Identification of an eight-lncRNA prognostic model for breast cancer using WGCNA network analysis and a Cox?proportional hazards model based on L1-penalized estimation.
ABSTRACT: An ever?increasing number of long noncoding (lnc)RNAs has been identified in breast cancer. The present study aimed to establish an lncRNA signature for predicting survival in breast cancer. RNA expression profiling was performed using microarray gene expression data from the National Center for Biotechnology Information Gene Expression Omnibus, followed by the identification of breast cancer?related preserved modules using weighted gene co?expression network (WGCNA) network analysis. From the lncRNAs identified in these preserved modules, prognostic lncRNAs were selected using univariate Cox regression analysis in combination with the L1?penalized (LASSO) Cox?proportional Hazards (Cox?PH) model. A risk score based on these prognostic lncRNAs was calculated and used for risk stratification. Differentially expressed RNAs (DERs) in breast cancer were identified using MetaDE. Gene Set Enrichment Analysis pathway enrichment analysis was conducted for these prognostic lncRNAs and the DERs related to the lncRNAs in the preserved modules. A total of five preserved modules comprising 73 lncRNAs were mined. An eight?lncRNA signature (IGHA1, IGHGP, IGKV2?28, IGLL3P, IGLV3?10, AZGP1P1, LINC00472 and SLC16A6P1) was identified using the LASSO Cox?PH model. Risk score based on these eight lncRNAs could classify breast cancer patients into two groups with significantly different survival times. The eight?lncRNA signature was validated using three independent cohorts. These prognostic lncRNAs were significantly associated with the cell adhesion molecules pathway, JAK?signal transducer and activator of transcription 5A pathway, and erbb pathway and are potentially involved in regulating angiotensin II receptor type 1, neuropeptide Y receptor Y1, KISS1 receptor, and C?C motif chemokine ligand 5. The developed eight?lncRNA signature may have clinical implications for predicting prognosis in breast cancer. Overall, this study provided possible molecular targets for the development of novel therapies against breast cancer.
Project description:The present study aimed to identify a long non?coding (lnc) RNAs?based signature for prognosis assessment in gastric cancer (GC) patients. By integrating gene expression data of GC and normal samples from the National Center for Biotechnology Information Gene Expression Omnibus, the EBI ArrayExpress and The Cancer Genome Atlas (TCGA) repositories, the common RNAs in Genomic Spatial Event (GSE) 65801, GSE29998, E?MTAB?1338, and TCGA set were screened and used to construct a weighted correlation network analysis (WGCNA) network for mining GC?related modules. Consensus differentially expressed RNAs (DERs) between GC and normal samples in the four datasets were screened using the MetaDE method. From the overlapped lncRNAs shared by preserved WGCNA modules and the consensus DERs, an lncRNAs signature was obtained using L1?penalized (lasso) Cox?proportional hazard (PH) model. LncRNA?mRNA networks were constructed for these signature lncRNAs, followed by functional annotation. A total of 14,824 common mRNAs and 2,869 common lncRNAs were identified in the 4 sets and 5 GC?associated WGCNA modules were preserved across all sets. MetaDE method identified 1,121 consensus DERs. A total of 50 lncRNAs were shared by preserved WGCNA modules and the consensus DERs. Subsequently, an 11?lncRNA signature was identified by LASSO?based Cox?PH model. The lncRNAs signature?based risk score could divide patients into 2 risk groups with significantly different overall survival and recurrence?free survival times. The predictive capability of this signature was verified in an independent set. These signature lncRNAs were implicated in several biological processes and pathways associated with the immune response, the inflammatory response and cell cycle control. The present study identified an 11?lncRNA signature that could predict the survival rate for GC.
