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.     

A novel seven-lncRNA signature for prognosis prediction in hepatocellular carcinoma

Liver cancer is still one of the leading causes of cancer-related death worldwide. This study is dedicated to developing a multi–long noncoding RNA (lncRNA) model for risk stratification and prognosis prediction on patients with hepatocellular carcinoma (HCC). We first downloaded lncRNA expression profiles and corresponding clinical information of patients with liver cancer from The Cancer Genome Atlas database. Differentially expressed (DE) lncRNAs between HCC samples and normal samples were identified. In total, 308 patients with HCC were randomly divided into a training group (n = 154) and a testing group (n = 154). Univariate Cox regression and least absolute shrinkage and selection operator Cox regression analyses were performed to select the best survival-related candidates from these DE lncRNAs in the training set. Seven lncRNAs (AC009005.2, RP11-363N22.3, RP11-932O9.10, RP11-572O6.1, RP11-190C22.8, RP11-388C12.8, and ZFPM2-AS1) were finally identified and used to construct a seven-lncRNA signature. The signature could classify patients into high-risk and low-risk groups with significantly different overall survival. The area under the curve of receiver operating characteristic curve for the signature to predict 5-year survival reached more than 0.75. Besides, the prognostic value of the seven-lncRNA signature was independent of conventional clinical factors. The predictive performance of the signature was further validated in the testing set and the whole set. Functional enrichment analysis indicated that the seven prognostic lncRNAs may be involved in several essential biological processes and pathways. The current study demonstrated the potential clinical implications of the seven-lncRNA signature for survival prediction of patients with HCC.     

An Immune-Related Six-lncRNA Signature to Improve Prognosis Prediction of Glioblastoma Multiforme

Recent studies have demonstrated the utility and superiority of long non-coding RNAs (lncRNAs) as novel biomarkers for cancer diagnosis, prognosis, and therapy. In the present study, the prognostic value of lncRNAs in glioblastoma multiforme was systematically investigated by performing a genome-wide analysis of lncRNA expression profiles in 419 glioblastoma patients from The Cancer Genome Atlas (TCGA) project. Using survival analysis and Cox regression model, we identified a set of six lncRNAs (AC005013.5, UBE2R2-AS1, ENTPD1-AS1, RP11-89C21.2, AC073115.6, and XLOC_004803) demonstrating an ability to stratify patients into high- and low-risk groups with significantly different survival (median 0.899 vs. 1.611 years, p = 3.87e?09, log-rank test) in the training cohort. The six-lncRNA signature was successfully validated on independent test cohort of 219 patients with glioblastoma, and it revealed superior performance for risk stratification with respect to existing lncRNA-related signatures. Multivariate Cox and stratification analysis indicated that the six-lncRNA signature was an independent prognostic factor after adjusting for other clinical covariates. Further in silico functional analysis suggested that the six-lncRNA signature may be involved in the immune-related biological processes and pathways which are very well known in the context of glioblastoma tumorigenesis. The identified lncRNA signature had important clinical implication for improving outcome prediction and guiding the tailored therapy for glioblastoma patients with further prospective validation.     

Immune-related long noncoding RNA signature for predicting survival and immune checkpoint blockade in hepatocellular carcinoma

Long noncoding RNAs (lncRNAs) show multiple functions, including immune response. Recently, the immune-related lncRNAs have been reported in some cancers. We first investigated the immune-related lncRNA signature as a potential target in hepatocellular carcinoma (HCC) survival. The training set (n = 368) and the independent external validation cohort (n = 115) were used. Immune genes and lncRNAs coexpression were constructed to identify immune-related lncRNAs. Cox regression analyses were perfumed to establish the immune-related lncRNA signature. Regulatory roles of this signature on cancer pathways and the immunologic features were investigated. The correlation between immune checkpoint inhibitors and this signature was examined. In this study, the immune-related lncRNA signature was identified in HCC, which could stratify patients into high- and low-risk groups. This immune-related lncRNA signature was correlated with disease progression and worse survival and was an independent prognostic biomarker. Our immune-related lncRNA signature was still a powerful tool in predicting survival in each stratum of age, gender, and tumor stage. This signature mediated cell cycle, glycolysis, DNA repair, mammalian target of rapamycin signaling, and immunologic characteristics (i.e., natural killer cells vs. Th1 cells down, etc). This signature was associated with immune cell infiltration (i.e., macrophages M0, Tregs, CD4 memory T cells, and macrophages M1, etc.,) and immune checkpoint blockade (ICB) immunotherapy-related molecules (i.e., PD-L1, PD-L2, and IDO1). Our findings suggested that the immune-related lncRNA signature had an important value for survival prediction and may have the potential to measure the response to ICB immunotherapy. This signature may guide the selection of the immunotherapy for HCC.     

