A novel lncRNA-focus expression signature for survival prediction in endometrial carcinoma

Non-coding RNAs in gynecologic cancer

The term "gynecologic cancer" pertains to neoplasms impacting the reproductive tissues and organs of women encompassing the endometrium, vagina, cervix, uterus, vulva, and ovaries. The progression of gynecologic cancer is linked to various molecular mechanisms. Historically, cancer research primarily focused on protein-coding genes. However, recent years have unveiled the involvement of non-coding RNAs (ncRNAs), including microRNAs, long non-coding RNAs (LncRNAs), and circular RNAs, in modulating cellular functions within gynecological cancer. Substantial evidence suggests that ncRNAs may wield a dual role in gynecological cancer, acting as either oncogenic or tumor-suppressive agents. Numerous clinical trials are presently investigating the roles of ncRNAs as biomarkers and therapeutic agents. These endeavors may introduce a fresh perspective on the diagnosis and treatment of gynecological cancer. In this overview, we highlight some of the ncRNAs associated with gynecological cancers.

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.

Integrative Analysis Reveals a Nine TP53 Pathway-Related lncRNA Prognostic Signature in Endometrial Cancer

Background

TP53 mutation is a common mutation gene in uterine corpus endometrial carcinoma (UCEC), and the TP53 signaling pathway plays an essential role in the tumorigenesis, progression, and immune infiltration in UCEC. We aimed to discover TP53 pathway-related lncRNAs in UCEC. Materials and methods. 528 UCEC patients with 587 transcriptional profiles were enrolled in this study. We first investigated the differential status of TP53 signaling pathway between tumor and normal tissues by GSEA analysis, then identified TP53 pathway-related lncRNAs, accordingly establishing a nine TP53 pathway related to the lncRNA signature in the training set and verified this signature in the test set. Besides, the interaction network was constructed; the immune infiltration, drug response to cisplatin and paclitaxel, and mutation atlas were investigated. Finally, we performed a subgroup analysis to check the universality of this signature.

Results

A nine TP53 pathway-related lncRNA prognostic signature was constructed and verified superior accuracy in predicting the overall survival of UCEC patients. Besides, high-risk patients showed a poor prognosis, but they were more sensitive to the cisplatin and paclitaxel. Notably, M2 macrophages were higher infiltrated in high-risk patients, and TP53 showed a significantly higher mutation in high-risk patients than low-risk patients.

Conclusions

We constructed and verified a nine TP53 pathway-related lncRNA prognostic signature in UCEC, which also contributes to the decision-making of the chemotherapy.

COVID-19–associated lncRNAs as predictors of survival in uterine corpus endometrial carcinoma: A prognostic model

Background: Patients with uterine corpus endometrial carcinoma (UCEC) may be susceptible to the coronavirus disease-2019 (COVID-19). Long non–coding RNAs take on a critical significance in UCEC occurrence, development, and prognosis. Accordingly, this study aimed to develop a novel model related to COVID-19–related lncRNAs for optimizing the prognosis of endometrial carcinoma.

Methods: The samples of endometrial carcinoma patients and the relevant clinical data were acquired in the Carcinoma Genome Atlas (TCGA) database. COVID-19–related lncRNAs were analyzed and obtained by coexpression. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were performed to establish a COVID-19–related lncRNA risk model. Kaplan–Meier analysis, principal component analysis (PCA), and functional enrichment annotation were used to analyze the risk model. Finally, the potential immunotherapeutic signatures and drug sensitivity prediction targeting this model were also discussed.

Results: The risk model comprising 10 COVID-19–associated lncRNAs was identified as a predictive ability for overall survival (OS) in UCEC patients. PCA analysis confirmed a reliable clustering ability of the risk model. By regrouping the patients with this model, different clinic-pathological characteristics, immunotherapeutic response, and chemotherapeutics sensitivity were also observed in different groups.

Conclusion: This risk model was developed based on COVID-19–associated lncRNAs which would be conducive to the precise treatment of patients with UCEC.

Prognostic signature of endoplasmic reticulum stress-related long noncoding RNAs in head and neck squamous cell carcinoma: Association with somatic mutation and tumor immune microenvironment

Background/purpose

Analysis of endoplasmic reticulum stress (ERS)-related long noncoding RNAs (LncRNAs) may enable prognostic stratification in patients with head and neck squamous cell carcinoma (HNSCC). This study aimed to comprehensively analyze the ERS-related LncRNAs signature and its effects on the prognosis, tumorigenesis, and tumor immune microenvironment in HNSCC.

Materials and methods

The transcriptome data of HNSCC were obtained from TCGA. Least absolute shrinkage selection operator algorithm, and multivariate Cox regression were used to screen LncRNAs for the signature construction. Somatic mutation, gene enrichment, and immune infiltration analyses were further performed.

Results

458 ERS-related LncRNAs were identified and 55 of which were correlated with HNSCC prognosis. Ten ERS-related LncRNAs were selected to establish a risk prediction signature. When dividing patients into high-risk and low-risk groups by signature score, high-risk group correlated with worse survival rates (hazard ratio = 1.211; 95% confidence interval 1.123-1.306, P < 0.001). The area under the curve was 0.751 and 0.716 in the training and validation cohorts at 3-year. Moreover, high-risk group have increased somatic mutation rates and reduced infiltration abundancy of B cells and CD8+ T cells.

Conclusion

The prognostic signature based on ERS-related LncRNAs may serve as a predictor of altered oncogene mutations and immune microenvironment, which provided an insight into the relationship between ERS, LncRNAs, and tumor progression.

N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma

Uterine corpus endometrial carcinoma (UCEC) is a malignant disease that, at present, has no well-characterised prognostic biomarker. In this study, two clusters were identified based on 28 N1-methyladenosine- (m1A-) related long noncoding RNAs (lncRNAs), of which cluster 1 was related to immune pathways according to the results of an enrichment analysis. We further observed better prognosis in patients with higher levels of immune cell infiltration, tumor mutation burden, microsatellite instability, and immune checkpoint gene expression. In addition, through Cox regression analysis and least absolute shrinkage and selection operator regression analysis, 10 m1A-related lncRNAs (mRLs) were employed to build a prognosis model. We found that people in higher risk categories had a poorer survival probability than those in lower risk. Low-risk samples were enriched with immune-related pathways, while the high-risk group was similar to the definition of the “immune desert” phenotype, which was associated with decreased immune infiltration, T cell failure, and decreased tumor mutation burden, while also being insensitive to immunotherapy and chemotherapy. This mRL-based model has the ability to accurately predict the prognosis of UCEC patients, and the mRLs could become promising therapeutic targets in enhancing the response of immunotherapy.

DSCAM-AS1 Long Non-Coding RNA Exerts Oncogenic Functions in Endometrial Adenocarcinoma via Activation of a Tumor-Promoting Transcriptome Profile

Accumulating evidence suggests that lncRNA DSCAM-AS1 acts tumor-promoting in various cancer entities. In breast cancer, DSCAM-AS1 was shown to be the lncRNA being most responsive to induction by estrogen receptor α (ERα). In this study, we examined the function of DSCAM-AS1 in endometrial adenocarcinoma using in silico and different in vitro approaches. Initial analysis of open-source data revealed DSCAM-AS1 overexpression in endometrial cancer (EC) (p < 0.01) and a significant association with shorter overall survival of EC patients (HR = 1.78, p < 0.01). In EC, DSCAM-AS1 was associated with endometrial tumor promotor gene PRL and with expression of ERα and its target genes TFF1 and PGR. Silencing of this lncRNA by RNAi in two EC cell lines was more efficient in ERα-negative HEC-1B cells and reduced their growth and the expression of proliferation activators like NOTCH1, PTK2 and EGR1. DSCAM-AS1 knockdown triggered an anti-tumoral transcriptome response as revealed by Affymetrix microarray analysis, emerging from down-regulation of tumor-promoting genes and induction of tumor-suppressive networks. Finally, several genes regulated upon DSCAM-AS1 silencing in vitro were found to be inversely correlated with this lncRNA in EC tissues. This study clearly suggests an oncogenic function of DSCAM-AS1 in endometrial adenocarcinoma via activation of a tumor-promoting transcriptome profile.

