A novel autophagy-related lncRNA survival model for lung adenocarcinoma

Long non-coding RNA (lncRNA) is an important regulatory factor in the development of lung adenocarcinoma, which is related to the control of autophagy. LncRNA can also be used as a biomarker of prognosis in patients with lung adenocarcinoma. Therefore, it is important to determine the prognostic value of autophagy-related lncRNA in lung adenocarcinoma. In this study, autophagy-related mRNAs-lncRNAs were screened from lung adenocarcinoma and a co-expression network of autophagy-related mRNAs-lncRNAs was constructed by using The Cancer Genome Atlas (TCGA). The univariate and multivariate Cox proportional hazard analyses were used to evaluate the prognostic value of the autophagy-related lncRNAs and finally obtained a survival model composed of 11 autophagy-related lncRNAs. Through Kaplan-Meier analysis, univariate and multivariate Cox regression analysis and time-dependent receiver operating characteristic (ROC) curve analysis, it was further verified that the survival model was a new independent prognostic factor for patients with lung adenocarcinoma. In addition, based on the survival model, gene set enrichment analysis (GSEA) was used to illustrate the function of genes in low-risk and high-risk groups. These 11 lncRNAs were GAS6-AS1, AC106047.1, AC010980.2, AL034397.3, NKILA, AL606489.1, HLA-DQB1-AS1, LINC01116, LINC01806, FAM83A-AS1 and AC090559.1. The hazard ratio (HR) of the risk score was 1.256 (1.196-1.320) (P < .001) in univariate Cox regression analysis and 1.215 (1.149-1.286) (P < .001) in multivariate Cox regression analysis. And the AUC value of the risk score was 0.809. The 11 autophagy-related lncRNA survival models had important predictive value for the prognosis of lung adenocarcinoma and may become clinical autophagy-related therapeutic targets.     

Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

Background: The present study investigated the independent prognostic value of glycolysis-related long noncoding (lnc)RNAs in clear cell renal cell carcinoma (ccRCC).Methods: A coexpression analysis of glycolysis-related mRNAs–long noncoding RNAs (lncRNAs) in ccRCC from The Cancer Genome Atlas (TCGA) was carried out. Clinical samples were randomly divided into training and validation sets. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to establish a glycolysis risk model with prognostic value for ccRCC, which was validated in the training and validation sets and in the whole cohort by Kaplan–Meier, univariate and multivariate Cox regression, and receiver operating characteristic (ROC) curve analyses. Principal component analysis (PCA) and functional annotation by gene set enrichment analysis (GSEA) were performed to evaluate the risk model.Results: We identified 297 glycolysis-associated lncRNAs in ccRCC; of these, 7 were found to have prognostic value in ccRCC patients by Kaplan–Meier, univariate and multivariate Cox regression, and ROC curve analyses. The results of the GSEA suggested a close association between the 7-lncRNA signature and glycolysis-related biological processes and pathways.Conclusion: The seven identified glycolysis-related lncRNAs constitute an lncRNA signature with prognostic value for ccRCC and provide potential therapeutic targets for the treatment of ccRCC patients.     

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.     

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.     

The role of EMT-related lncRNA in the process of triple-negative breast cancer metastasis

Triple-negative breast cancer (TNBC) is the most malignant and fatal subtype of breast cancer, which has characterized by negativity expression of ER, PR, and HER2. Metastasis is the main factor affecting the prognosis of TNBC, and the process of metastasis is related to abnormal activation of epithelial–mesenchymal transition (EMT). Recent studies have shown that long non-coding RNA (LncRNA) plays an important role in regulating the metastasis and invasion of TNBC. Therefore, based on the metastasis-related EMT signaling pathway, great efforts have confirmed that LncRNA is involved in the molecular mechanism of TNBC metastasis, which will provide new strategies to improve the treatment and prognosis of TNBC. In this review, we summarized many signal pathways related to EMT involved in the transfer process. The advances from the most recent studies of lncRNAs in the EMT-related signal pathways of TNBC metastasis. We also discussed the clinical research, application, and challenges of LncRNA in TNBC.     