Prediction of head and neck squamous cell carcinoma survival based on the expression of 15 lncRNAs

Recent evidence suggests that long noncoding RNAs (lncRNAs) are essential regulators of many cancer-related processes, including cancer cell proliferation, invasion, and migration. There is thus a reason to believe that the detection of lncRNAs may be useful as a diagnostic and prognostic strategy for cancer detection, however, at present no effective genome-wide tests are available for clinical use, constraining the use of such a strategy. In this study, we performed a comprehensive assessment of lncRNAs expressed in samples in the head and neck squamous cell carcinoma (HNSCC) cohort available in The Cancer Genome Atlas database. A risk score (RS) model was constructed based on the expression data of these 15 lncRNAs in the validation data set of HNSCC patients and was subsequently validated in validation data set and the entire data set. We were able to stratify patients into high- and low-risk categories, using our lncRNA expression panel to determine an RS, with significant differences in overall survival (OS) between these two groups in our test set (median survival, 1.863 vs. 5.484 years; log-rank test, p < 0.001). We were able to confirm the predictive value of our 15-lncRNA signature using both a validation data set and a full data set, finding our signature to be reproducible and effective as a means of predicting HNSCC patient OS. Through the multivariate Cox regression and stratified analyses, we were further able to confirm that the predictive value of this RS was independent of other predictive factors such as clinicopathological parameters. The Gene set enrichment analysis revealed potential functional roles for these 15 lncRNAs in tumor progression. Our findings indicate that an RS established based on a panel of lncRNA expression signatures can effectively predict OS and facilitate patient stratification in HNSCC.     

An eight-long noncoding RNA expression signature for colorectal cancer patients’ prognosis

Long noncoding RNAs (lncRNAs) have recently emerged as important biomarkers of cancer progression. Here, we proposed to develop a lncRNA-based signature with a prognostic value for colorectal cancer (CRC) overall survival (OS). Through mining microarray datasets, we analyzed the lncRNA expression profiles of 122 patients with CRC from Gene Expression Omnibus. Associations between lncRNA and CRC OS were firstly evaluated through univariate Cox regression analysis. A random survival forest method was applied for further screening of the lncRNA signature, which resulted in eight lncRNAs, including PEG3-AS1, LOC100505715, MINCR, DBH-AS1, LINC00664, FAM224A, LOC642852, and LINC00662. Combination of the eight lncRNAs weighted by their multivariate Cox regression coefficients formed a prognostic signature, through which, we could divide the 122 patients with CRC into two subgroups with significantly different OS. Good robustness of the lncRNA signature's prognostic value was verified through an independent data set consisting of 55 patients with CRC. In addition, gene set enrichment analysis indicated the potential association between high prognostic value and oxygen metabolism-related processes. This result should indicate that lncRNAs could be a useful signature for CRC prognosis.     

Identification of autophagy-related long non-coding RNA prognostic signature for breast cancer

Autophagy-related long non-coding RNAs (lncRNAs) disorders are related to the occurrence and development of breast cancer. The purpose of this study is to explore whether autophagy-related lncRNA can predict the prognosis of breast cancer patients. The autophagy-related lncRNAs prognostic signature was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression. We identified five autophagy-related lncRNAs (MAPT-AS1, LINC01871, AL122010.1, AC090912.1, AC061992.1) associated with prognostic value, and they were used to construct an autophagy-related lncRNA prognostic signature (ALPS) model. ALPS model offered an independent prognostic value (HR = 1.664, 1.381-2.006), where this risk score of the model was significantly related to the TNM stage, ER, PR and HER2 status in breast cancer patients. Nomogram could be utilized to predict survival for patients with breast cancer. Principal component analysis and Sankey Diagram results indicated that the distribution of five lncRNAs from the ALPS model tends to be low-risk. Gene set enrichment analysis showed that the high-risk group was enriched in autophagy and cancer-related pathways, and the low-risk group was enriched in regulatory immune-related pathways. These results indicated that the ALPS model composed of five autophagy-related lncRNAs could predict the prognosis of breast cancer patients.     

