Comprehensive investigation of a novel differentially expressed lncRNA expression profile signature to assess the survival of patients with colorectal adenocarcinoma

Screening key lncRNAs for human rectal adenocarcinoma based on lncRNA-mRNA functional synergistic network

Background

Rectal adenocarcinoma (READ) is one of the deadliest malignancies, and the molecular mechanisms underlying the initiation and development of READ remain largely unknown. In this study, we aimed to find key long noncoding RNAs (lncRNAs) and mRNAs in READ by RNA sequencing.

Methods

RNA sequencing was performed to identify differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) between READ and normal tissue. READ‐specific protein‐protein interaction (PPI), DElncRNA‐DEmRNA coexpression, and DElncRNA‐nearby DEmRNA interaction networks were constructed. DEmRNAs and DEmRNAs coexpressed with DElncRNAs were functionally annotated.

Results

A total of 2113 DEmRNAs and 150 DElncRNAs between READ and normal tissue were identified. The PPI network identified several hub proteins, including CDK1, AURKB, CDC6, FOXQ1, NUF2, and TOP2A. The DElncRNA‐DEmRNA coexpression and DElncRNA‐nearby DEmRNA interaction networks identified some hub lncRNAs, including CCAT1, LOC105374879, GAS5, and B3GALT5‐AS1. The colorectal cancer pathway, the intestinal immune network for IgA production and the p53 signaling pathway were three pathways significantly enriched in DEmRNAs and DEmRNAs coexpressed with DElncRNAs. MSH6 coexpressed with two DElncRNAs (LOC105374879 and CASC15) and BCL2 coexpressed with B3GALT5‐AS1 were significantly enriched in the colorectal cancer signaling pathway. TNFRSF17 coexpressed with B3GALT5‐AS1 was enriched in the intestinal immune network for IgA production. CCNB2 coexpressed with LOC105374879 was enriched in the p53 signaling pathway.

Conclusion

A total of four DEmRNAs (MSH6, BCL2, TNFRSF17, and CCNB2) and three DElncRNAs (LOC105374879, CASC15, and B3GALT5‐AS1) may be involved in the pathogenesis of READ; this data may contribute to understanding the mechanisms of READ and the development of therapeutic strategies for READ.

Integrated analysis of two-lncRNA signature as a potential prognostic biomarker in cervical cancer: a study based on public database

Background

Cervical cancer (CC) is a common gynecological malignancy in women worldwide. Evidence suggests that long non-coding RNAs (lncRNAs) can be used as biomarkers in patients with CC. However, prognostic biomarkers for CC are still lacking. The aim of our study was to find lncRNA biomarkers which are able to predict prognosis in CC based on the data from The Cancer Genome Atlas (TCGA).

Methods

The patients were divided into three groups according to FIGO stage. Differentially expressed lncRNAs were identified in CC tissue compared to adjacent normal tissues based on a fold change >2 and <0.5 at P < 0.05 for up- and downregulated lncRNA, respectively. The relationship between survival outcome and lncRNA expression was assessed with univariate and multivariate Cox proportional hazards regression analysis. We constructed a risk score as a method to evaluate prognosis. We used receiver operating characteristic (ROC) curve and the area under curve (AUC) analyses to assess the diagnostic value of a two-lncRNA signature. We detected the expression levels of the two lncRNAs in 31 pairs of newly diagnosed CC specimens and paired adjacent non-cancerous tissue specimens, and also in CC cell lines. Finally, the results were statistically compared using t-tests.

Results

In total, 289 RNA sequencing profiles and accompanying clinical data were obtained. We identified 49 differentially expressed lncRNAs, of which two related to overall survival (OS) in CC patients. These two lncRNAs (ILF3-AS1 and RASA4CP) were found together as a single prognostic signature. Meanwhile, the prognosis of patients with low-risk CC was better and positively correlated with OS (P < 0.001). Further analysis showed that the combined two-lncRNA expression signature could be used as an independent biomarker to evaluate the prognosis in CC. qRT-PCR results were consistent with TCGA, confirming downregulated expression of both lncRNAs. Furthermore, upon ROC curve analysis, the AUC of the combined lncRNAs was greater than that of the single lncRNAs alone (0.723 vs 0.704 and 0.685), respectively; P < 0.05.

Conclusions

Our study showed that the two-lncRNA signature of ILF3-AS1 and RASA4CP can be used as an independent biomarker for the prognosis of CC, based on bioinformatic analysis.

A Potential Prognostic Gene Signature for Predicting Survival for Glioblastoma Patients

OBJECTIVE

This study aimed to screen prognostic gene signature of glioblastoma (GBM) to construct prognostic model.

METHODS

Based on the GBM information in the Cancer Genome Atlas (TCGA, training set), prognostic genes (Set X) were screened by Cox regression. Then, the optimized prognostic gene signature (Set Y) was further screened by the Cox-Proportional Hazards (Cox-PH). Next, two prognostic models were constructed: model A was based on the Set Y; model B was based on part of the Set X. The samples were divided into low- and high-risk groups according to the median prognosis index (PI). GBM datasets in Gene Expression Ominous (GEO, GSE13041) and Chinese Glioma Genome Atlas (CGGA) were used as the testing datasets to confirm the prognostic models constructed based on TCGA.

RESULTS

We identified that the prognostic 14-gene signature was significantly associated with the overall survival (OS) in the TCGA. In model A, patients in high- and low-risk groups showed the significantly different OS (P = 7.47 × 10-9, area under curve (AUC) 0.995) and the prognostic ability were also confirmed in testing sets (P=0.0098 and 0.037). The model B in training set was significant but failed in testing sets.