A comprehensive prognostic and immune analysis of enhancer RNA identifies IGFBP7-AS1 as a novel prognostic biomarker in Uterine Corpus Endometrial Carcinoma

BACKGROUND

Long non-coding RNAs (lncRNA) have been implicated in a hand of studies that supported an involvement and co-operation in Uterine Corpus Endometrial Carcinoma (UCEC). Enhancer RNAs (eRNA), a functional subtype of lncRNA, have a key role throughout the genome to guide protein production, thus potentially associated with diseases.

METHODS

In this study, we mainly applied the Cancer Genome Atlas (TCGA) dataset to systematically discover crucial eRNAs involving UCEC. For the key eRNAs in UCEC, we employed RT-qPCR to compare eRNA expression levels in tumor tissues and paired normal adjacent tissues from UCEC patients for validation. Furthermore, the relationships between the key eRNAs and immune activities were measured from several aspects, including the analysis for tumor microenvironment, immune infiltration cells, immune check point genes, tumor mutation burden, and microsatellite instability, as well as m6A related genes. Finally, the key eRNAs were verified by a comprehensive pan-cancer analysis.

RESULTS

IGFBP7 Antisense RNA 1 (IGFBP7-AS1) was identified as the key eRNA for its expression patterns of low levels in tumor tissues and favorable prognostic value in UCEC correlated with its target gene IGFBP7. In RT-qPCR analysis, IGFBP7-AS1 and IGFBP7 had down-regulated expression in tumor tissues, which was consistent with previous analysis. Moreover, IGFBP7-AS1 was found closely related with immune response in relevant immune analyses. Besides, IGFBP7-AS1 and its target gene IGFBP7 correlated with a multi-omics pan-cancer analysis.

CONCLUSIONS

Finally, we suggested that IGFBP7-AS1 played a key role in impacting on clinical outcomes of UCEC patients for its possible influence on immune activity.

The Expression of Pyroptosis-Related Gene May Influence the Occurrence, Development, and Prognosis of Uterine Corpus Endometrial Carcinoma

BACKGROUND

Increasing evidence has demonstrated that pyroptosis exerts key roles in the occurrence, development, and prognosis of uterine corpus endometrial carcinoma (UCEC). However, the mechanism of pyroptosis and its predictive value for prognosis remain largely unknown.

METHODS

UCEC data were acquired from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes in UCEC vs. normal cases were selected to perform a weighted correlation network analysis (WGCNA). Forty-two UCEC-associated pyroptosis-related genes were identified via applying differential expression analysis. Protein-protein interaction (PPI) and gene correlation analyses were applied to explore the relationship between 21 UCEC key genes and 42 UCEC-associated pyroptosis-related genes. The expression of 42 UCEC-associated pyroptosis-related genes of different grades was also calculated. The immune environment of UCEC was evaluated. Furthermore, pyroptosis-related genes were filtered out by the co-expression. Univariate and a least absolute shrinkage and selection operator (LASSO) Cox analyses were implemented to yield a pyroptosis-related gene model. We also performed consensus classification to regroup UCEC samples into two clusters. A clinically relevant heatmap and survival analysis curve were implemented to explore the clinicopathological features and relationship between two clusters. Furthermore, a Kaplan-Meier survival analysis was implemented to analyze the risk model.

RESULTS

Twenty-one UCEC key genes and 42 UCEC-associated pyroptosis-related genes were identified. The PPI and gene correlation analysis showed a clear relationship. The expression of 42 UCEC-associated pyroptosis-related genes of different grades was also depicted. A risk model based on pyroptosis-related genes was then developed to forecast overall survival among UCEC patients. Finally, Cox regression analysis verified this model as an independent risk factor for UCEC patients.

CONCLUSIONS

The expression of pyroptosis-related gene may influence UCEC occurrence, development, and prognosis.

Comprehensive Analysis of N6-Methylandenosine-Related Long Non-Coding RNAs Signature in Prognosis and Tumor Microenvironment of Bladder Cancer

To investigate the role of N6-methyladenosine (m6A)- related long non-coding RNAs (lncRNAs) in bladder cancer (BC). 50 m6A-related lncRNAs were screened out and were correlated with prognosis from BC samples in The Cancer Genome Atlas (TCGA). The lncRNAs were subdivided into cluster 1 and cluster 2 with consensus cluster analysis, and it was found that lncRNAs in cluster 2 were associated with poor prognosis and increased PD-L1 expression. Gene set enrichment analysis (GSEA) revealed tumor-related pathways in cluster 2. Through least absolute shrinkage and selection operator (LASSO) Cox regression analysis, univariate and multivariate Cox regression, and ROC analyses, 14 prognostic lncRNAs were selected and used to construct the m6A-related lncRNA prognostic signature (m6A-LPS), furthermore, that m6A-LPS was as a valuable independent prognostic factor. Interestingly, the m6A-LPS risk score was positively correlated with the immune score, PD-L1 expression, and the infiltration of immune cell subtypes in BC. SNHG16, a member of the high-risk group based on m6A-LPS, was highly expressed in BC tissues and cell lines and interfered with siRNA resulted in suppressed proliferation, migration, and invasion in vitro. Our study illustrates the role of m6A-related lncRNAs in BC. The m6A-LPS may be an important regulatory target of the tumor microenvironment (TME) in BC.

Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with low survival rate and poor prognosis. The traditional clinicopathological staging is insufficient to estimate the prognosis of UCEC. It is necessary to select a more effective prognostic signature of UCEC to predict the prognosis and immunotherapy effect of UCEC.

Methods

CIBERSORT and weighted correlation network analysis (WGCNA) algorithms were combined to screen modules related to regulatory T (Treg) cells. Subsequently, univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the genes in key modules. The difference in overall survival (OS) between high- and low-risk patients was analyzed by Kaplan-Meier analysis. The Tregs-related risk signature (TRRS) was screened by uni- and multivariate Cox analyses. Afterward, we analyzed the expression difference of TRRS and verified its ability to predict the prognosis of UCEC and the effect of immunotherapy.

Results

Red module has the highest correlation with Tregs among all clustered modules. Pathways enrichment indicated that the related processes of UCEC were primarily associated to the immune system. Eight genes (ZSWIM1, NPRL3, GOLGA7, ST6GALNAC4, CDC16, ITPK1, PCSK4, and CORO1B) were selected to construct TRRS. We found that this TRRS is a significantly independent prognostic factor of UCEC. Low-risk patients have higher overall survival than high-risk patients. The immune status of different groups was different, and tumor-related pathways were enriched in patients with higher risk score. Low-risk patients are more likely take higher tumor mutation burden (TMB). Meanwhile, they are more sensitive to chemotherapy than patients with high-risk score, which indicated a superior prognosis. Immune checkpoints such as PD-1, CTLA4, PD-L1, and PD-L2 all had a higher expression level in low-risk group. TRRS expression really has a relevance with the sensitivity of UCEC patients to chemotherapeutic drugs.