Screening of significant biomarkers related with prognosis of liver cancer by lncRNA-associated ceRNAs analysis

This study aimed to identify significant biomarkers related to the prognosis of liver cancer using long noncoding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) analysis. Differentially expressed mRNA and lncRNAs between liver cancer and paracancerous tissues were screened, and the functions of these mRNAs were predicted by gene ontology and pathway enrichment analyses. A ceRNA network consisting of differentially expressed mRNAs and lncRNAs was constructed. LncRNA FENDRR and lncRNA HAND2-AS1 were hub nodes in the ceRNA network. A risk score assessment model consisting of eight genes (PDE2A, ESR1, FBLN5, ALDH8A1, AKR1D1, EHHADH, ADRA1A, and GNE) associated with prognosis were developed. Multivariate Cox regression suggested that both pathologic_T and risk group could be regarded as independent prognostic factors. Furthermore, a nomogram model consisting of pathologic_T and risk group showed a good prediction ability for predicting the survival rate of liver cancer patients. The nomogram model consisting of pathologic_T and a risk score assessment model could be regarded as an independent factor for predicting prognosis of liver cancer.     

Development and validation of a five-lncRNA signature with prognostic value in colon cancer

Dysregulation of long noncoding RNAs (lncRNAs) has been found in a large number of human cancers, including colon cancer. Therefore, the implementation of potential lncRNAs biomarkers with prognostic prediction value are very much essential. GSE39582 data set was downloaded from database of Gene Expression Omnibus. Re-annotation analysis of lncRNA expression profiles was performed by NetAffx annotation files. Univariate and multivariate Cox proportional analyses helped select prognostic lncRNAs. Algorithm of random survival forest-variable hunting (RSF-VH) together with stepwise multivariate Cox proportional analysis were performed to establish lncRNA signature. The log-rank test was carried out to analyze and compare the Kaplan-Meier survival curves of patients’ overall survival (OS). Receiver operating characteristic (ROC) analysis was used for comparing the survival prediction regarding its specificity and sensitivity based on lncRNA risk score, followed by calculating the values of area under the curve (AUC). The single-sample GSEA (ssGSEA) analysis was used to describe biological functions associated with this signature. Finally, to determine the robustness of this model, we used the validation sets including GSE17536 and The Cancer Genome Atlas data set. After re-annotation analysis of lncRNAs, a total of 14 lncRNA probes were obtained by univariate and multivariate Cox proportional analysis. Then, the RSF-VH algorithm and stepwise multivariate Cox analysis helped to build a five-lncRNA prognostic signature for colon cancer. The patients in group with high risk showed an obviously shorter survival time compared with patients in group with low risk with AUC of 0.75. In addition, the five-lncRNA signature can be used to independently predict the survival of patients with colon cancer. The ssGSEA analysis revealed that pathways such as extracellular matrix-receptor interaction was activated with an increase in risk score. These findings determined the strong power of prognostic prediction value of this five-lncRNA signature for colon cancer.     

Construction of an immune-related LncRNA signature with prognostic significance for bladder cancer