Project description:Background:Prostate cancer (PCa) is the most common malignancy and the leading cause of cancer death in men. Recent studies suggest the molecular signature was more effective than the clinical indicators for the prognostic prediction, but all of the known studies focused on a single RNA type. The present study was to develop a new prognostic signature by integrating long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) and evaluate its prognostic performance. Methods:The RNA expression data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) or Gene Expression Omnibus database (GSE17951, GSE7076, and GSE16560). The PCa-driven modules were identified by constructing a weighted gene coexpression network, the corresponding genes of which were overlapped with differentially expressed RNAs (DERs) screened by the MetaDE package. The optimal prognostic signature was screened using the least absolute shrinkage and selection operator analysis. The prognostic performance and functions of the combined prognostic signature was then assessed. Results:Twelve PCa-driven modules were identified using TCGA dataset and validated in the GSE17951 and GSE7076 datasets, and six of them were considered to be preserved. A total of 217 genes in these 6 modules were overlapped with 699 DERs, from which a nine-gene prognostic signature was identified (including 3 lncRNAs and 6 mRNAs), and the risk score of each patient was calculated. The overall survival was significantly shortened in patients having the risk score higher than the cut-off, which was demonstrated in TCGA (p = 5.063E - 03) dataset and validated in the GSE16560 (p = 3.268E - 02) dataset. The prediction accuracy of this risk score was higher than that of clinical indicators (the Gleason score and prostate-specific antigen) or the single RNA type, with the area under the receiver operator characteristic curve of 0.945. Besides, some new therapeutic targets and mechanisms (MAGI2-AS3-SPARC/GJA1/CYSLTR1, DLG5-AS1-DEFB1, and RHPN1-AS1-CDC45/ORC) were also revealed. Conclusion:The risk score system established in this study may provide a novel reliable method to identify PCa patients at a high risk of death.
Project description:Dysregulated expression of lncRNAs has been observed in various human complex diseases (including cancers) by recent transcriptional profiling studies, highlighting potentials of lncRNAs as biomarkers for cancer diagnosis and prognosis. Despite some efforts have been made to search for novel lncRNA signature in breast cancer, the prognostic value of lncRNAs for ER-positive breast cancer patients still needs to be systematically investigated. In this study, we analyzed lncRNA expression profiles in a large of more than 600 breast cancer patients with ER-positive status from The Cancer Genome Atlas (TCGA) and identified six lncRNAs that are significantly associated with survival. Then a linear risk score model comprising six prognostic lncRNAs, termed six-lncRNA signature, was developed to identify high-risk patients from low-risk cases. The results of Kaplan-Meier analysis and ROC curves demonstrated the good sensitivity and specificity in survival prediction both in the training and testing datasets. Multivariate Cox regression analysis and stratified analysis showed that the six-lncRNA signature is an independent prognostic marker in survival prediction for ER-positive breast cancer patients. The GO enrichment analysis suggested that the six-lncRNA might involve with known breast cancer-related biological processes. With further experimental validation, these identified prognostic lncRNAs might have clinical implications for more personalized risk assessment for ER-positive breast cancer patients.
Project description:Purpose:This study focused on identification of long non-coding RNAs (lncRNAs) for prognosis prediction of glioblastoma (GBM) through weighted gene co-expression network analysis (WGCNA) and L1-penalized least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) model. Materials and methods:WGCNA was performed based on RNA expression profiles of GBM from Chinese Glioma Genome Atlas (CGGA), National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), and the European Bioinformatics Institute ArrayExpress for the identification of GBM-related modules. Subsequently, prognostic lncRNAs were determined using LASSO Cox PH model, followed by constructing a risk scoring model based on these lncRNAs. The risk score was used to divide patients into high- and low-risk groups. Difference in survival between groups was analyzed using Kaplan-Meier survival analysis. IncRNA-mRNA networks were built for the prognostic lncRNAs, followed by pathway enrichment analysis for these networks. Results:This study identified eight preserved GBM-related modules, including 188 lncRNAs. Consequently, C20orf166-AS1, LINC00645, LBX2-AS1, LINC00565, LINC00641, and PRRT3-AS1 were identified by LASSO Cox PH model. A risk scoring model based on the lncRNAs was constructed that could divide patients into different risk groups with significantly different survival rates. Prognostic value of this six-lncRNA signature was validated in two independent sets. C20orf166-AS1 was associated with antigen processing and presentation and cell adhesion molecule pathways, involving nine common genes. LBX2-AS1, LINC00641, PRRT3-AS1, and LINC00565 were related to focal adhesion, extracellular matrix receptor interaction, and mitogen-activated protein kinase signaling pathways, which shared 12 common genes. Conclusion:This prognostic six-lncRNA signature may improve prognosis prediction of GBM. This study reveals many pathways and genes involved in the mechanisms behind these lncRNAs.