CONCLUSION

The prognostic model which was constructed based on the prognostic 14-gene signature presented a high predictive ability for GBM. The 14-gene signature may have clinical implications in the subclassification of GBM.

HOTTIP Functions as a Key Candidate Biomarker in Head and Neck Squamous Cell Carcinoma by Integrated Bioinformatic Analysis

Background

Accumulating evidence has demonstrated the pivotal role of long noncoding RNAs (lncRNAs) in competing endogenous RNA (ceRNA) networks for predicting survival and evaluating prognosis in cancer patients. However, the pathogenesis of head and neck squamous cell carcinoma (HNSCC) remains unclear, and prognostic biomarkers for HNSCC are still lacking.

Methods

A total of 546 RNA sequencing profiles of HNSCC patients with clinical outcome data were obtained from the Cancer Genome Atlas (TCGA) database, providing a large sample of RNA sequencing data. From these, 71 Long noncoding RNAs lncRNAs, 8 microRNAs (miRNAs), and 16 messenger RNAs (mRNAs) were identified to construct a HNSCC-specific ceRNA network (fold change >2, P < 0.05). Univariate and multivariate Cox proportional regression models were used to assess independent indicators of prognosis. Then the expression of lncRNAs harboring prognostic value was validated in human HNSCC cell lines and tumor samples from our cohort and another two datasets from GEO (Gene Expression Omnibus) databases.

Results

As a result, a 3-mRNA signature and 6-lncRNA signature were identified. The six-lncRNA signature exhibited the highest prognostic value. Notably, in the six lncRNAs, HOTTIP showed the greatest prognostic value and was significantly correlated with clinical stage and histological grade of HNSCC patients. Furthermore, it was proved that HOTTIP was upregulated in HNSCC cell lines and cancerous tissues compared with corresponding normal cell lines and normal tissues. Functional assessment analysis revealed that HOTTIP might play a key role in the oncogenesis and progression of HNSCC.

Conclusion

The present study deepened our understanding of the ceRNA-related regulatory mechanism in the pathogenesis of HNSCC and identified candidate prognostic biomarkers for clinical outcome prediction in HNSCC. HOTTIP may function as a key candidate biomarker in HNSCC and serve as a prognostic marker for HNSCC patients.

Development and internal validation of a nine-lncRNA prognostic signature for prediction of overall survival in colorectal cancer patients

Background

Colorectal cancer remains a serious public health problem due to the poor prognosis. In the present study, we attempted to develop and validate a prognostic signature to predict the individual mortality risk in colorectal cancer patients.

Materials and Methods

The original study datasets were downloaded from The Cancer Genome Atlas database. The present study finally included 424 colorectal cancer patients with wholly gene expression information and overall survival information.

Results

A nine-lncRNA prognostic signature was built through univariate and multivariate Cox proportional regression model. Time-dependent receiver operating characteristic curves in model cohort demonstrated that the Harrell's concordance indexes of nine-lncRNA prognostic signature were 0.768 (95% CI [0.717-0.819]), 0.778 (95% CI [0.727-0.829]) and 0.870 (95% CI [0.819-0.921]) for 1-year, 3-year and 5-year overall survival respectively. In validation cohort, the Harrell's concordance indexes of nine-lncRNA prognostic signature were 0.761 (95% CI [0.710-0.812]), 0.801 (95% CI [0.750-0.852]) and 0.883 (95% CI [0.832-0.934]) for 1-year, 3-year and 5-year overall survival respectively. According to the median of nine-lncRNA prognostic signature score in model cohort, 424 CRC patients could be stratified into high risk group (n = 212) and low risk group (n = 212). Kaplan-Meier survival curves showed that the overall survival rate of high risk group was significantly lower than that of low risk group (P < 0.001).

Discussion

The present study developed and validated a nine-lncRNA prognostic signature for individual mortality risk assessment in colorectal cancer patients. This nine-lncRNA prognostic signature is helpful to evaluate the individual mortality risk and to improve the decision making of individualized treatments in colorectal cancer patients.

Prognostic value of small nuclear RNAs (snRNAs) for digestive tract pan- adenocarcinomas identified by RNA sequencing data

Malignant tumors of the digestive tract include esophageal, gastric, and colorectal carcinomas, which all have high global mortality rates. A clinical role for small nuclear RNA (snRNA), a type of small non-coding RNA, has not yet been documented for digestive tract pan-adenocarcinomas. Therefore, the aim of the study was to identify differentially expressed snRNAs and to explore their prognostic implications in pan-adenocarcinomas from the esophagus, stomach, colon, and rectum. The pan-carcinoma RNA-sequencing data of four types of digestive tract cancers with 1, 102 cases obtained from The Cancer Genome Atlas (TCGA) project were analyzed and the differentially expressed snRNAs were evaluated using the edgeR package. The prognostic value of each of the selected snRNAs was determined by univariate and multivariate Cox regression analyses. All the digestive tract pan-adenocarcinomas showed differential expression of three snRNAs: the up-regulated RNU1-106 P and RNU6-850 P and the down-regulated RNU6-529 P. Interestingly, RNU6-101 P appeared to be a risk factor for esophageal adenocarcinoma (ESAD) and RNVU1-4 was potentially a protective factor for stomach adenocarcinoma (STAD) survival. This consistent finding of differential expression of all three snRNAs in all four types of digestive system cancers suggests potential roles for these snRNAs in the tumorigenesis of digestive system cancers. RNU6-101 P could play a pivotal role in the progression of ESAD and RNVU1-4 could perform a protective role in STAD. However, since the current findings were based on RNA-sequencing data mining, more studies are needed for verification.