Conclusion

We developed and validated a TRRS to estimate the prognosis and reflect the immune status of UCEC, which could accurately assess the prognosis of patients with UCEC and supply personalized treatments for them.

Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer

Uterine corpus endometrial carcinoma locally infiltrates numerous immune cells and other tumor immune microenvironment components. These cells are involved in malignant tumor growth and proliferation and the process of resistance toward immunotherapies. Here, we aimed to develop a tumor immune microenvironment-related prognostic signature for high-risk grade III endometrial carcinoma based on The Cancer Genome Atlas. The signature was systematically correlated with immune infiltration characteristics of the tumor microenvironment. The seven-gene Riskscore signature was robust and performed well in training, testing, and Gene Expression Omnibus-independent cohorts. A nomogram comprising the gene signature accurately predicted patient prognosis, with our model performing better than other endometrial cancer-related signatures. Analysis of the IMvigor210 immunotherapy cohort revealed that subgroups with a low Riskscore had a better prognosis than subgroups with a high Riskscore. Subgroups with a low Riskscore exhibited immune cell infiltration and inflammatory profiles, whereas subgroups with a high Riskscore experienced progressive disease. The receiver operating characteristic curve indicated that risk score, neoantigen, and tumor mutation burden models together accurately predicted treatment response. Taken together, we developed a tumor microenvironment-based seven-gene prognostic stratification system to predict the prognosis of patients with high-risk endometrial cancer and guide more effective immunotherapy strategies.

Immune-related eight-lncRNA signature for improving prognosis prediction of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the leading cause of cancer‐related deaths worldwide. Therefore, the identification of a novel prediction signature for predicting the prognosis risk and survival outcomes is urgently demanded.

Methods

We integrated a machine‐learning frame by combing the Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression model to identify the LUAD‐related long non‐coding RNA (lncRNA) survival biomarkers. Subsequently, the Spearman correlation test was employed to interrogate the relationships between lncRNA signature and tumor immunity and constructed the competing endogenous RNA (ceRNA) network.

Results

Herein, we identified an eight‐lncRNA signature (PR‐lncRNA signature, NPSR1‐AS1, SATB2‐AS1, LINC01090, FGF12‐AS2, AC005256.1, MAFA‐AS1, BFSP2‐AS1, and CPC5‐AS1), which contributes to predicting LUAD patient's prognosis risk and survival outcomes. The PR‐lncRNA signature has also been confirmed as the robust signature in independent datasets. Further parsing of the LUAD tumor immune infiltration showed the PR‐lncRNAs were closely associated with the abundance of multiple immune cells infiltration and the expression of MHC molecules. Furthermore, by constructing the PR‐lncRNA–related ceRNA network, we interrogated more potential anti‐cancer therapy targets.

Conclusion

lncRNAs, as emerging cancer biomarkers, play an important role in a variety of cancer processes. Identification of PR‐lncRNA signatures allows us to better predict patient's survival outcomes and disease risk. Finally, the PR‐lncRNA signatures could help us to develop novel LUAD anti‐cancer therapeutic strategies.

Construction of a 5 immune-related lncRNA-based prognostic model of NSCLC via bioinformatics

Participate in tumorigenic, oncogenic, and tumor suppressive pathways through gene expression regulation. We aimed to build an immune-related long noncoding RNA (lncRNA) prognostic model to enhance nonsmall cell lung cancer (NSCLC) prognostic prediction.The original data were collected from the cancer genome atlas database. Perl and R software were used for statistical analysis. The effects of lncRNAs expression on prognosis were analyzed by Gene Expression Profiling Interactive Analysis. Silico functional analysis were performed by DAVID Bioinformatics Resources.The median risk score as a dividing value separated patients into high- and low-risk groups. These 2 groups had different 5-year survival rates, median survival times, and immune statuses. The 5-lncRNA signature was validated as an independent prognostic factor with high accuracy (area under the receiver operating characteristic = 0.722). Silico functional analysis connected the lncRNAs with immune-related biological processes and pathways in carcinogenesis.The novel immune-related lncRNA prognostic model had significant clinical implication for enhancing lung adenocarcinoma outcome prediction and guiding the choice of treatment.

Identification and validation of tumor microenvironment-related lncRNA prognostic signature for uveal melanoma

AIM

To investigate the role of tumor microenvironment (TME)-related long non-coding RNA (lncRNA) in uveal melanoma (UM), probable prognostic signature and potential small molecule drugs using bioinformatics analysis.

METHODS

UM expression profile data were downloaded from the Cancer Genome Atlas (TCGA) and bioinformatics methods were used to find prognostic lncRNAs related to UM immune cell infiltration. The gene expression profile data of 80 TCGA specimens were analyzed using the single sample Gene Set Enrichment Analysis (ssGSEA) method, and the immune cell infiltration of a single specimen was evaluated. Finally, the specimens were divided into high and low infiltration groups. The differential expression between the two groups was analyzed using the R package 'edgeR'. Univariate, multivariate and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses were performed to explore the prognostic value of TME-related lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses were also performed. The Connectivity Map (CMap) data set was used to screen molecular drugs that may treat UM.

RESULTS

A total of 2393 differentially expressed genes were identified and met the criteria for the low and high immune cell infiltration groups. Univariate Cox analysis of lncRNA genes with differential expression identified 186 genes associated with prognosis. Eight prognostic markers of TME-included lncRNA genes were established as potentially independent prognostic elements. Among 269 differentially expressed lncRNAs, 69 were up-regulated and 200 were down-regulated. Univariate Cox regression analysis of the risk indicators and clinical characteristics of the 8 lncRNA gene constructs showed that age, TNM stage, tumor base diameter, and low and high risk indices had significant prognostic value. We screened the potential small-molecule drugs for UM, including W-13, AH-6809 and Imatinib.

CONCLUSION

The prognostic markers identified in this study are reliable biomarkers of UM. This study expands our current understanding of the role of TME-related lncRNAs in UM genesis, which may lay the foundations for future treatment of this disease.

Predicting the immune landscape of invasive breast carcinoma based on the novel signature of immune-related lncRNA

Background

The composition of the population of immune‐related long non‐coding ribonucleic acid (irlncRNA) generates a signature, irrespective of expression level, with potential value in predicting the survival status of patients with invasive breast carcinoma.

Methods

The current study uses univariate analysis to identify differentially expressed irlncRNA (DEirlncRNA) pairs from RNA‐Seq data from The Cancer Genome Atlas (TCGA). 36 pairs of DEirlncRNA pairs were identified. Using various algorithms to construct a model, we have compared the area under the curve and calculated the 5‐year curve of Akaike information criterion (AIC) values, which allows determination of the threshold indicating the maximum value for differentiation. Through cut‐off point to establish the optimal model for distinguishing high‐risk or low‐risk groups among breast cancer patients. We assigned individual patients with invasive breast cancer to either high risk or low risk groups depending on the cut‐off point, re‐evaluated the tumor immune cell infiltration, the effectiveness of chemotherapy, immunosuppressive biomarkers, and immunotherapy.

Results

After re‐assessing patients according to the threshold, we demonstrated an effective means of distinguish the severity of the disease, and identified patients with different clinicopathological characteristics, specific tumor immune infiltration states, high sensitivity to chemotherapy,wellpredicted response to immunotherapy and thus a more favorable survival outcome.

Conclusions

The current study presents novel findings regarding the use of irlncRNA without the need to predict precise expression levels in the prognosis of breast cancer patients and to indicate their suitability for anti‐tumor immunotherapy.

Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma

Endometrial carcinoma (EC) is a common malignancy of female genital system which exhibits a unique immune profile. It is a promising strategy to quantify immune patterns of EC for predicting prognosis and therapeutic efficiency. Here, we attempted to identify the possible immune microenvironment-related prognostic markers of EC. We obtained the RNA sequencing and corresponding clinical data of EC from TCGA database. Then, 3 immune scores based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm were computed. Correlation between above ESTIMATE scores and other immune-related scores, molecular subtypes, prognosis, and gene mutation status (including BRCA and TP53) were further analyzed. Afterwards, gene modules associated with the ESTIMATE scores were screened out through hierarchical clustering analysis and weighted gene co-expression network analysis (WGCNA). Differentially expressed analysis was performed and genes shared by the most relevant modules were found out. KEGG pathway enrichment analysis was conducted to explore the biological functions of those genes. Survival analysis was carried out to identify prognostic immune-related genes and GSE17025 database was further used to confirm the correlation between immune-related genes and the ImmuneScore. The immune-related scores based on ESTIMATE algorithm was closely related to the immune microenvironment of EC. 3 gene modules that had the closest correlations with 3 ESTIMATE scores were obtained. 109 immune-related genes were preliminarily found out and 29 pathways were significantly enriched, most of which were associated with immune response. Univariate survival analysis revealed that there were 14 genes positively associated with both OS and PFS. Among which, 11 genes showed marked correlations with ImmuneScore values in GSE17025 database. Our current study profiled the immune status and identified 14 novel immune-related prognostic biomarkers for EC. Our findings may help to investigate the complicated tumor microenvironment and develop novel individualized therapeutic targets for EC.

Identification of lncRNA Signature Associated With Pan-Cancer Prognosis

Long noncoding RNAs (lncRNAs) have emerged as potential prognostic markers in various human cancers as they participate in many malignant behaviors. However, the value of lncRNAs as prognostic markers among diverse human cancers is still under investigation, and a systematic signature based on these transcripts that related to pan-cancer prognosis has yet to be reported. In this study, we proposed a framework to incorporate statistical power, biological rationale, and machine learning models for pan-cancer prognosis analysis. The framework identified a 5-lncRNA signature (ENSG00000206567, PCAT29, ENSG00000257989, LOC388282, and LINC00339) from TCGA training studies (n = 1,878). The identified lncRNAs are significantly associated (all P ≤ 1.48E-11) with overall survival (OS) of the TCGA cohort (n = 4,231). The signature stratified the cohort into low- and high-risk groups with significantly distinct survival outcomes (median OS of 9.84 years versus 4.37 years, log-rank P = 1.48E-38) and achieved a time-dependent ROC/AUC of 0.66 at 5 years. After routine clinical factors involved, the signature demonstrated better performance for long-term prognostic estimation (AUC of 0.72). Moreover, the signature was further evaluated on two independent external cohorts (TARGET, n = 1,122; CPTAC, n = 391; National Cancer Institute) which yielded similar prognostic values (AUC of 0.60 and 0.75; log-rank P = 8.6E-09 and P = 2.7E-06). An indexing system was developed to map the 5-lncRNA signature to prognoses of pan-cancer patients. In silico functional analysis indicated that the lncRNAs are associated with common biological processes driving human cancers. The five lncRNAs, especially ENSG00000206567, ENSG00000257989 and LOC388282 that never reported before, may serve as viable molecular targets common among diverse cancers.

Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma

BACKGROUND

An increasing number of studies have indicated that the abnormal expression of certain long noncoding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with myeloma.

METHODS

Gene expression data of myeloma patients were downloaded from the Gene Expression Omnibus (GEO) database (GSE4581 and GSE57317). Cox regression analysis, Kaplan-Meier, and receiver operating characteristic (ROC) analysis were performed to construct and validate the prediction model. Single sample gene set enrichment (ssGSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to predict the function of a specified lncRNA.

RESULTS

In this study, a seven-lncRNA signature was identified and used to construct a risk score system for myeloma prognosis. This system was used to stratify patients with different survival rates in the training set into high-risk and low-risk groups. Test set, the entire test set, the external validation set, and the myeloma subtype achieved the authentication of the results. In addition, functional enrichment analysis indicated that 7 prognostic lncRNAs may be involved in the tumorigenesis of myeloma through cancer-related pathways and biological processes. The results of the immune score showed that IF_I was negatively correlated with the risk score. Compared with the published gene signature, the 7-lncRNA model has a higher C-index (above 0.8).

CONCLUSION

In summary, our data provide evidence that seven lncRNAs could be used as independent biomarkers to predict the prognosis of myeloma, which also indicated that these 7 lncRNAs may be involved in the progression of myeloma.

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.

A 4-gene signature predicts prognosis of uterine serous carcinoma

Background

Uterine serous carcinoma (USC) is an aggressive type of endometrial cancer that accounts for up to 40% of endometrial cancer deaths, creating an urgent need for prognostic biomarkers.

Methods

USC RNA-Seq data and corresponding patients’ clinical records were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression datasets. Univariate cox, Lasso, and Multivariate cox regression analyses were conducted to forge a prognostic signature. Multivariable and univariable cox regression analysis and ROC curve evaluated the prediction efficiency both in the training and testing sets.

Results

We uncovered 1385 genes dysregulated in 110 cases of USC tissue relative to 113 cases of normal uterine tissue. Functional enrichment analysis of these genes revealed the involvement of various cancer-related pathways in USC. A novel 4-gene signature (KRT23, CXCL1, SOX9 and ABCA10) of USC prognosis was finally forged by serial regression analyses. Overall patient survival (OS) and recurrence-free survival (RFS) were significantly lower in the high-risk group relative to the low-risk group in both the training and testing sets. The area under the ROC curve of the 4-gene signature was highest among clinicopathological features in predicting OS and RFS. The 4-gene signature was found to be an independent prognostic indicator in USC and was a superior predictor of OS in early stage of USC.

Conclusions

Our findings highlight the potential of the 4-gene signature as a guide for personalized USC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07834-4.

An Integrated Autophagy-Related Long Noncoding RNA Signature as a Prognostic Biomarker for Human Endometrial Cancer: A Bioinformatics-Based Approach

Endometrial cancer is one of the most common malignant tumors, lowering the quality of life among women worldwide. Autophagy plays dual roles in these malignancies. To search for prognostic markers for endometrial cancer, we mined The Cancer Genome Atlas and the Human Autophagy Database for information on endometrial cancer and autophagy-related genes and identified five autophagy-related long noncoding RNAs (lncRNAs) (LINC01871, SCARNA9, SOS1-IT1, AL161618.1, and FIRRE). Based on these autophagy-related lncRNAs, samples were divided into high-risk and low-risk groups. Survival analysis showed that the survival rate of the high-risk group was significantly lower than that of the low-risk group. Univariate and multivariate independent prognostic analyses showed that patients' age, pathological grade, and FIGO stage were all risk factors for poor prognosis. A clinical correlation analysis of the relationship between the five autophagy-related lncRNAs and patients' age, pathological grade, and FIGO stage was also per https://orcid.org/0000-0001-7090-1750 formed. Histopathological assessment of the tumor microenvironment showed that the ESTIMATE, immune, and stromal scores in the high-risk group were lower than those in the low-risk group. Principal component analysis and functional annotation were performed to confirm the correlations. To further evaluate the effect of the model constructed on prognosis, samples were divided into training (60%) and validation (40%) groups, regarding the risk status as an independent prognostic risk factor. A prognostic nomogram was constructed using patients' age, pathological grade, FIGO stage, and risk status to estimate the patients' survival rate. C-index and multi-index ROC curves were generated to verify the stability and accuracy of the nomogram. From this analysis, we concluded that the five lncRNAs identified in this study could affect the incidence and development of endometrial cancer by regulating the autophagy process. Therefore, these molecules may have the potential to serve as novel therapeutic targets and biomarkers.