Bladder cancer (BLCA) is one of the most common urological cancer with increasing cases and deaths every year. In the present study, we aim to construct an immune-related prognostic lncRNA signature (IRPLS) in bladder cancer (BLCA) patients and explore its immunogenomic implications in pan-cancers. First, the immune-related differentially expressed lncRNAs (IRDELs) were identified by ‘limma’ R package and the score of IRPLS in every patient were evaluated by Cox regression. The dysregulation of IRDELs expression between cancer and para-cancer normal tissues was validated through RT-qPCR. Then, we further explore the biological functions of a novel lncRNA from IRPLS, RP11-89 in BLCA using CCK8 assay, Transwell assay and Apoptosis analysis, which indicated that RP11-89 was able to promote cell proliferation and invasive capacity while inhibits cell apoptosis in BLCA. In addition, we performed bioinformatic methods and RIP to investigate and validate the RP11-89/miR-27a-3p/PPARγ pathway in order to explore the mechanism. Next, CIBERSORT and ESTIMATE algorithm were used to evaluate abundance of tumour-infiltrating immune cells and scores of tumour environment elements in BLCA with different level of IRPLS risk scores. Finally, multiple bioinformatic methods were performed to show us the immune landscape of these four lncRNAs for pan-cancers. In conclusion, this study first constructed an immune-related prognostic lncRNA signature, which consists of RP11-89, PSORS1C3, LINC02672 and MIR100HG and might shed lights on novel targets for individualized immunotherapy for BLCA patients.     

Five key lncRNAs considered as prognostic targets for predicting pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, and the 5‐year survival rate was only 7.7%. To improve prognosis, a screening biomarker for early diagnosis of pancreatic cancer is in urgent need. Long non‐coding RNA (lncRNA) expression profiles as potential cancer prognostic biomarkers play critical roles in development of tumorigenesis and metastasis of cancer. However, lncRNA signatures in predicting the survival of a patient with PDAC remain unknown. In the current study, we try to identify potential lncRNA biomarkers and their prognostic values in PDAC. LncRNAs expression profiles and corresponding clinical information for 182 cases with PDAC were acquired from The Cancer Genome Atlas (TCGA). A total of 14 470 lncRNA were identified in the cohort, and 175 PDAC patients had clinical variables. We obtained 108 differential expressed lncRNA via R packages. Univariate and multivariate Cox proportional hazards regression, lasso regression was performed to screen the potential prognostic lncRNA. Five lncRNAs have been recognized to significantly correlate with OS. We established a linear prognostic model of five lncRNA (C9orf139, MIR600HG, RP5‐965G21.4, RP11‐436K8.1, and CTC‐327F10.4) and divided patients into high‐ and low‐risk group according to the prognostic index. The five lncRNAs played independent prognostic biomarkers of OS of PDAC patients and the AUC of the ROC curve for the five lncRNAs signatures prediction 5‐year survival was 0.742. In addition, targeted genes of MIR600HG, C9orf139, and CTC‐327F10.4 were explored and functional enrichment was also conducted. These results suggested that this five‐lncRNAs signature could act as potential prognostic biomarkers in the prediction of PDAC patient's survival.     

Long noncoding RNA HOXA-AS2 regulates the expression of SCN3A by sponging miR-106a in breast cancer

Breast cancer is the most commonly diagnosed cancer that affects women worldwide. This study aimed to investigate the competing endogenous RNAs (ceRNAs) mechanism in breast cancer. Microarray data were downloaded from the University of California Santa Cruz (UCSC) Xena database. The limma package was used to screen the differentially expressed messenger RNAs (DEMs) and differentially expressed long noncoding RNAs (DELs). Subsequently, functional analysis was performed using DAVID tool. After constructing the protein-protein interaction (PPI) network, we identified the major gene modules using the Cytoscape software. Univariate survival analysis in the survival package was performed. Finally, the ceRNA regulatory network was constructed to identify the critical genes. A total of 1380 DEMs and 345 DELs were identified in breast cancer samples compared with normal samples. Functional enrichment analysis showed that DEMs were mainly involved in cell division, and cell cycle. We screened four major gene modules and identified the hub nodes in these functional modules. Several DEMs (including FABP7, C4BPA, and LAMB3) and three long noncoding RNAs (lncRNAs) (LINC00092, SLC26A4.AS1, and COLCA1) exhibited significant correlation with patients' survival outcomes. In the ceRNA network, the lncRNA HOXA-AS2 regulated the expression level of SCN3A by interacting with hsa-miR-106a-5p. Thus, our study investigated the ceRNA mechanism in breast cancer. The results showed that lncRNA HOXA-AS2 might modulate the expression of SCN3A by sponging miR-106a in breast cancer.     

