Identification and validation of stemness-related lncRNA prognostic signature for breast cancer.
ABSTRACT: BACKGROUND:Long noncoding RNAs (lncRNAs) are emerging as crucial contributors to the development of breast cancer and are involved in the stemness regulation of breast cancer stem cells (BCSCs). LncRNAs are closely associated with the prognosis of breast cancer patients. It is critical to identify BCSC-related lncRNAs with prognostic value in breast cancer. METHODS:A co-expression network of BCSC-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA) was constructed. Univariate and multivariate Cox proportional hazards analyses were used to identify a stemness risk model with prognostic value. Kaplan-Meier analysis, univariate and multivariate Cox regression analyses and receiver operating characteristic (ROC) curve analysis were performed to validate the risk model. Principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation were conducted to analyze the risk model. RESULTS:In this study, BCSC-related lncRNAs in breast cancer were identified. We evaluated the prognostic value of these BCSC-related lncRNAs and eventually obtained a prognostic risk model consisting of 12 BCSC-related lncRNAs (Z68871.1, LINC00578, AC097639.1, AP003119.3, AP001207.3, LINC00668, AL122010.1, AC245297.3, LINC01871, AP000851.2, AC022509.2 and SEMA3B-AS1). The risk model was further verified as a novel independent prognostic factor for breast cancer patients based on the calculated risk score. Moreover, based on the risk model, the low- risk and high-risk groups displayed different stemness statuses. CONCLUSIONS:These findings suggested that the 12 BCSC-related lncRNA signature might be a promising prognostic factor for breast cancer and can promote the management of BCSC-related therapy in clinical practice.
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:Cancer stem cells (CSCs) are considered the roots of cancer metastasis and recurrence (CSCs), due in part to their self-renewal and therapy resistance properties. However, the underlying mechanisms for the regulation of CSC stemness are poorly understood. Recently, increasing evidence shows that long non-coding RNAs (lncRNAs) are critical regulators for cancer cell function in various malignancies including breast cancer, but how lncRNAs regulate the function of breast cancer stem cells (BCSCs) remains to be determined. Herein, using lncRNA/mRNA microarray assays, a novel lncRNA (named lnc030) is identified, which is highly expressed in BCSCs in vitro and in vivo, as a pivotal regulator in maintaining BCSC stemness and promoting tumorigenesis. Mechanistically, lnc030 cooperates with poly(rC) binding protein 2(PCBP2) to stabilize squalene epoxidase (SQLE) mRNA, resulting in an increase of cholesterol synthesis. The increased cholesterol in turn actives PI3K/Akt signaling, which governs BCSC stemness. In summary, these findings demonstrate that a new, lnc030-based mechanism for regulating cholesterol synthesis and stemness properties of BCSCs. The lnc030-SQLE-cholesterol synthesis pathway may serve as an effective therapeutic target for BCSC elimination and breast cancer treatment.
Project description:<h4>Background</h4>Endometrial cancer is among the most common malignant tumors threatening the health of women. Recently, immunity and long noncoding RNA (lncRNA) have been widely examined in oncology and shown to play important roles in oncology. Here, we searched for immune-related lncRNAs as prognostic biomarkers to predict the outcome of patients with endometrial cancer.<h4>Methods</h4>RNA sequencing data for 575 endometrial cancer samples and immune-related genes were downloaded from The Cancer Genome Atlas (TCGA) database and gene set enrichment analysis (GSEA) gene sets, respectively. Immune-related lncRNAs showing a coexpression relationship with immune-related genes were obtained, and Cox regression analysis was performed to construct the prognostic model. Survival, independent prognostic, and clinical correlation analyses were performed to evaluate the prognostic model. Immune infiltration of endometrial cancer samples was also evaluated. Functional annotation of 12 immune-related lncRNAs was performed using GSEA software. Prognostic nomogram and survival analysis for independent prognostic risk factors were performed to evaluate the prognostic model and calculate the survival time based on the prognostic model.<h4>Results</h4>Twelve immune-related lncRNAs (ELN-AS1, AC103563.7, PCAT19, AF131215.5, LINC01871, AC084117.1, NRAV, SCARNA9, AL049539.1, POC1B-AS1, AC108134.4, and AC019080.5) were obtained, and a prognostic model was constructed. The survival rate in the high-risk group was significantly lower than that in the low-risk group. Patient age, pathological grade, the International Federation of Gynecology and Obstetrics (FIGO) stage, and risk status were the risk factors. The 12 immune-related lncRNAs correlated with patient age, pathological grade, and FIGO stage. Principal component analysis and functional annotation showed that the high-risk and low-risk groups separated better, and the immune status of the high-risk and low-risk groups differed. Nomogram and receiver operating characteristic (ROC) curves effectively predicted the prognosis of endometrial cancer. Additionally, age, pathological grade, FIGO stage, and risk status were all related to patient survival.<h4>Conclusion</h4>We identified 12 immune-related lncRNAs affecting the prognosis of endometrial cancer, which may be useful as therapeutic targets and molecular biomarkers.