Integrated analysis of long noncoding RNA associated-competing endogenous RNA as prognostic biomarkers in clear cell renal carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant carcinomas and its molecular mechanisms remain unclear. Long noncoding RNA (lncRNA) could bind sites of miRNA which affect the expression of mRNA according to the competing endogenous (ceRNA) theory. The aim of the present study was to construct a ceRNA network and to identify key lncRNA to predict survival prognosis. We identified differentially expressed mRNA, lncRNA and miRNA between tumor tissues and normal tissues from The Cancer Genome Atlas database. Then, using bioinformatics tools, we explored the connection of 89 lncRNA, 10 miRNA and 22 mRNA, and we constructed the ceRNA network. Furthermore, we analyzed the functions and pathways of 22 differentially expressed mRNA. Then, univariate and multivariate Cox regression analyses of these 89 lncRNA and overall survival were explored. Nine lncRNA were finally screened out in the training group. The patients were divided into high‐risk and low‐risk groups according to the 9 lncRNA and low‐risk scores having better clinical overall survival (P < .01). Furthermore, the receiver operating characteristic curve demonstrates the predicted role of the 9 lncRNA. The 9‐lncRNA signature was successfully proved in the testing group and the entire group. Finally, multivariate Cox regression analysis and stratification analysis further proved that the 9‐lncRNA signature was an independent factor to predict survival. In summary, the present study provides a deeper understanding of the lncRNA‐related ceRNA network in ccRCC and suggests that the 9‐lncRNA signature could serve as an independent biomarker to predict survival in ccRCC patients.

Systematic analysis of lncRNA-miRNA-mRNA competing endogenous RNA network identifies four-lncRNA signature as a prognostic biomarker for breast cancer

Background

Increasing evidence has underscored the role of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in the development and progression of tumors. Nevertheless, lncRNA biomarkers in lncRNA-related ceRNA network that can predict the prognosis of breast cancer (BC) are still lacking. The aim of our study was to identify potential lncRNA signatures capable of predicting overall survival (OS) of BC patients.

Methods

The RNA sequencing data and clinical characteristics of BC patients were obtained from the Cancer Genome Atlas database, and differentially expressed lncRNA (DElncRNAs), DEmRNAs, and DEmiRNAs were then identified between BC and normal breast tissue samples. Subsequently, the lncRNA–miRNA–mRNA ceRNA network of BC was established, and the gene oncology enrichment analyses for the DEmRNAs interacting with lncRNAs in the ceRNA network was implemented. Using univariate and multivariate Cox regression analyses, a four-lncRNA signature was developed and used for predicting the survival in BC patients. We applied receiver operating characteristic analysis to assess the performance of our model.

Results

A total of 1061 DElncRNAs, 2150 DEmRNAs, and 82 DEmiRNAs were identified between BC and normal breast tissue samples. A lncRNA–miRNA–mRNA ceRNA network of BC was established, which comprised of 8 DEmiRNAs, 48 DElncRNAs, and 10 DEmRNAs. Further gene oncology enrichment analyses revealed that the DEmRNAs interacting with lncRNAs in the ceRNA network participated in cell leading edge, protease binding, alpha-catenin binding, gamma-catenin binding, and adenylate cyclase binding. A univariate regression analysis of the DElncRNAs revealed 7 lncRNAs (ADAMTS9-AS1, AC061992.1, LINC00536, HOTAIR, AL391421.1, TLR8-AS1 and LINC00491) that were associated with OS of BC patients. A multivariate Cox regression analysis demonstrated that 4 of those lncRNAs (ADAMTS9-AS1, LINC00536, AL391421.1 and LINC00491) had significant prognostic value, and their cumulative risk score indicated that this 4-lncRNA signature independently predicted OS in BC patients. Furthermore, the area under the curve of the 4-lncRNA signature associated with 3-year survival was 0.696.

Conclusions

The current study provides novel insights into the lncRNA-related ceRNA network in BC and the 4 lncRNA biomarkers may be independent prognostic signatures in predicting the survival of BC patients.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1640-2) contains supplementary material, which is available to authorized users.

Comprehensive analysis of novel three-long noncoding RNA signatures as a diagnostic and prognostic biomarkers of human triple-negative breast cancer

Currently, traditional predictors of prognosis (tumor size, nodal status, progesterone receptor [PR], estrogen receptor [ER], or human epidermal growth factor receptor-2 [HER2]) are insufficient for precise survival prediction for triple-negative breast cancer (TNBC). Long noncoding RNAs (lncRNAs) have been observed to exert critical functions in cancer, including in TNBC. Nevertheless, systematically tracking expression-based lncRNA biomarkers based on the sequence data for the prediction of prognosis in TNBC has not yet been investigated. To ascertain whether biomarkers exist that can distinguish TNBC from adjacent normal tissue or nTNBC, we implemented a comprehensive analysis of lncRNA expression profiles and clinical data of 1097 BC samples from The Cancer Genome Atlas database. A total of 1510 differentially expressed lncRNAs in normal and TNBC samples were extracted. Similarly, 672 differentially expressed lncRNAs between nTNBC and TNBC samples were detected. The receiver operating characteristic curve analysis indicated that three upregulated lncRNAs (AC091043.1, AP000924.1, and FOXCUT) may be of strong diagnostic value for predicting the existence of TNBC in the training and validation sets (area under the curve (AUC > 0.85). Kaplan-Meier analysis demonstrated that the other three lncRNAs (AC010343.3, AL354793.1, and FGF10-AS1) were associated with the prognosis of TNBC patients (P < 0.05). We used the three overall survival (OS)-related lncRNAs to establish a three-lncRNA signature. Multivariate Cox regression analysis suggested that the three-lncRNA signature was a prognostic factor independent of other clinical variables ( P < 0.01) for predicting OS in TNBC patients that could be utilized to classify patients into high- or low-risk subgroups. Our results might provide efficient signatures for clinical diagnosis and prognostic evaluation of TNBC.