LncRNAs as potential diagnostic and prognostic biomarkers in gastric cancer: A novel approach to personalized medicine

Gastric cancer is the third leading cause of cancer death with 5-year survival rate of about 30–35%. Since early detection is associated with decreased mortality, identification of novel biomarkers for early diagnosis and proper management of patients with the best response to therapy is urgently needed. Long noncoding RNAs (lncRNAs) due to their high specificity, easy accessibility in a noninvasive manner, as well as their aberrant expression under different pathological and physiological conditions, have received a great attention as potential diagnostic, prognostic, or predictive biomarkers. They may also serve as targets for treating gastric cancer. In this review, we highlighted the role of lncRNAs as tumor suppressors or oncogenes that make them potential biomarkers for the diagnosis and prognosis of gastric cancer. Relatively, lncRNAs such as H19, HOTAIR, UCA1, PVT1, tissue differentiation-inducing nonprotein coding, and LINC00152 could be potential diagnostic and prognostic markers in patients with gastric cancer. Also, the impact of lncRNAs such as ecCEBPA, MLK7-AS1, TUG1, HOXA11-AS, GAPLINC, LEIGC, multidrug resistance-related and upregulated lncRNA, PVT1 on gastric cancer epigenetic and drug resistance as well as their potential as therapeutic targets for personalized medicine was discussed.     

Long non‐coding RNA SNHG14 induces trastuzumab resistance of breast cancer via regulating PABPC1 expression through H3K27 acetylation

Currently, resistance to trastuzumab, a human epidermal growth factor receptor 2 (HER2) inhibitor, has become one major obstacle for improving the clinical outcome of patients with advanced HER2⁺ breast cancer. While cell behaviour can be modulated by long non‐coding RNAs (lncRNAs), the contributions of lncRNAs in progression and trastuzumab resistance of breast cancer are largely unknown. To this end, the involvement and regulatory functions of lncRNA SNHG14 in human breast cancer were investigated. RT‐qPCR assay showed that SNHG14 was up‐regulated in breast cancer tissues and associated with trastuzumab response. Gain‐ and loss‐of‐function experiments revealed that overexpression of SNHG14 promotes cell proliferation, invasion and trastuzumab resistance, whereas knockdown of SNHG14 showed an opposite effect. PABPC1 gene was identified as a downstream target of SNHG14, and PABPC1 mediates the SNHG14‐induced oncogenic effects. More importantly, ChIP assays demonstrated that lncRNA SNHG14 may induce PABPC1 expression through modulating H3K27 acetylation in the promoter of PABPC1 gene, thus resulting in the activation of Nrf2 signalling pathway. These data suggest that lncRNA SNHG14 promotes breast cancer tumorigenesis and trastuzumab resistance through regulating PABPC1 expression through H3K27 acetylation. Therefore, SNHG14 may serve as a novel diagnostic and therapeutic target for breast cancer patients.     

Identification of key lncRNAs in the carcinogenesis and progression of colon adenocarcinoma by co-expression network analysis

Colon adenocarcinoma (COAD) is one of the most common cancers, and its carcinogenesis and progression is influenced by multiple long non-coding RNAs (lncRNA), especially through the miRNA sponge effect. In this study, more than 4000 lncRNAs were re-annotated from the microarray datasets through probe sequence mapping to obtain reliable lncRNA expression profiles. As a systems biology method for describing the correlation patterns among genes across microarray samples, weighted gene co-expression network analysis was conducted to identify lncRNA modules associated with the five stepwise stages from normal colonic samples to COAD (n = 94). In the most relevant module (R² = −0.78, P = 4E-20), four hub lncRNAs were identified (CTD-2396E7.11, PCGF5, RP11-33O4.1, and RP11-164P12.5). Then, these four hub lncRNAs were validated using two other independent datasets including GSE20916 (n = 145) and GSE39582 (n = 552). The results indicated that all hub lncRNAs were significantly negatively correlated with the three-stage colonic carcinogenesis, as well as TNM stages in COAD (one-way analysis of variance P < 0.05). Kaplan-Meier survival curve showed that patients with higher expression of each hub lncRNA had a significantly higher overall survival rate and lower relapse risk (log-rank P < 0.05). In conclusion, through co-expression analysis, we identified and validated four key lncRNAs in association with the carcinogenesis and progression of COAD, and these lncRNAs might have important clinical implications for improving the risk stratification, therapeutic decision and prognosis prediction in COAD patients.