The Evaluation of AREG, MMP-2, CHI3L1, GFAP, and OPN Serum Combined Value in Astrocytic Glioma Patients' Diagnosis and Prognosis

Gliomas account for approximately 70% of primary brain tumors in adults. Of all gliomas, grade IV astrocytoma, also called glioblastoma, has the poorest overall survival, with <5% of patients surviving five years after diagnosis. Due to the aggressiveness, lethal nature, and impaired surgical accessibility of the tumor, early diagnosis of the tumor and, in addition, prediction of the patient's survival time are important. We hypothesize that combining the protein level values of highly recognizable glioblastoma serum biomarkers could help to achieve higher specificity and sensitivity in predicting glioma patient outcome as compared to single markers. The aim of this study was to select the most promising astrocytoma patient overall survival prediction variables from five secretory proteins-glial fibrillary acidic protein (GFAP), matrix metalloproteinase-2 (MMP-2), chitinase 3-like 1 (CHI3L1), osteopontin (OPN), and amphiregulin (AREG)-combining them with routinely used tumor markers to create a Patient Survival Score calculation tool. The study group consisted of 70 astrocytoma patients and 31 healthy controls. We demonstrated that integrating serum CHI3L1 and OPN protein level values and tumor isocitrate dehydrogenase 1 IDH1 mutational status into one parameter could predict low-grade astrocytoma patients' two-year survival with 93.8% accuracy.

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).

A four-methylated LncRNA signature predicts survival of osteosarcoma patients based on machine learning

Risk stratification using prognostic markers facilitates clinical decision-making in treatment of osteosarcoma (OS). In this study, we performed a comprehensive analysis of DNA methylation and transcriptome data from OS patients to establish an optimal methylated lncRNA signature for determining OS patient prognosis. The original OS datasets were downloaded from the the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Univariate, Lasso, and machine learning algorithm-iterative Lasso Cox regression analyses were used to establish a methylated lncRNA signature that significantly correlated with OS patient survival. The validity of this signature was verified by the Kaplan-Meier curves, Receiver Operating Characteristic (ROC) curves. We established a four-methylated lncRNA signature that can predict OS patient survival (verified in independent cohort [GSE39055]). Kaplan-Meier analysis showed that the signature can distinguish between the survival of high- and low-risk patients. ROC analysis corroborated this finding and revealed that the signature had higher prediction accuracy than known biomarkers. Kaplan-Meier analysis of the clinical subgroup showed that the signature's prognostic ability was independent of clinicopathological factors. The four-methylated lncRNA signature is an independent prognostic biomarker of OS.

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.

Identification of a TLR-Induced Four-lncRNA Signature as a Novel Prognostic Biomarker in Esophageal Carcinoma

Long non-coding RNAs (lncRNAs) have emerged as key regulators of Toll-like receptor (TLR) signaling to control innate immunity, and this regulatory mechanism has recently been implicated in esophageal carcinoma (ESCA). However, a comprehensive analysis of TLR-induced lncRNAs and their roles in diagnosis and prognosis in ESCA is still lacking. In this study, we first investigated the precise relationship between lncRNA perturbations and alteration of TLR signaling by constructing the lncRNA-TLRs co-expression network involved in ESCA, and identified 357 TLR-related lncRNAs. Of them, four TLR-related lncRNAs (AP000696.1, LINC00689, LINC00900, and AP000487.1) are significantly associated with the overall survival (OS) of ESCA patients, and utilizing this four-lncRNA signature is capable of stratifying patients into high-risk and low-risk groups with significantly different OS in the discovery set. Further analysis in different independent patient sets also confirmed the robustness of the prognostic value of the four-TLR-lncRNA signature in predicting the OS of ESCA patients. Moreover, the results of multivariate analysis in different patient sets indicated that the four-TLR-lncRNA signature is an independent factor after adjusted by other clinical factors. Thus, we have identified a TLR-induced four-lncRNA signature, which represents a promising prognosis biomarker for ESCA, and our study might provide new candidate targets for therapeutic intervention via targeting TLR-induced lncRNAs in ESCA patients.

Characterization of lncRNA-Associated ceRNA Network to Reveal Potential Prognostic Biomarkers in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most fatal malignant tumors harmful to human health. The complexity and behavior characteristics of long-non-coding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) network in LUAD patients are still unclear. The purpose of this study was to elucidate the regulatory networks of dysregulated RNAs, view, and identify potential prognosis signatures involved in LUAD. The expression profiles of mRNAs, lncRNAs, and miRNAs were obtained from the TCGA database. In total, 2078 DEmRNAs, 257 DElncRNAs, and 101 DEmiRNAs were sorted out. A PPI network including 45 DEmRNAs was constructed. Ten hub genes in the PPI network associated with cell cycle-related pathways were identified and they played key roles in regulating cell proliferation. A total of three DEmiRNAs, seven DElncRNAs, and six DEmRNAs were enrolled in the ceRNA network. Except for certain genes without any published study reports, all the genes in the ceRNA network played an essential role in controlling tumor cell proliferation and were associated with prognosis in LUAD. Finally, based on step regression and Cox regression survival analysis, we identified four candidate biomarkers, including miR490, miR1293, LINC01740, and IGF2BP1, and established a risk model based on the four genes. Our study provided a global view and systematic dissection of the lncRNA-associated ceRNA network, and the identified four genes might be novel important prognostic factors involved in LUAD pathogenesis.

A novel alternative splicing-based prediction model for uteri corpus endometrial carcinoma

Alternative splicing (AS) is crucial a mechanism by which the complexity of mammalian and viral proteom increased overwhelmingly. There lacks systematic and comprehensive analysis of the prognostic significance of AS profiling landscape for uteri corpus endometrial carcinoma (UCEC). In this study, univariate and multivariate Cox regression analyses were conducted to identify candidate survival-associated AS events curated from SpliceSeq for the construction of prognostic index (PI) models. A correlation network between splicing factor-related AS events and significant survival-associated AS events were constructed using Cytoscape 3.5. As consequence, 28281 AS events from 8137 genes were detected from 506 UCEC patients, including 2630 survival-associated AS events. Kaplan Meier survival analysis revealed that six of the seven PI models (AD, AP, AT, ME, RI and ALL) exhibited good performance in stratifying the prognosis of low risk and high risk group (P<0.05). Among the six PI models, PI-AT performed best with an area under curves (AUC) value of 0.758 from time-dependent receiver operating characteristic. Correlation network implicated potential regulatory mechanism of AS events in UCEC. PI models based on survival-associated AS events for UCEC in this study showed preferable prognosis-predicting ability and may be promising prognostic indicators for UCEC patients.

Summary: This is the first study to systematically investigate the prognostic value of AS in UCEC. Findings in the presents study supported the clinical potential of AS for UCEC and shed light on the potential AS-associated molecular basis of UCEC.

