival time compared with patients in the low-risk group (median survival of 230 vs. 460 days, log-rank test P<0.001). Moreover, this lncRNA-signature could be used as an independent prognostic marker for DIPG patient survival. The present study provided novel candidates for the investigation of potential diagnostic or prognostic biomarkers and/or therapeutic targets of DIPG, as well as a novel insight into the underlying mechanisms of DIPG.

Genome-wide methylation analysis identifies novel prognostic methylation markers in colon adenocarcinoma

Previous studies have indicated that abnormal methylation is a critical and early event in the pathogenesis of most types of human cancer, which contributes to tumorigenesis. However, there has been little focus on the potential of DNA methylation patterns as predictive markers for the prognosis of colon adenocarcinoma (COAD). In the present study, a genome-wide comparative analysis of DNA methylation profiles was performed between 315 COAD samples and 38 matched tumor-adjacent normal tissue samples. A total of 675 differentially methylated regions (DMRs) associated with 630 genes were identified, including 654 hypermethylated regions (UMRs) and 21 hypomethylated regions, which were capable of distinguishing COAD samples from non-malignant tissue samples. Although most of the DMRs appeared to be located within the gene body or promoter regions, UMRs were mostly located within CpG islands. Functional analysis suggested that genes associated with DMRs were enriched in many of the core cancer-signaling pathways known to be important in COAD biology. A survival analysis was also performed, which identified 7 DMRs as potential candidate markers with the ability to classify patients into high and low-risk groups with significantly different overall survival. The present study provides a better understanding of the molecular mechanisms underlying COAD, and demonstrates the utility of aberrant DNA methylation in the prognosis of COAD.

Identification of ceRNA network based on a RNA-seq shows prognostic lncRNA biomarkers in human lung adenocarcinoma

Previous studies have emphasized the significant functions of long non-coding RNAs (lncRNAs) as competing endogenous RNAs (ceRNAs) in tumor biology. However, the functions of certain cancer lncRNAs in the lncRNA-related ceRNA network in lung adenocarcinoma (LUAD) are unknown. A systematic and integrative survey of RNA-seq data from The Cancer Genome Atlas (TCGA) was performed to identify candidate lncRNAs for the prognosis of LUAD. In total, 20,502 genes that contain 181 lncRNAs were evaluated in a cohort of 570 LUAD cases. Initially, 6,280 differentially expressed genes (fold-change >2, P<0.05) were obtained using R package, which includes 75 lncRNAs. Next, by univariate regression and multivariate Cox proportional hazards analysis, 32 genes were associated with survival in LUAD. Using these 29 mRNAs and 3 lncRNAs, a prognosis index (PI) was calculated to accurately estimate the survival in LUAD: PI=∑exp_{risk gene} × HRrisk gene. Furthermore, the 32-gene signature was an independent prognostic indicator for LUAD (HR >1; P<0.05, by multivariate analysis). Weighted gene co-expression network analysis (WGCNA) of three risk lncRNAs-FAM138B, NHEG1 and TLX1NB-was performed, based on the P-values of the associated genes, and the top 27 miRNAs that bound to these lncRNAs were predicted by Miranda as target miRNAs. Next, these target miRNAs were transferred to the TarBase, miRTarBase, miRecards and starBase v2.0 databases to obtain their target genes. According to the previous miRNA-mRNA and miRNA-lncRNA data, three lncRNA-miRNA-mRNA ceRNA networks were established, based on the 29 prognostic mRNAs, forming a regulatory network in LUAD. The present study provided insight into the lncRNA-related ceRNA network in LUAD and has identified potential diagnostic and prognostic biomarkers.