Screening key long non-coding RNAs in early-stage colon adenocarcinoma by RNA-sequencing

AIM

We aim to identify the key long noncoding RNAs (lncRNAs) in early-stage colon adenocarcinoma (COAD).

PATIENTS & METHODS

Compared with colonic intraepithelial neoplasia, differentially expressed lncRNAs (DElncRNAs) in early-stage COAD were obtained by RNA-sequencing. Our previous work has obtained the differentially expressed mRNAs and miRNAs (DEmRNAs and DEmiRNAs) in early-stage COAD. DEmiRNA-DElncRNA-DEmRNA interaction analysis and functional annotation were performed. Validation of expression and receiver-operating characteristic analyses were performed based on The Cancer Genome Atlas.

RESULTS

Seventy-nine significantly DElncRNAs in early-stage COAD were obtained. MiR-153-3p-TUG1-DAPK1/ARNT2/KLK3/PLD1/SMAD2 and miR-153-3p-SNHG17-COL11A1/IGFBP3/KLF6 interactions were associated with early-stage COAD. Five DElncRNAs (ELFN1-AS1, LINC01234, SNHG17, UCA1 and LOC101929549) involved in early-stage COAD with potential diagnostic value.

CONCLUSION

LncRNAs involve in early-stage COAD by interaction with COAD-regulated genes and miRNAs.

Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma

AIM

To construct a long non-coding RNA (lncRNA) signature for predicting hepatocellular carcinoma (HCC) prognosis with high efficiency.

METHODS

Differentially expressed lncRNAs (DELs) between HCC specimens and peritumor liver specimens were identified using the edgeR package to analyze The Cancer Genome Atlas (TCGA) LIHC dataset. Univariate Cox proportional hazards regression was performed to obtain the DELs significantly associated with overall survival (OS) in a training set. These OS-related DELs were further analyzed using a stepwise multivariate Cox regression model. Those lncRNAs fitted in the multivariate Cox regression model and independently associated with overall survival were chosen to build a prognostic risk formula. The prognostic value of this formula was then validated in the test group and the entire cohort and further compared with two previously identified prognostic signatures for HCC. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed to explore the potential biological functions of the lncRNAs in the signature.

RESULTS

Based on lncRNA expression profiling of 370 HCC patients from the TCGA database, we constructed a 5-lncRNA signature (AC015908.3, AC091057.3, TMCC1-AS1, DCST1-AS1 and FOXD2-AS1) that was significantly associated with prognosis. HCC patients with high-risk scores based on the expression of the 5 lncRNAs had significantly shorter survival times compared to patients with low-risk scores in both the training and test groups. Multivariate Cox regression analysis demonstrated that the prognostic value of the 5 lncRNAs was independent of clinicopathological parameters. A comparison study involving two previously identified prognostic signatures for HCC demonstrated that this 5-lncRNA signature showed improved prognostic power compared with the other two signatures. Functional enrichment analysis indicated that the 5 lncRNAs were potentially involved in metabolic processes, fibrinolysis and complement activation.

CONCLUSION

Our present study constructed a 5-lncRNA signature that improves survival prediction and can be used as a prognostic biomarker for HCC patients.

LncRNA DLEU1 contributes to colorectal cancer progression via activation of KPNA3

Background

Accumulating evidences show that long noncoding RNAs (lncRNA) play essential roles in the development and progression of various malignancies. However, their functions remains poorly understood and many lncRNAs have not been defined in colorectal cancer (CRC). In this study, we investigated the role of DLEU1 in CRC.

Methods

Quantitative real-time PCR was used to detect the expression of DLEU1 and survival analysis was adopted to explore the association between DLEU1 expression and the prognosis of CRC patients. CRC cells were stably transfected with lentivirus approach and cell proliferation, migration, invasion and cell apoptosis, as well as tumorigenesis in nude mice were performed to assess the effects of DLEU1 in BCa. Biotin-coupled probe pull down assay, RNA immunoprecipitation and Fluorescence in situ hybridization assays were conducted to confirm the relationship between DLEU1 and SMARCA1.

Results

Here we revealed that DLEU1 was crucial for activation of KPNA3 by recruiting SMARCA1, an essential subunit of the NURF chromatin remodeling complex, in CRC. DLEU1 was indispensible for the deposition of SMARCA1 at the promoter of KPNA3 gene. Increased expression of DLEU1 and KPNA3 was observed in human CRC tissues. And higher expression of DLEU1 or KPNA3 in patients indicates lower survival rate and poorer prognosis. DLEU1 knockdown remarkably inhibited CRC cell proliferation, migration and invasion in vitro and in vivo while overexpressing KPNA3 in the meantime reversed it.