Long non-coding RNA CCAT1 is overexpressed in endometrial cancer and regulates growth and transcriptome of endometrial adenocarcinoma cells

BACKGROUND

Long non-coding RNAs (lncRNAs) play important roles in regulation of gene expression and are involved in pathogenesis of different diseases including cancer. Recent studies suggested the lncRNA Colon cancer associated transcript-1 (CCAT1) to act as putative oncogene. In this study, to elucidate the role of this lncRNA in endometrial cancer, we examined its expression in normal endometrium and type 1 endometrial cancer and knocked down its expression in endometrial cancer cell lines followed by transcriptome and pathway analyses.

METHODS

CCAT1 expression was examined in 100 tissue samples of normal endometrium and type 1 endometrial cancer tissues by means of RT-qPCR. Knockdown of CCAT1 expression in HEC-1B and RL95/2 endometrial cancer cells was performed by siRNA transfection. Affymetrix GeneChip arrays were used to elucidate the effect of both lncRNAs on the transcriptome of these cell lines.

RESULTS

Median CCAT1 expression was found to be 9.3-fold higher in endometrial cancer when compared to normal endometrium (p < 0.05). In contrast to premenopausal endometrium and G1, G2 and G3 graded endometrial cancer, CCAT1 expression was nearly absent in postmenopausal tissue. Knockdown of CCAT1 by transient siRNA transfection significantly reduced proliferation of HEC-1B cancer cells in vitro by 35.5 % 6 days after transfection and notably reduced their colony formation ability. Affymetrix microarray and Ingenuity pathway analyses revealed a set of up- or down-regulated genes in transfected ERα-negative HEC-1B cells forming a network controlled by the key regulators TNF and TP53, including genes known to be involved in growth control, providing putative molecular mechanisms underlying the observed growth inhibition of HEC-1B cells. In contrast, CCAT1 knockdown in ERα-positive RL95/2 cells did not significantly affect proliferation, but resulted in down-regulation of a network of ERα target genes.

CONCLUSIONS

Given that the lncRNA CCAT1 was found to be overexpressed in endometrial cancer, affected the growth of HEC-1B cells and the expression of growth regulatory genes, our data suggest CCAT1 to exert oncogenic functions in endometrial cancer and encourage further studies to examine to what extent this lncRNA might be a potential therapy target in this cancer entity.

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.

Construction of a prognosis‑associated long noncoding RNA‑mRNA network for multiple myeloma based on microarray and bioinformatics analysis

At present, the association between prognosis-associated long noncoding RNAs (lncRNAs) and mRNAs is yet to be reported in multiple myeloma (MM). The aim of the present study was to construct prognostic models with lncRNAs and mRNAs, and to map the interactions between these lncRNAs and mRNAs in MM. LncRNA and mRNA data from 559 patients with MM were acquired from the Genome Expression Omnibus (dataset GSE24080), and their prognostic values were calculated using the survival package in R. Multivariate Cox analysis was used on the top 20 most significant prognosis-associated mRNAs and lncRNAs to develop prognostic signatures. The performances of these prognostic signatures were tested using the survivalROC package in R, which allows for time-dependent receiver operator characteristic (ROC) curve estimation. Weighted correlation network analysis (WGCNA) was conducted to investigate the associations between lncRNAs and mRNAs, and a lncRNA-mRNA network was constructed using Cytoscape software. Univariate Cox regression analysis identified 39 lncRNAs and 1,445 mRNAs that were significantly associated with event-free survival of MM patients. The top 20 most significant survival-associated lncRNAs and mRNAs were selected as candidates for analyzing independent MM prognostic factors. Both signatures could be used to separate patients into two groups with distinct outcomes. The areas under the ROC curves were 0.739 for the lncRNA signature and 0.732 for the mRNA signature. In the lncRNA-mRNA network, a total of 143 mRNAs were positively or negatively associated with 23 prognosis-associated lncRNAs. NCRNA00201, LOC115110 and RP5-968J1.1 were the most dominant drivers. The present study constructed a model that predicted prognosis in MM and formed a network with the corresponding prognosis-associated mRNAs, providing a novel perspective for the clinical diagnosis and treatment of MM, and suggesting novel directions for interpreting the mechanisms underlying the development of MM.

Integrative Analysis of Methylation and Transcriptome Identified Epigenetically Regulated lncRNAs With Prognostic Relevance for Thyroid Cancer

Emerging evidence has shown that epigenetic changes in DNA methylation, an important regulator of long non-coding RNA (lncRNA) expression, can disturb the expression patterns of lncRNAs and contribute to carcinogenesis. However, knowledge about crosstalk effects between DNA methylation and lncRNA regulation in thyroid cancer (THCA) remain largely unknown. In this study, we performed an integrated analysis of methylation and the transcriptome and identified 483 epigenetically regulated lncRNAs (EpilncRNAs) associated with the development and progression of THCA. These EpilncRNAs can be divided into two categories based on their methylation and expression patterns: 228 HyperLncRNAs and 255 HypoLncRNAs. Then, we identified a methylation-driven 5-lncRNA-based signature (EpiLncPM) to improve prognosis prediction using the random survival forest and multivariate Cox analysis, which were then validated using the training dataset [Hazard ratio (HR) = 50.097, 95% confidence interval (CI): 10.231-245.312, p < 0.001] and testing dataset (HR = 4.395, 95% CI: 0.981-19.686, p = 0.053). Multivariate analysis suggested that the EpiLncPM is an independent prognostic factor. By performing a functional enrichment analysis of GO and KEGG for mRNAs co-expressed with the EpiLncPM, we found that the EpiLncPM was involved in immune and inflammatory-related biological processes. Finally, in situ hybridization analysis in 119 papillary thyroid carcinoma (PTC) tissues and paired adjacent normal tissues revealed that selected candidate lncRNA AC110011 has significantly higher expression of PTC compared to adjacent non-neoplastic tissues, and was closely related to the tumor size, lymph node metastasis, and extrathyroidal extension. In summary, our study characterized the crosstalk between DNA methylation and lncRNA, and provided novel biomarkers for the prognosis of THCA.

Comprehensive Analysis of Aberrantly Expressed Profiles of lncRNAs and miRNAs with Associated ceRNA Network in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Lung cancer (LC) continues to be the leading cause of cancer-related deaths worldwide and the prognosis remains poor worldwide. At present, the long non-coding RNAs (lncRNAs) was considered as a part of competing endogenous RNA (ceRNA) network act as natural microRNA (miRNA) sponges to regulate protein-coding gene expression. However, functional roles of lncRNA-mediated ceRNAs in LC are insufficiently understood. To classify the specific mechanism of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we comprehensively compared the expression profiles of mRNAs, lncRNAs and miRNAs obtained from 509 LUAD, 473 LUSC tissues and 49 adjacent non-cancerous lung tissues, based on The Cancer Genome Atlas (TCGA). After screening for differently expressed (DE) mRNAs, DEmiRNAs, DElncRNAs and weighted gene co-expression network analysis (WGCNA) (|log2FC| > 2.0 and an adjusted p value <0.05), a total of 4478 DEmRNAs, 526 DElncRNAs and 75 DEmiRNAs in LUAD, while 6237 DEmRNAs, 843 DElncRNAs and 117 DEmiRNAs in LUSC were discovered. Interaction (PPI) network analysis was performed to identify 656 nodes and 2987 edges (minimum required interaction score > 0.9), as well as 8 different protein-protein interactions. Gene ontology (GO) analysis mainly associated with cell proliferation. KEGG pathway enrichment analyses most partly associated with metabolism pathway and cytokine-cytokine receptor interaction. Finally, the dysregulated lncRNA-miRNA-ceRNA network was constructed based on correlation analyses and a total of 62 dysregulated lncRNAs, 28 DEmRNAs and 18 DEmiRNAs were involved. The most significant lncRNAs included DElncRNAs, LINC00641 and AC004947.2, miRNAs included miR-6860, miR-1285-3p, miR-767-3p and miR-7974, mRNAs included MAP3K3, FGD3 and ATP1B2. Then we analyzed and described the potential characteristics of biological function and pathological roles of the LUAD and LUSC ceRNA co-regulatory network. Our findings revealed ceRNA network will be beneficial for promoting the understanding of lncRNA-mediated ceRNA regulatory mechanisms in the pathogenesis of LUAD and LUSC.