Project description:<h4>Background</h4>Ovarian cancer (OC) is the most deadly gynaecological cancer, contributing significantly to female cancer-related deaths worldwide. Improving the outlook for OC patients depends on the identification of more reliable prognostic biomarkers for early diagnosis and survival prediction. The various roles of long non-coding RNAs (lncRNAs) in OC have attracted increasing attention. This study aimed to identify a lncRNA-based signature for survival prediction in OC patients.<h4>Methods</h4>RNA expression data and clinical information from a large number of OC patients were downloaded from a public database. These data were regarded as a training set to construct a weighed gene co-expression network analysis (WGCNA) network, mine stable modules, and screen differentially expressed lncRNAs. The prognostic lncRNAs were screened using univariate Cox regression analysis and the optimal prognosis lncRNA combination was screened using a Cox-PH model. The finalised lncRNA combination was used to construct the risk score system, which was validated and assessed for effectiveness using other independent datasets. Further functional pathway enrichment was performed using gene set enrichment analysis (GSEA).<h4>Results</h4>A co-expression network was constructed and four stable modules with OC-related biological functions were obtained. A total of 19 lncRNAs significantly related to prognosis of ovarian cancer were obtained using univariate Cox regression analysis, and the 5 prognostic signature lncRNAs GAS5, HCP5, PART1, SNHG11, and SNHG5 were used to establish a risk assessment system. The reliability of the prognostic scoring system was further confirmed using validation sets, which indicated that the risk assessment system could be used as an independent prognostic factor. Pathway enrichment analysis revealed that the network modules related to the above five prognostic genes were significantly associated with cell local adhesion, cancer signaling pathways, JAK-STAT signalling, and endogenous cell receptor interaction.<h4>Conclusions</h4>The risk score system established in this study could provide a novel reliable method to identify individuals at high risk of OC. In addition, the five prognostic lncRNAs identified here are promising potential prognostic biomarkers that could help to elucidate the pathogenesis of OC.
Project description:Long non-coding RNAs (lncRNAs) have been implicated in a variety of biological processes, and dysregulated lncRNAs have demonstrated potential roles as biomarkers and therapeutic targets for cancer prognosis and treatment. In this study, by repurposing microarray probes, we analyzed lncRNA expression profiles of 916 breast cancer patients from the Gene Expression Omnibus (GEO). Nine lncRNAs were identified to be significantly associated with metastasis-free survival (MFS) in the training dataset of 254 patients using the Cox proportional hazards regression model. These nine lncRNAs were then combined to form a single prognostic signature for predicting metastatic risk in breast cancer patients that was able to classify patients in the training dataset into high- and low-risk subgroups with significantly different MFSs (median 2.4 years versus 3.0 years, log-rank test p < 0.001). This nine-lncRNA signature was similarly effective for prognosis in a testing dataset and two independent datasets. Further analysis showed that the predictive ability of the signature was independent of clinical variables, including age, ER status, ESR1 status and ERBB2 status. Our results indicated that lncRNA signature could be a useful prognostic marker to predict metastatic risk in breast cancer patients and may improve upon our understanding of the molecular mechanisms underlying breast cancer metastasis.