Conclusions

Our results identify DLEU1 as a key regulator by a novel DLEU1/SMARCA1/KPNA3 axis in CRC development and progression, which may provide a potential biomarker and therapeutic target for the management of CRC.

Electronic supplementary material

The online version of this article (10.1186/s12943-018-0873-2) contains supplementary material, which is available to authorized users.

MicroRNAs as biomarkers for the progression and prognosis of colon carcinoma

Early detection is critical for the treatment of colon carcinoma. However, current biomarkers for its diagnosis and prognosis are insufficient and improvement is required. Aberrantly expressed microRNAs (miRNAs/miRs) in colon carcinoma have been identified to function as potential diagnostic and prognostic biomarkers. In the present study, 245 differentially expressed miRNAs between colon carcinoma and normal tissues were identified by a bioinformatics analysis of a dataset from The Cancer Genome Atlas. A six-miRNA (miR-149, miR-3189, miR-3677, miR-3917, miR-4999 and miR-6854) prognostic prediction system was established, which is able to independently and effectively predict the prognosis of colon carcinoma patients [P<0.001, area under the receiver operating characteristic curve (AUC)=0.763]. Furthermore, the six miRNAs were highly correlated with the tumor-nodes-metastasis (TNM) stage and were able to distinguish between different stages (high vs. low TNM stage, P<0.001). Of note, combination of the six-miRNA signature and TNM stage provides an improved prediction of the patient's prognosis (AUC=0.797). Functional enrichment analysis revealed the possible mechanistic involvement of these predictive miRNAs in cancer-associated biological processes and pathways. Taken together, the present study demonstrated the promising potential of the novel six-miRNA model as an independent factor for the prediction of the progression and prognosis of colon carcinoma.

Long noncoding RNA TMEM75 promotes colorectal cancer progression by activation of SIM2

Colorectal cancer (CRC) is one of the most frequent cancers worldwide and leads to a great many deaths each year. However, the underlying mechanisms that regulate colorectal cancer development and progression remain elusive. Here we identified an uncharacteristic long noncoding RNA TMEM75 that was upregulated in colorectal cancer compared to non-tumor tissues. We found that TMEM75 expression levels are positively correlated with advanced clinical stage. TMEM75 significantly inhibited the proliferation, migration, and invasion of CaCo2 and HCT116 cells in vitro. Moreover, TMEM75 silence delayed tumor growth in vivo. Mechanistically, TMEM75 was demonstrated to initiate the expression of transcription factor SIM2. We also found that the expression of SIM2 was upregulated and positively correlated that of TMEM75 in colorectal cancer tissues. Furthermore, ectopic expression of SIM2 rescued TMEM75 depletion-induced inhibition on cell proliferation, migration and invasion in colorectal cancer. Altogether, our findings indicated that TMEM75 functions as an oncogene relying on activation of SIM2 in colorectal cancer for the first time.

The Correlation Between the Immune and Epithelial-Mesenchymal Transition Signatures Suggests Potential Therapeutic Targets and Prognosis Prediction Approaches in Kidney Cancer

Both epithelial-mesenchymal transition (EMT) and immune regulation are important biological process in malignant tumours. The current research aims to comprehensively explore the potential association between the epithelial-mesenchymal transition (EMT) signature and immune checkpoint signature and its role in predicting the prognosis of clear-cell renal cell carcinoma (ccRCC) patients. EMT-related genes were collected from an experiment-based study and then were investigated using data from the Cancer Genome Atlas. A total of 357 genes were included, and 23 of them that were upregulated and correlated with prognosis were analysed further as core EMT genes in ccRCC. Interestingly, the emerging immune checkpoints CD276, OX40 and TGFB1 were found to be significantly co-expressed with core EMT genes, and TGFB1, CXCR4, IL10, and IL6 were the most important molecules potentially interacting with EMT molecules in our model, as determined from mRNA co-expression and protein-protein interaction network analysis. Additionally, an integrated scoring model based on FOXM1, TIMP1 and IL6 was successfully established to distinguish ccRCC patients with different clinical risks. Our results identified core genes in the EMT-immunophenotyping correlation and evaluated their risk assessment capabilities, providing more potential therapeutic targets and prediction approaches regarding the translational research of treatment and prognosis in ccRCC.

MicroRNA-671-3p inhibits the development of breast cancer: A study based on in vitro experiments, in-house quantitative polymerase chain reaction and bioinformatics analysis

MicroRNAs (miRNAs or miRs) are highly conserved small noncoding RNA molecules involved in gene regulation. An increasing number of studies have demonstrated that miRNAs act as oncogenes or antioncogenes in various types of cancer, including breast cancer (BC). However, the exact role of miR‑671‑3p in BC has not yet been reported. In the present study, in vitro experiments were implemented to explore the effects of miR‑671‑3p on the proliferation and apoptosis of BC cells, and reverse transcription‑quantitative polymerase chain reaction was conducted using in‑house clinical BC samples to address the expression level and clinical value of miR‑671‑3p in BC. Simultaneously, miR‑671‑3p target genes were collected, and subsequent bioinformatics analyses were executed to probe the potential signaling pathway through which miR‑671‑3p influenced the occurrence and progression of BC. According to the results, the expression level of miR‑671‑3p was lower in BC tissues compared with that in adjacent non‑tumorous tissues (P=0.048), and the area under the curve was 0.697 (95% confidence interval=0.538‑0.856), with a sensitivity and specificity of 0.818 and 0.579, respectively. Forced miR‑671‑3p expression in the BC cell line MDA‑MB‑231 evidently arrested cell proliferation and induced cell apoptosis. Furthermore, in silico enrichment analyses suggested that miR‑671‑3p may be involved in the initiation and progression of BC through the targeting of genes associated with the Wnt signaling pathway. In conclusion, the present study findings suggested that miR‑671‑3p may function as a tumor suppressor in BC by influencing the Wnt signaling cascade, which provides a prospective molecular target for the therapy of BC.