Upregulation of long non-coding RNA OGFRP1 facilitates endometrial cancer by regulating miR-124-3p/SIRT1 axis and by activating PI3K/AKT/GSK-3β pathway

We planned to investigate the possible influences of long non-coding RNA (opioid growth factor receptor pseudogene 1) OGFRP1 in endometrial cancer and its potential regulatory mechanism. We measured the level of OGFRP1 in endometrial cancer tissues and evaluated the influences of OGFRP1 dysregulation on the tumour cell biological processes of endometrial cancer cells. Further, the regulatory relationships between OGFRP1 and miR-124-3p, between miR-124-3p and Sirtuin1 (SIRT1) were, respectively, investigated. The interaction between OGFRP1 dysregulation and activation of PI3K/AKT/GSK-3β pathway was revealed by Western blotting. OGFRP1 was up-regulated in endometrial cancer tissues and cells. OGFRP1 suppression inhibited the malignant behaviour (inhibited cell viability, promoted cell apoptosis, and suppressed cell migration and invasion) of the Ishikawa cells via negatively regulating miR-124-3p. SIRT1 was a target gene of miR-124-3p, and miR-124-3p regulated tumour growth and metastasis by the down-stream signal of SIRT1. Moreover, suppression of OGFRP1 restrained the activation of PI3K/AKT/GSK-3β signals in the Ishikawa cells via miR-124-3p/SIRT1 axis. Our experiments revealed that upregulation of OGFRP1 may enhance the progression of endometrial cancer by regulating miR-124-3p/SIRT1 axis and by activating PI3K/AKT/GSK-3β pathway. OGFRP1 may be of significance in illustrating the biology of endometrial cancer.

Investigation of the Clinical Significance and Prognostic Value of the lncRNA ACVR2B-As1 in Liver Cancer

A refined liver cancer staging system and effective prognostic prediction can help clinicians make optimized treatment decisions, which is essential in our fight against cancer and for improving the unsatisfying survival rate of liver cancer globally. The prognosis of liver cancer is not only related to tumor status, it is also affected by the patients' liver functions and the chosen treatment. Currently, several staging systems are being tested. Herein, we analyzed RNA-seq data from the TCGA database and identified a newly annotated lncRNA, ACVR2B-AS1, whose expression is upregulated in liver cancer. Higher ACVR2B-AS1 expression is an independent adverse prognostic factor for overall survival (OS) and relapse-free survival (RFS) in liver cancer patients. Our work suggests that the lncRNA ACVR2B-AS1 could be a candidate biomarker for liver cancer prognosis. Furthermore, ACVR2B-AS1 might serve as a potential therapeutic target, which is a possibility that is worthy of further study.

Comprehensive Cis-Regulation Analysis of Genetic Variants in Human Lymphoblastoid Cell Lines

Genetic variants can influence the expression of mRNA and protein. Genetic regulatory loci such as expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTLs) exist in several species. However, it remains unclear how human genetic variants regulate mRNA and protein expression. Here, we characterized six mechanistic models for the genetic regulatory patterns of single-nucleotide polymorphisms (SNPs) and their actions on post-transcriptional expression. Data from Yoruba HapMap lymphoblastoid cell lines were analyzed to identify human cis-eQTLs and pQTLs, as well as protein-specific QTLs (psQTLs). Our results indicated that genetic regulatory loci primarily affected mRNA and protein abundance in patterns where the two were well-correlated. While this finding was observed in both humans and mice (57.5% and 70.3%, respectively), the genetic regulatory patterns differed between species, implying evolutionary differences. Mouse SNPs generally targeted changes in transcript expression (51%), whereas in humans, they largely regulated protein abundance, independent of transcription levels (55.9%). The latter independent function can be explained by psQTLs. Our analysis suggests that local functional genetic variants in the human genome mainly modulate protein abundance independent of mRNA levels through post-transcriptional mechanisms. These findings clarify the impact of genetic variation on phenotype, which is of particular relevance to disease risk and treatment response.

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.

Systematic Characterization of Circular RNA-Associated CeRNA Network Identified Novel circRNA Biomarkers in Alzheimer's Disease

Alzheimer's disease (AD), a degenerative disease of the central nervous system, is the most common form of dementia in old age. The complexity and behavior of circular RNA (circRNA)-associated competing endogenous RNA (ceRNA) network remained poorly characterized in AD. The aim of this study was to elucidate the regulatory networks of dysregulated circRNAs from ceRNA view and identify potential risk circRNAs involved in AD pathogenesis. Consistent differentially expressed genes (CDEGs) were obtained using meta-analysis for multiple microarrays, and differentially expressed miRNAs (DEmiRs) were identified using empirical Bayes method. The circRNA-associated ceRNA network (cirCeNET) was constructed based on “ceRNA hypothesis” using an integrated system biology method. A total of 1,872 CDEGs and 48 DEmiRs were screened across different datasets. By mapping CDEGs and DEmiRs into the cirCeNET, an AD-related circRNA-associated ceRNA network (ADcirCeNET) was constructed, including 3,907 edges and 1,407 nodes (276 circRNAs, 14 miRNAs and 1,117 mRNAs). By prioritizing AD risk circRNA-associated ceRNAs, we found that the circRNA KIAA1586 occurred most frequently in the AD risk circRNA-associated ceRNAs and function as a ceRNA that operates by competitively binding three known AD-risk miRNAs. In silico functional analysis suggested that circRNA KIAA1586-related ceRNA network was significantly enriched in known AD-associated biological processes. Our study provided a global view and systematic dissection of circRNA-associated ceRNA network. The identified circRNA KIAA1586 may be a key risk factor involved in AD pathogenesis.

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.

Integrative analysis of miRNA and mRNA expression profiles reveals a novel mRNA/miRNA signature to improve risk classification for patients with gastric cancer

Gastric cancer (GC) is one of the most common types of malignant cancer and is associated with poor prognosis. Although the prognosis of patients with GC is associated with grade, stage and lymph node metastases, these traditional clinical features are inadequate to predict the outcome of GC. Therefore, there has been an increased focus on identifying novel molecular biomarkers for early diagnosis and prognosis, in order to improve outcomes in GC. In the present study, an integrative analysis of microRNA (miRNA) expression profiles, mRNA expression profiles and clinical characteristics was performed in a large cohort of patients with GC in order to identify an integrative prognostic model for improving postoperative risk classification. An integrative mRNA/miRNA signature (IMMIS), comprised of three miRNAs and one mRNA, was identified from a large number of differentially expressed miRNAs and mRNAs using univariate and multivariate Cox regression analysis. The prognostic value of the IMMIS was validated in the discovery cohort, testing cohort and The Cancer Genome Atlas (TCGA) cohort. The present results suggested that the identified signature had a reliable predictive performance and could classify the patients into high- and low-risk groups with significantly different overall survival times. In the discovery cohort, the hazard ratio (HR) was 2.805 with a 95% CI=1.722–4.567 (P<0.001). The median overall survival time as 1.49 vs. 3.85 years. In the testing cohort, the HR was 1.625 with a 95% CI=1.004–2.638 (P=0.039) and the median overall survival time was 2.17 vs. 4.62 years. In the TCGA cohort, the HR was 2.139 with a 95% CI=1.519–3.012 (P<0.001) and the median overall survival time was 1.53 vs. 4.62 years. The IMMIS constituted a reliable independent prognostic factor compared with clinical covariates, including age, sex, grade and stage, as indicated by multivariate and stratified analyses. Furthermore, comparative analysis revealed that the predictive value of the IMMIS was superior to the mRNA-based signature alone. The present results suggested the potential value of the IMMIS as a promising novel biomarker for improving the clinical management of patients with GC.