Project description:<h4>Background</h4>Autophagy is a "self-feeding" phenomenon of cells, which is crucial in mammalian development. Long non-coding RNA (lncRNA) is a new regulatory factor for cell autophagy, which can regulate the process of autophagy to affect tumor progression. However, poor attention has been paid to the roles of autophagy-related lncRNAs in breast cancer.<h4>Objective</h4>This study aimed to construct an autophagy-related lncRNA signature that can effectively predict the prognosis of breast cancer patients and explore the potential functions of these lncRNAs.<h4>Methods</h4>The RNA sequencing (RNA-Seq) data of breast cancer patients was collected from The Cancer Genome Atlas (TCGA) database and the GSE20685 database. Multivariate Cox analysis was implemented to produce an autophagy-related lncRNA signature in the TCGA cohort. The signature was then validated in the GSE20685 cohort. The receiver operator characteristic (ROC) curve was performed to evaluate the predictive ability of the signature. Gene set enrichment analysis (GSEA) was used to explore the potential functions based on the signature. Finally, the study developed a nomogram and internal verification based on the autophagy-related lncRNAs.<h4>Results</h4>A signature composed of 9 autophagy-related lncRNAs was determined as a prognostic model, and 1,109 breast cancer patients were divided into high-risk group and low-risk group based on median risk score of the signature. Further analysis demonstrated that the over survival (OS) of breast cancer patients in the high-risk group was poorer than that in the low-risk group based on the prognostic signature. The area under the curve (AUC) of ROC curve verified the sensitivity and specificity of this signature. Additionally, we confirmed the signature is an independent factor and found it may be correlated to the progression of breast cancer. GSEA showed gene sets were notably enriched in carcinogenic activation pathways and autophagy-related pathways. The qRT-PCR identified 5 lncRNAs with significantly differential expression in breast cancer cells based on the 9 lncRNAs of the prognostic model, and the results were consistent with the tissues.<h4>Conclusion</h4>In summary, our signature has potential predictive value in the prognosis of breast cancer and these autophagy-related lncRNAs may play significant roles in the diagnosis and treatment of breast cancer.
Project description: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.
Project description:Head and neck squamous cell carcinoma (HNSCC) ranks as the sixth most common cancer among systemic malignant tumors, with 600 000 new cases occurring every year worldwide. Since HNSCC has high heterogeneity and complex pathogenesis, no effective prognostic indicator has yet been identified. Here, we aimed to identify a lncRNA signature associated with the prognosis of HNSCC as a potential new biomarker. LncRNA expression data were downloaded from The Cancer Genome Atlas database. A polygenic risk score model was constructed by using Lasso-Cox regression analysis. Weighted gene co-expression network analysis (WGCNA) was applied to analyze the co-expression modules of lncRNAs associated with the prognosis of HNSCC. The robustness of the signature was validated in testing and external cohorts. Polymerase chain reaction was performed to detect the expression levels of identified lncRNAs in cancer and adjacent tissues. We constructed an 8-lncRNA signature (LINC00567, LINC00996, MTOR-AS1, PRKG1-AS1, RAB11B-AS1, RPS6KA2-AS1, SH3BP5-AS1, ZNF451-AS1) that could be used as an independent prognostic factor of HNSCC. The signature showed strong robustness and had stable prediction performance in different cohorts. WGCNA results showed that modules related to risk score mainly participated in biological processes such as blood vessel development, positive regulation of catabolic processes, and regulation of growth. The prognostic risk score model based on lncRNA for HNSCC may help clinicians conduct individualized treatment plans.
Project description:Current studies have shown that long non-coding RNAs (lncRNAs) may serve as prognostic biomarkers in multiple cancers. Therefore, we postulated that expression patterns of multiple lncRNAs combined into a single signature could improve clinicopathological risk stratification and prediction of overall survival rate for breast cancer patients. Two algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to select candidate lncRNAs. Univariate and multivariate Cox regression analyses were employed to construct a seven-lncRNA signature for breast cancer. Stratified analysis revealed that the signature was significantly associated with multiple clinicopathological risk factors. For clinical use, we developed a nomogram model to predict overall survival and odds of death for breast cancer patients. Single-sample gene set enrichment analysis (ssGSEA), CIBERSORT algorithm and ESTIMATE method were employed to assess the relative immune cell infiltrations of each sample. Differentially infiltration of immune cells and diverse tumour mutation burden (TMB) scores might give rise to the efficacy of lncRNA signature for predicting the overall survival of patients. Correlation analysis implied that LINC01215 was associated with multiple immune-related signalling pathways. A seven-lncRNA prognostic signature is a reliable tool to predict the prognosis of breast cancer patients.