Long non-coding RNAs RP5-821D11.7, APCDD1L-AS1 and RP11-277P12.9 were associated with the prognosis of lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC), a type of non-small cell lung carcinoma, has a poor therapeutic response, high relapse rate and poor prognosis. The present study was designed to reveal the key long non-coding RNAs (lncRNAs) associated with the prognosis of LUSC. The lncRNA expression profiles of LUSC and adjacent samples were downloaded from The Cancer Genome Atlas database. Based on the edgeR and DEseq packages, the differentially expressed lncRNAs (DELs) between LUSC and adjacent samples were obtained and the intersecting DELs were regarded as significant DELs. Subsequently, a prognostic risk model was established using Cox regression analysis and its classification effect was detected by survival analysis. Using survival analysis, the effect of the prognostic risk model was assessed in the validation set and other types of cancer. Finally, the co-expression genes of key lncRNAs were screened using the Multi-Experiment Matrix tool and the STRING database, and their functions were predicted via enrichment analysis using the Database for Annotation, Visualization and Integrated Discovery tool. A total of 2,041 significant DELs between LUSC and adjacent samples were screened. The prognostic risk model consisting of RP5-821D11.7, APCDD1L-AS1 and RP11-277P12.9 was established, which had a good classification effect. Cox multivariate regression analysis demonstrated that risk score may serve as an independent prognostic factor. Furthermore, certain co-expression genes of RP5-821D11.7 (including proliferating cell nuclear antigen), APCDD1L-AS1 (including semaphorin 5A, semaphorin 6D, ADAMTS like 1, ADAM metallopeptidase with thrombospondin type 1 motif 6, slit guidance ligand 3, and tenascin C) and RP11-277P12.9 (including Wnt family member 2B) were identified. Additionally, ‘positive regulation of cell migration’ and ‘proteinaceous extracellular matrix’ were enriched. In conclusion, the expression levels of the lncRNAs RP5-821D11.7, APCDD1L-AS1 and RP11-277P12.9 may affect the prognosis of LUSC.

Clinical value and potential pathways of miR-183-5p in bladder cancer: A study based on miRNA-seq data and bioinformatics analysis

The clinicopathological value and exploration of the potential molecular mechanism of microRNA-183-5p (miR-183-5p) have been investigated in various cancers; however, to the best of the author's knowledge, no similar research has been reported for bladder cancer. In the present study, it was revealed that the expression level of miR-183-5p was notably increased in bladder cancer tissues compared with adjacent non-cancerous tissues (P=0.001) and was markedly increased in the tissue samples of papillary, pathological T stage (T0-T2) and pathological stage (I-II) compared with tissue samples of their counterparts (P=0.05), according to data from The Cancer Genome Atlas. Receiver operating characteristic analysis revealed the robust diagnostic value of miR-183-5p for distinguishing bladder cancer from non-cancerous bladder tissues (area under curve=0.948; 95% confidence interval: 0.919-0.977). Amplification and deep deletion of miR-183-5p were indicated by cBioPortal, accounting for 1% (4/412) of bladder cancer cases. Data from YM500v3 demonstrated that compared with other cancers, bladder cancer exhibited high expression levels of miR-183-5p, and miR-183-5p expression in primary solid tumors was much higher compared with solid normal tissues. A meta-analysis indicated that miR-183-5p was more highly expressed in bladder cancer samples compared with normal counterparts. A total of 88 potential target genes of miR-183-5p were identified, 13 of which were discerned as hub genes by protein-protein interaction. The epithelial-to-mesenchymal transition pathway was the most significantly enriched pathway by FunRich (P=0.0001). In summary, miR-183-5p may participate in the tumorigenesis and development of bladder cancer via certain signaling pathways, particularly the epithelial-to-mesenchymal transition pathway. However, the exact molecular mechanism of miR-183-5p in bladder cancer must be validated by in vitro and in vivo experiments.

Survival prediction of kidney renal papillary cell carcinoma by comprehensive LncRNA characterization

Kidney renal papillary cell carcinoma (KIRP) accounts for 10%-15% of renal cell carcinoma (RCC), patients with KIRP tend to have a poor prognosis, and there was a lack of effective prognostic indicators for this type of cancer. Currently, owing to the availability of The Cancer Genome Atlas (TCGA), long non-coding RNAs (LncRNAs) have been discovered to indicate a prognostic value in some tumors. In that regard, we analyzed lncRNA-sequencing data of KIRP in TCGA, and among 780 differentially-expressed lncRNAs, we selected 37 lncRNAs which were able to assist the prognosis. In addition, by using the multivariate cox regression analysis, the prognosis index (PI) that consisted of 7 lncRNAs (including AFAP1-AS1, GAS6-AS1, RP11-1C8.7, RP11-21L19.1, RP11-503C24.1, RP11-536I6.2, and RP11-63A11.1) could predict the progression and outcomes of KIRP with accuracy. More importantly, the PI was considered an independent indicator for prognostication of KIRP. Moreover, having categorized patients with KIRP into cohorts of high risk and low risk, according to the PI, we found that the key genes and pathways varied in these two groups. Overall, these LncRNAs, especially the PI, may be conceived as biomarkers and helpful for determining the different pathological stages for KIRP patients. However, their biological functions need to be further confirmed.