Comprehensive analysis of autophagy‐related prognostic genes in breast cancer

Accumulating evidence revealed that autophagy played vital roles in breast cancer (BC) progression. Thus, the aim of this study was to investigate the prognostic value of autophagy‐related genes (ARGs) and develop a ARG‐based model to evaluate 5‐year overall survival (OS) in BC patients. We acquired ARG expression profiling in a large BC cohort (N = 1007) from The Cancer Genome Atlas (TCGA) database. The correlation between ARGs and OS was confirmed by the LASSO and Cox regression analyses. A predictive model was established based on independent prognostic variables. Thus, time‐dependent receiver operating curve (ROC), calibration plot, decision curve and subgroup analysis were conducted to determine the predictive performance of ARG‐based model. Four ARGs (ATG4A, IFNG, NRG1 and SERPINA1) were identified using the LASSO and multivariate Cox regression analyses. A ARG‐based model was constructed based on the four ARGs and two clinicopathological risk factors (age and TNM stage), dividing patients into high‐risk and low‐risk groups. The 5‐year OS of patients in the low‐risk group was higher than that in the high‐risk group (P < 0.0001). Time‐dependent ROC at 5 years indicated that the four ARG–based tool had better prognostic accuracy than TNM stage in the training cohort (AUC: 0.731 vs 0.640, P < 0.01) and validation cohort (AUC: 0.804 vs 0.671, P < 0.01). The mutation frequencies of the four ARGs (ATG4A, IFNG, NRG1 and SERPINA1) were 0.9%, 2.8%, 8% and 1.3%, respectively. We built and verified a novel four ARG–based nomogram, a credible approach to predict 5‐year OS in BC, which can assist oncologists in determining effective therapeutic strategies.     

Identification of pyroptosis-related lncRNAs for constructing a prognostic model and their correlation with immune infiltration in breast cancer

The inflammasome-dependent cell death, which is denoted as pyroptosis, might be abnormally regulated during oncogenesis and tumour progression. Long non-coding RNAs (LncRNAs) are pivotal orchestrators in breast cancer (BC), which have the potential to be a biomarker for BC diagnosis and therapy. The present study aims to explore the correlation between pyroptosis-related lncRNAs and BC prognosis. In this study, a profile of 8 differentially expressed lncRNAs was screened in the TCGA database and used to construct a prognostic model. The BC patients were divided into high- and low-risk groups dependent on the median cutoff of the risk score in the model. Interestingly, the risk model significantly distinguished the clinical characteristics of BC patients between high- and low-risk groups. Then, the risk score of the model was identified to be an excellent independent prognostic factor. Notably, the GO, KEGG, GSEA and ssGSEA analyses revealed the different immune statuses between the high- and low-risk groups. Particularly, the 8 lncRNAs expressed differentially in BC tissues between two risk subgroups in vitro validation. Collectively, this constructed well-validated model is of high effectiveness to predict the prognosis of BC, which will provide novel means that is applicable for BC prognosis recognition.     

A robust immune-related lncRNA signature for the prognosis of human colorectal cancer

Background: Colorectal cancer (CRC) is one of the most prevalent malignant cancers worldwide. Immune-related long non-coding RNAs (IRlncRNAs) are proved to be essential in the development and progression of carcinoma. The purpose of the present study was to develop and validate a prognostic IRlncRNA signature for CRC patients.Methods: Gene expression profiles of CRC samples were downloaded from The Cancer Genome Atlas (TCGA) database. Immune-related genes were obtained from the ImmPort database and were used to identify IRlncRNA by correlation analysis. Through LASSO Cox regression analyses, a prognostic signature was constructed. Functional enrichment analysis was performed by gene set enrichment analysis (GSEA). TIMER2.0 web server and tumor immune dysfunction and exclusion (TIDE) algorithm were employed to analyze the association between our model and tumor-infiltrating immune cells and immunotherapy response. The expression levels of IRlncRNAs in cell lines were detected by quantitative real-time PCR (qPCR).Results: A 9-IRlncRNA signature was developed by a LASSO Cox proportional regression model. Based on the signature, CRC patients were divided into high- and low-risk groups with different prognoses. GSEA results indicated that patients in high-risk group were associated with cancer-related pathways. In addition, patients in low-risk group were found to have more infiltration of anti-tumor immune cells and might show a favorable response to immunotherapy. Finally, the result of qPCR revealed that most IRlncRNAs were differently expressed between normal and tumor cell lines.Conclusion: The constructed 9-IRlncRNA signature has potential to predict the prognosis of CRC patients and may be helpful to guide personalized immunotherapy.     