Overexpression of HPIP as a biomarker for metastasis and prognosis prediction in endometrial cancer patients

BACKGROUND

Hematopoietic pre-B-cell leukemia transcription factor (PBX)-interacting protein (HPIP) has shown to be overexpressed in several human cancers. The purpose of this study was to explore the expression of HPIP in endometrial cancer (EC) and its associated effects on disease.

METHODS

A total of 113 EC patients at the Harbin Medical University Cancer Hospital between August 2011 and September 2012 were studied for immunohistochemistry analysis. HPIP expression was detected using real-time reverse transcription PCR, Western blotting, and immunohistochemistry. Prognostic value of HPIP expression was examined using multivariate Cox regression analysis and Kaplan-Meier method.

RESULTS

The result of Western blotting indicated that HPIP protein expression is significantly high in normal tissues compared to EC tissues (P < 0.001). The expression of HPIP was significantly associated with FIGO stage (P < 0.001), histological grade (P < 0.001), depth of myometrial invasion (P < 0.001), and lymph node metastasis (P = 0.033). Kaplan-Meier analysis demonstrated that there was a significant difference in overall survival and disease-free survival between the two groups of patients stratified by HPIP expression level (log-rank, both P = 0.002). Patients with HPIP high expression had significantly shorter median survival time than those with HPIP low expression. Moreover, results of the multivariate analysis revealed that HPIP expression was an independent prognostic factor for predicting overall survival (P = 0.015) and disease-free survival (P = 0.017) in patients with EC.

CONCLUSION

The present study provides evidence that HPIP predicts EC progression and poor survival, highlighting its potential as a therapeutic target for EC.

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 long noncoding RNAs signature to improve survival prediction in endometrioid endometrial cancer

PURPOSE

The dysregulation of long noncoding RNAs (lncRNAs) has been reported to be correlated to carcinogenesis and cancer progression. Endometrial cancer (EC), arising from the endometrium or the inner lining of the uterus, is one of the most common gynecological malignancies. We aim to explore the prognostic value of the lncRNAs in patients with endometrioid endometrial cancer (EEC) and to identify the potential lncRNA signature for predicting survival of patients with EEC.

METHODS

We performed a genome-wide analysis of the lncRNA expression profiling in The Cancer Genome Atlas (TCGA)-Uterine Corpus Endometrial Carcinoma database (408 EEC) to identify the prognosis related lncRNAs for the EEC. The patients with EEC were randomly divided into a training set (204 for endometrioid) and a testing set (204 for endometrioid). The lncRNA signature was identified in the training set, and then independently validated in the testing set and the complete set (training set plus testing set).

RESULTS

We developed a nine-lncRNA signature-based risk score in the patients with EEC. The risk score based on the novel lncRNAs signature was able to separate patients of training set into high-and low-risk groups with significantly different overall survival and progression-free survival. These can also be successfully confirmed in the testing set and complete set.

CONCLUSION

A nine-lncRNA expression signature was identified and validated which can predict EEC patient's survival. These findings may have important implications in the understanding of the potential therapeutic methods for patients with EEC.

High TRIM44 expression as a valuable biomarker for diagnosis and prognosis in cervical cancer

Tripartite motif containing 44 (TRIM44) has been reported to be up-regulated in multiple aggressive malignant tumors. However, its expression status and clinical significance in cervical cancer remain unknown. The purpose of this study was to investigate the clinical significance of TRIM44 expression and the prognosis in patients with cervical cancer (CC). Fresh frozen tissues from 5 samples of CC and 4 normal cervical tissues were analyzed for TRIM44 expression using RT- PCR and Western blot analysis. 122 paraffin-embedded surgical specimens from patients with CC were collected for an immunohistochemistry. TRIM44 expression was found to be significantly up-regulated in cervical cancer specimens compared with adjacent normal tissues (P<0.001). Statistical analysis showed that TRIM44 expression was significantly correlated with the International Federation of Gynecology and Obstetrics (FIGO) stage, histological grade and lymph node metastasis, but not with age, histological type, and tumor size. Kaplan–Meier survival analysis suggested that high TRIM44 expression was associated with poor prognosis. Patients highly expressing TRIM44 have significantly shorter overall survival (OS) (P=0.006) and disease-free survival (DFS) (P=0.002). Furthermore, multivariate Cox analysis showed TRIM44 was an independent risk factor for poor prognosis. Our study demonstrated that TRIM44 expression contributes to the progression of cervical cancer, and could be used as a marker of clinical diagnosis and prognosis of patients with cervical cancer.

Identification of a six-gene signature with prognostic value for patients with endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is frequently diagnosed among women worldwide. However, there are different prognostic outcomes because of heterogeneity. Thus, the aim of the current study was to identify a gene signature that can predict the prognosis of patients with UCEC. UCEC gene expression profiles were first downloaded from the The Cancer Genome Atlas (TCGA) database. After data processing and forward screening, 11 390 key genes were selected. The UCEC samples were randomly divided into training and testing sets. In total, 996 genes with prognostic value were then examined by univariate Cox survival analysis with a P-value <0.01 in the training set. Next, using robust likelihood-based survival modeling, we developed a six-gene signature (CTSW, PCSK4, LRRC8D, TNFRSF18, IHH, and CDKN2A) with a prognostic function in UCEC. A prognostic risk score system was developed by multivariate Cox proportional hazard regression based on this six-gene signature. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer overall survival (OS) outcomes than those in the low-risk group (log-rank test P-value <0.0001). This signature was further validated in the testing dataset and the entire TCGA dataset. In conclusion, we conducted an integrated study to develop a six-gene signature for the prognostic prediction of patients with UCEC. Our findings may provide novel biomarkers for prognosis and have significant implications in the understanding of therapeutic targets for UCEC.

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.

Recurrence-Associated Long Non-coding RNA Signature for Determining the Risk of Recurrence in Patients with Colon Cancer

Patients with colon cancer are often faced a high risk of disease recurrence within 5 years of treatment that is the major cause of cancer mortality. Reliable molecular markers were required to improve the most effective personalized therapy. Here, we identified a recurrence-associated six-lncRNA (long non-coding RNA) signature (LINC0184, AC105243.1, LOC101928168, ILF3-AS1, MIR31HG, and AC006329.1) that can effectively distinguish between high and low risk of cancer recurrence from 389 patients of a discovery dataset, and validated its robust performance in four independent datasets comprising a total of 906 colon cancer patients. We found that the six-lncRNA signature was an independent predictive factor of disease recurrence in multivariate analysis and was superior to the performance of clinical factors and known gene signature. Furthermore, in silico functional analysis showed that the six-lncRNA-signature-associated coding genes are significantly enriched in proliferation and angiogenesis, cell death, as well as critical cancer pathways that could play important roles in colon cancer recurrence. Together, the six-lncRNA signature holds great potential for recurrence risk assessment and personalized management of colon cancer patients.