Diagnosis, prognosis and bioinformatics analysis of lncRNAs in hepatocellular carcinoma

This study aims to comprehensively analyze the diagnosis and prognosis of lncRNAs in hepatocellular carcinoma (HCC). From the Gene Expression Omnibus database, we screened out and analyzed the differently expressed lncRNAs and related mRNAs using bioinformatics methods. The expressions of lncRNAs were validated in tumor tissues, cell lines and the Cancer Genome Atlas database. At the same time, we also conducted an exploratory analysis on the diagnostic and prognostic ability of lncRNAs in HCC. In this study, we found that most of the targeted mRNAs promote the biological process of cell division, cellular component of nucleoplasm, molecular function of protein binding, which were significantly associated with 12 KEGG pathways. LncRNA CRNDE and LINC01419 also had significant diagnostic value in HCC. In particular, the sensitivity, specificity and area under the curve of CRNDE were 71.0%, 87.1% and 0.701 (95% CI: 0.543-0.860), respectively. In addition, the high expression of CRNDE and GBAP1 predicated poor prognosis, while the high expression of LINC01093 suggested the opposite outcome. Through the comprehensive analysis of lncRNAs, it provided an important reference for the early diagnosis, prognosis evaluation, pathogenesis and targeted therapy of HCC.

Long non-coding RNA LINC00959 predicts colorectal cancer patient prognosis and inhibits tumor progression

Long non-coding RNAs (lncRNAs) are increasingly implicated in tumorigenesis and cancer progression. This study focused on the relationship between the lncRNA LINC00959 and colorectal cancer (CRC). We found that LINC00959 expression was lower in CRC tissues than normal colorectal mucosae. High LINC00959 expression was negatively associated with TNM stage, distant metastasis, and lymphatic metastasis, and correlated with a better prognosis in 87 CRC cases. In vitro, LINC00959 knockdown enhanced colon cancer cell proliferation, invasion, and migration; upregulated N-cadherin and vimentin; and downregulated E-cadherin and Caspase-3. LINC00959 overexpression produced the opposite effects. These data suggest that LINC00959 inhibits tumor cell invasion and migration by suppressing epithelial-mesenchymal transition and promotes apoptosis through Caspase-3. LINC00959 may be a tumor suppressor and useful prognostic biomarker in CRC.

Expression profiles analysis of long non-coding RNAs identified novel lncRNA biomarkers with predictive value in outcome of cutaneous melanoma

Recent advancements in cancer biology have identified a large number of lncRNAs that are dysregulated expression in the development and tumorigenesis of cancers, highlighting the importance of lncRNAs as a key player for human cancers. However, the prognostic value of lncRNAs still remains unclear and needs to be further investigated. In the present study, we aim to assess the prognostic value of lncRNAs in cutaneous melanoma by integrated lncRNA expression profiles from TCGA database and matched clinical information from a large cohort of patients with cutaneous melanoma. We finally identified a set of six lncRNAs that are significantly associated with survival of patients with cutaneous melanoma. A linear combination of six lncRNAs (LINC01260, HCP5, PIGBOS1, RP11-247L20.4, CTA-292E10.6 and CTB-113P19.5) was constructed as a six-lncRNA signature which classified patients of training cohort into the high-risk group and low-risk group with significantly different survival time. The prognostic value of the six-lncRNA signature was validated in both the validation cohort and entire TCGA cohort. Moreover, the six-lncRNA signature is independent of known clinic-pathological factors by multivariate Cox regression analysis and demonstrated good performance for predicting three- and five-year overall survival by time-dependent receiver operating characteristic (ROC) analysis. Our study provides novel insights into the molecular heterogeneity of cutaneous melanoma and also shows potentially important implications of lncRNAs for prognosis and therapy for cutaneous melanoma.

Comprehensive analysis of a novel four-lncRNA signature as a prognostic biomarker for human gastric cancer

Emerging evidence indicates that long non-coding RNAs (lncRNAs) play a crucial role in predicting survival for gastric cancer (GC) patients. This study aims to identify a lncRNA-related signature for evaluating the overall survival of 379 GC patients from The Cancer Genome Atlas (TCGA) database. The associations between survival outcome and the expression of lncRNAs were evaluated by the univariate and multivariate Cox proportional hazards regression analyses. Four lncRNAs (LINC01018, LOC553137, MIR4435-2HG, and TTTY14) were identified as significantly correlated with overall survival. These four lncRNAs were gathered as a single prognostic signature. There was a significant positive correlation between GC patients with low-risk scores and overall survival (P = 0.001). Further analysis suggested that the prognostic value of this four-lncRNA signature was independent in clinical features. Gene set enrichment analysis found that these four lncRNAs were correlated with several molecular pathways of the tumor. Our study indicates that this novel lncRNA expression signature may be a useful biomarker of the prognosis for GC patients, based on bioinformatics analysis.