LncRNA ITGA9-AS1抑制乳腺癌细胞增殖并增强其对顺铂的敏感性

长链非编码RNAs (long non-coding RNAs, lncRNAs) 是一类长度大于200 nt,无蛋白质编码功能的RNAs。近年来,lncRNAs在肿瘤发生发展中的作用备受关注。LncRNAs芯片分析结合后期实时荧光定量PCR验证发现,ITGA9-AS1在MCF-7细胞中的表达量显著高于耐药细胞MCF-7/5Fu,且其在乳腺癌细胞中的表达量显著低于正常乳腺上皮细胞。生物信息学预测,ITGA9 AS1无蛋白质编码功能。在乳腺癌细胞T47D中过表达ITGA9-AS1,可显著抑制该细胞的增殖和克隆形成能力,增加该细胞对化疗药物顺铂（cisplatin, cDDP）的敏感性。相反,在乳腺上皮细胞MCF-10A中敲低ITGA9-AS1的表达,能够明显增加该细胞的增殖能力和克隆形成能力,同时降低该细胞对cDDP的敏感性。总之,lncRNA ITGA9-AS1可抑制乳腺癌细胞增殖,增强乳腺癌细胞对化疗药物的敏感性。     

lncRNAs功能注释和预测

随着测序技术的发展,在各种哺乳动物中发现越来越多的长非编码RNAs（long non-coding RNAs,lncRNAs）,但是大部分lncRNAs的功能却未知.鉴于lncRNAs在众多生物过程如免疫反应、发育和基因印迹中表现出对蛋白编码基因和其它非编码RNAs的重要调节作用,对lncRNAs的功能研究也成为生物学家和生物信息学家研究的热点. 其中,功能注释和预测是目前研究lncRNAs功能的主要方法之一.本文主要对lncRNAs功能注释和预测方法的研究进展作一综述,包括以下几个方面：基于共表达网络的方法、基于miRNAs的方法、基于蛋白质结合的方法、基于表观遗传修饰的方法以及基于ceRNA网络的方法. 为进一步研究lncRNAs的功能提供参考,同时为开发更加有效的注释或预测方法提供线索.     

T-cell receptors provide potential prognostic signatures for breast cancer

Although T-cell receptors (TCRs) are related to the progression of breast cancer (BC), their prognostic values remain unclear. We downloaded the messenger RNA (mRNA) profiles and corresponding clinical information of 1413 BC patients from the Cancer Genome Atlas and Gene Expression Omnibus database, respectively. The different expression analysis of 104 TCRs in BC samples was performed, and the consensus clustering based on 104 TCRs was performed by using the K-mean method of R language. Univariate cox regression analysis was used to screen TCRs significantly associated with the prognosis of BC, and LASSO Cox analysis was applied to optimize key TCRs. The risk score was calculated using the prognostic model constructed based on six optimal TCRs, and multivariate Cox regression analysis was used to determine whether it was an independent prognostic signature. Finally, the nomogram was constructed to predict the overall survival of BC patients. Six optimal TCRs (ZAP70, GRAP2, NFKBIE, IFNG, NFKBIA, and PAK5), which were favorable for the prognosis of BC patients, were screened. Risk score could reliably predict the prognosis of BC patients as an independent prognostic signature. In addition, when bringing into two independent prognostic signatures, age and risk score, the nomogram model could better predict the overall survival of BC patients. Our results suggested that the poor prognosis of BC patients with high risk might be due to an immunosuppressive microenvironment. In summary, a prognostic risk model based on six TCRs was established and could efficiently predict the prognosis of BC patients.     

The prognostic and therapeutic values of long noncoding RNA PANDAR in colorectal cancer

Long noncoding RNAs (lncRNAs) consist of 200 nucleotide sequences that play essential roles in different processes, including cell proliferation, and differentiation. There is evidence showing that the dysregulation of lncRNAs promoter of CDKN1A antisense DNA damage-activated RNA (PANDAR) leads to the development and progression in several cancers including colorectal cancer, via p53-dependent manner. This suggests that these lncRNAs may be of value as prognostic indices and a therapeutic target, as a high expression of lncRNAs PANDAR is associated with poor prognosis. Furthermore, modulating lncRNAs PANDAR has been reported to induce apoptosis and inhibit the tumor growth through modulation of cell cycle and epithelial-mesenchymal transition (EMT) pathway. The aim of the current review was to provide an overview of the prognostic and therapeutic values of lncRNAs PANDAR in colorectal cancer     

基于基因组不稳定性相关lncRNA的宫颈癌患者预后模型

结合TCGA数据库中宫颈癌的lncRNA表达谱和体细胞突变谱,构建基于突变假设的计算框架,鉴定出36个与宫颈癌基因组不稳定性相关的lncRNA;对其共表达的基因功能进行分析,发现与36个lncRNA共表达的基因在2-氧代戊二酸代谢过程和2-氧羧酸代谢通路中富集。构建了基于基因组不稳定性衍生的两个lncRNA的基因特征(GILncSig),将Train组患者分为高风险组和低风险组,两组患者生存率显著不同,这一结果在Test组患者中得到进一步验证。通过独立预后分析,结果显示GILncSig可独立于其他临床性状,作为宫颈癌患者的整体生存相关独立预后因子。总之,本研究为进一步探讨lncRNA在基因组不稳定性中的作用提供了关键的方法和资源,为识别基因组不稳定性相关的肿瘤标志物提供了新的预测方法。     