Integrated analysis of competing endogenous RNA network revealing lncRNAs as potential prognostic biomarkers in human lung squamous cell carcinoma

Accumulating evidence shows the important role of long non-coding RNAs (lncRNAs) in competing endogenous RNA (ceRNA) networks for predicting survival in tumor patients. However, prognostic biomarkers for lung squamous cell carcinoma (LUSC) are still lacking. The objective of this study is to identify a lncRNA signature for evaluation of overall survival (OS) in 474 LUSC patients from The Cancer Genome Atlas (TCGA) database. A total of 474 RNA sequencing profiles in LUSC patients with clinical data were obtained, providing a large sample of RNA sequencing data, and 83 LUSC-specific lncRNAs, 26 miRNAs, and 85 mRNAs were identified to construct the ceRNA network (fold change>2, P<0.05). Among these above 83 LUSC-specific lncRNAs, 22 were assessed as closely related to OS in LUSC patients using a univariate Cox proportional regression model. Meanwhile, two (FMO6P and PRR26) of the above 22 OS-related lncRNAs were identified using a multivariate Cox regression model to construct a risk score as an independent indicator of the prognostic value of the lncRNA signature in LUSC patients. LUSC patients with low-risk scores were more positively correlated with OS (P<0.001). The present study provides a deeper understanding of the lncRNA-related ceRNA network in LUSC and suggests that the two-lncRNA signature could serve as an independent biomarker for prognosis of LUSC.

Identification of four prognostic LncRNAs for survival prediction of patients with hepatocellular carcinoma

Background

As the fifth most common cancer worldwide, Hepatocellular carcinoma (HCC) is also the third most common cause of cancer-related death in China. Several lncRNAs have been demonstrated to be associated with occurrence and prognosis of HCC. However, identification of prognostic lncRNA signature for HCC with expression profiling data has not been conducted yet.

Methods

With the reuse of public available TCGA data, expression profiles of lncRNA for 371 patients with HCC were obtained and analyzed to find the independent prognostic lncRNA. Based on the expression of lncRNA, we developed a risk score model, which was evaluated by survival analysis and ROC (receiver operating characteristic) curve. Enrichment analysis was performed to predict the possible role of the identified lncRNA in HCC prognosis.

Results

Four lncRNAs (RP11-322E11.5, RP11-150O12.3, AC093609.1, CTC-297N7.9) were found to be significantly and independently associated with survival of HCC patients. We used these four lncRNAs to construct a risk score model, which exhibited a strong ability to distinguish patients with significantly different prognosis (HR = 2.718, 95% CI [2.103–3.514], p = 2.32e−14). Similar results were observed in the subsequent stratification survival analysis for HBV infection status and pathological stage. The ROC curve also implied our risk score as a good indicator for 5-year survival prediction. Furthermore, enrichment analysis revealed that the four signature lncRNAs may be involved in multiple pathways related to tumorigenesis and prognosis.

Discussion

Our study recognized four lncRNAs to be significantly associated with prognosis of liver cancer, and could provide novel insights into the potential mechanisms of HCC progression. Additionally, CTC-297N7.9 may influence the downstream TMEM220 gene expression through cis-regualtion. Nevertheless, further well-designed experimental studies are needed to validate our findings.

Clinical Significance and Effect of lncRNA HOXA11-AS in NSCLC: A Study Based on Bioinformatics, In Vitro and in Vivo Verification

HOXA11 antisense RNA (HOXA11-AS) has been shown to be involved in tumorigenesis and development of different cancers. However, the role of HOXA11-AS in non-small cell lung cancer (NSCLC) remains unclear. In this study, we firstly explored and confirmed the expression of HOXA11-AS in NSCLC tissues and cells. Cytometry, CCK-8, cell scratch, migration, Matrigel invasion and flow cytometry assays were performed to determine the biological impact of HOXA11-AS in vitro. Furthermore, a chick embryo chorioallantoic membrane (CAM) model of NSCLC was constructed to explore the effect of HOXA11-AS on tumorigenicity and angiogenesis in vivo. Additionally, bioinformatics analyses were performed to investigate the prospective pathways of HOXA11-AS co-expressed genes. As results, HOXA11-AS was markedly highly expressed in NSCLC tissues and cells. Furthermore, the proliferation, migration, invasion, tumorigenic and angiogenic ability of NSCLC cells were all inhibited and apoptosis was induced after HOXA11-AS knock-down. HOXA11-AS RNAi also led to cell cycle arrest on G0/G1 or G2/M phase. In addition, the non-small cell lung cancer pathway might be involved in regulating the co-expressed genes of HOXA11-AS in NSCLC. These results indicate that HOXA11-AS plays pivotal roles in NSCLC and it can become a novel therapeutic direction for treating NSCLC.

Identification of potential long non-coding RNA biomarkers associated with the progression of colon cancer

Increasing evidence has suggested that dysregulated lncRNA expression played important roles in the development and progression of human cancers. Although prognostic roles of lncRNAs have been recognized for colon cancer (CC) patients, the search for novel lncRNA biomarkers potentially involved in CC progression is an urgent and still largely unmet medical need. In this study, we evaluated the lncRNA expression changes during the progression of CC by analyzing two cohorts of previously published expression profiles of CC patients and identified hundreds of differentially expressed lncRNAs. Then we identified eight lncRNAs that closely associated with the progression of CC patients from a large number of significantly altered lncRNAs using random forest supervised classification algorithm. Finally, an SVM-based lncRNA risk classifier was developed to discriminate high-risk CC patients from persons with early-stage and validated in both the training dataset and testing dataset by survival analysis and five-fold cross-validation strategy. Our pathway enrichment analysis based on protein-coding genes that are co-expressed with lncRNAs, suggested that variation in expression of eight lncRNAs biomarkers might affect critical pathways involved in CC progression. With further validation, these eight lncRNAs might have significant implications for the clinical management of CC patients with early stage and improve our understanding of cancer progression.