Identification of LINC01234 and MIR210HG as novel prognostic signature for colorectal adenocarcinoma

This study aimed to identify potential biomarkers and the therapeutic targets for colorectal adenocarcinoma by systematically evaluate a large scale of long noncoding RNAs (lncRNAs) expression data from TCGA. The algorithm t-distributed stochastic neighbor embedding and hierarchical clustering were utilized to group the samples into three clusters that showed a different prognosis. To identify the relationship between the clustered groups and different histoclinical features, different statistical methods were used. The functions of LINC01234 and MIR210HG were investigated with the help of the public database. The results showed that the expression levels of lncRNAs were able to distinguish the tumor samples from the normal tissues and in further they were able to predict the prognosis of the patients. We proposed two potential lncRNAs, which might serve as a biomarker or therapeutic targets. LINC01234 can be a good biomarker. In contrast, MIR210HG participated in the progression of colorectal adenocarcinoma by regulating hypoxia. It might function through an lncRNA–microRNA–messenger RNA regulatory network with MIR210 and RASSF7.     

A risk signature with four autophagy-related genes for predicting survival of glioblastoma multiforme

Glioblastoma multiforme (GBM) is a devastating brain tumour without effective treatment. Recent studies have shown that autophagy is a promising therapeutic strategy for GBM. Therefore, it is necessary to identify novel biomarkers associated with autophagy in GBM. In this study, we downloaded autophagy-related genes from Human Autophagy Database (HADb) and Gene Set Enrichment Analysis (GSEA) website. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were performed to identify genes for constructing a risk signature. A nomogram was developed by integrating the risk signature with clinicopathological factors. Time-dependent receiver operating characteristic (ROC) curve and calibration plot were used to evaluate the efficiency of the prognostic model. Finally, four autophagy-related genes (DIRAS3, LGALS8, MAPK8 and STAM) were identified and were used for constructing a risk signature, which proved to be an independent risk factor for GBM patients. Furthermore, a nomogram was developed based on the risk signature and clinicopathological factors (IDH1 status, age and history of radiotherapy or chemotherapy). ROC curve and calibration plot suggested the nomogram could accurately predict 1-, 3- and 5-year survival rate of GBM patients. For function analysis, the risk signature was associated with apoptosis, necrosis, immunity, inflammation response and MAPK signalling pathway. In conclusion, the risk signature with 4 autophagy-related genes could serve as an independent prognostic factor for GBM patients. Moreover, we developed a nomogram based on the risk signature and clinical traits which was validated to perform better for predicting 1-, 3- and 5-year survival rate of GBM.     

A novel antisense lncRNA NT5E promotes progression by modulating the expression of SYNCRIP and predicts a poor prognosis in pancreatic cancer

A novel antisense lncRNA NT5E was identified in a previous microarray that was clearly up‐regulated in pancreatic cancer (PC) tissues. However, its biological function remains unclear. Thus, we aimed to explore its function and clinical significance in PC. The lncNT5E expression was determined in PC specimens and cell lines. In vitro and in vivo studies detected the impact of lncNT5E depletion on PC cell proliferation, migration and invasion. Western blotting investigated the epithelial‐mesenchymal transition (EMT) markers. The interaction between lncNT5E and the promoter region of SYNCRIP was detected by dual‐luciferase reporter assay. The role of lncNT5E in modulating SYNCRIP was investigated in vitro. Our results showed that lncNT5E was significantly up‐regulated in PC tissues and cell lines and associated with poor prognosis. LncNT5E depletion inhibited PC cell proliferation, migration, invasion and EMT in vitro and caused tumorigenesis arrest in vivo. Furthermore, SYNCRIP knockdown had effects similar to those of lncNT5E depletion. A significant positive relationship was observed between lncNT5E and SYNCRIP. Moreover, the dual‐luciferase reporter assays indicated that lncNT5E depletion significantly inhibited SYNCRIP promoter activity. Importantly, the malignant phenotypes of lncNT5E depletion were rescued by overexpressing SYNCRIP. In conclusion, lncNT5E predicts poor prognosis and promotes PC progression by modulating SYNCRIP expression.