Project description:Breast cancer is an aggressive disease with a high incidence in women worldwide. Two decades ago, a controversial hypothesis was proposed that cancer arises from a subpopulation of "tumor initiating cells" or "cancer stem cells-like" (CSC). Today, CSC are defined as small subset of somatic cancer cells within a tumor with self-renewal properties driven by the aberrant expression of genes involved in the maintenance of a stemness-like phenotype. The understanding of the underlying cellular and molecular mechanisms involved in the maintenance of CSC subpopulation are fundamental in the development and persistence of breast cancer. Nowadays, the hypothesis suggests that genetic and epigenetic alterations give rise to breast cancer stem cells (bCSC), which are responsible for self-renewal, tumor growth, chemoresistance, poor prognosis and low survival in patients. However, the prominence of bCSC, as well as the molecular mechanisms that regulates and promotes the malignant phenotypes, are still poorly understood. The role of non-coding RNAs (ncRNAs), such as long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) acting as oncogenes or tumor suppressor genes has been recently highlighted by a plethora of studies in breast cancer. These ncRNAs positively or negatively impact on different signaling pathways that govern the cancer hallmarks associated with bCSC, making them attractive targets for therapy. In this review, we present a current summary of the studies on the pivotal roles of lncRNAs and microRNAs in the regulation of genes associated to stemness of bCSC.
Project description:Breast cancer risk prediction models are used to plan clinical trials and counsel women; however, relationships of predicted risks of breast cancer incidence and prognosis after breast cancer diagnosis are unknown.Using largely pre-diagnostic information from the Breast Cancer Surveillance Consortium (BCSC) for 37,939 invasive breast cancers (1996-2007), we estimated 5-year breast cancer risk (<1%; 1-1.66%; ?1.67%) with three models: BCSC 1-year risk model (BCSC-1; adapted to 5-year predictions); Breast Cancer Risk Assessment Tool (BCRAT); and BCSC 5-year risk model (BCSC-5). Breast cancer-specific mortality post-diagnosis (range: 1-13 years; median: 5.4-5.6 years) was related to predicted risk of developing breast cancer using unadjusted Cox proportional hazards models, and in age-stratified (35-44; 45-54; 55-69; 70-89 years) models adjusted for continuous age, BCSC registry, calendar period, income, mode of presentation, stage and treatment. Mean age at diagnosis was 60 years.Of 6,021 deaths, 2,993 (49.7%) were ascribed to breast cancer. In unadjusted case-only analyses, predicted breast cancer risk ?1.67% versus <1.0% was associated with lower risk of breast cancer death; BCSC-1: hazard ratio (HR) = 0.82 (95% CI = 0.75-0.90); BCRAT: HR = 0.72 (95% CI = 0.65-0.81) and BCSC-5: HR = 0.84 (95% CI = 0.75-0.94). Age-stratified, adjusted models showed similar, although mostly non-significant HRs. Among women ages 55-69 years, HRs approximated 1.0. Generally, higher predicted risk was inversely related to percentages of cancers with unfavorable prognostic characteristics, especially among women 35-44 years.Among cases assessed with three models, higher predicted risk of developing breast cancer was not associated with greater risk of breast cancer death; thus, these models would have limited utility in planning studies to evaluate breast cancer mortality reduction strategies. Further, when offering women counseling, it may be useful to note that high predicted risk of developing breast cancer does not imply that if cancer develops it will behave aggressively.
Project description:BACKGROUND:Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancersdevelopment and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Therefore, we aimed at developing an immune-related lncRNA signature to improve the prognosis prediction of breast cancer. METHODS:We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separated into training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan-Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate andmultivariate Cox regression analyses. RESULTS:A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group (p?=?1.215e?-?06 in the training set; p?=?0.0069 in the validation set; p?=?1.233e?-?07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set, 0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR?=?1.432; 95% CI 1.204-1.702, p?<?0.001), validation set (HR?=?1.162; 95% CI 1.004-1.345, p?=?0.044), and whole set (HR?=?1.240; 95% CI 1.128-1.362, p?<?0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways. CONCLUSIONS:We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.
Project description:Tumor initiation, development, and relapse may be closely associated with cancer stem cells (CSCs). The complicated mechanisms underlying the maintenance of CSCs are keeping in illustration. Long noncoding RNAs (lncRNAs), due to their multifunction in various biological processes, have been indicated to play a crucial role in CSC renewal and stemness maintenance. Using lncRNA array, we identified a novel lncRNA (named lnc408) in epithelial-mesenchymal transition-related breast CSCs (BCSCs). The lnc408 is high expressed in BCSCs in vitro and in vivo. The enhanced lnc408 is critical to BCSC characteristics and tumorigenesis. Lnc408 can recruit transcript factor SP3 to CBY1 promoter to serve as an inhibitor in CBY1 transcription in BCSCs. The high expressed CBY1 in non-BCSC interacts with 14-3-3 and β-catenin to form a ternary complex, which leads a translocation of the ternary complex into cytoplasm from nucleus and degradation of β-catenin in phosphorylation-dependent pattern. The lnc408-mediated decrease of CBY1 in BCSCs impairs the formation of 14-3-3/β-catenin/CBY1 complex, and keeps β-catenin in nucleus to promote CSC-associated CD44, SOX2, Nanog, Klf4, and c-Myc expressions and contributes to mammosphere formation; however, restoration of CBY1 expression in tumor cells reduces BCSC and its enrichment, thus lnc408 plays an essential role in maintenance of BCSC stemness. In shortly, these findings highlight that the novel lnc408 functions as an oncogenic factor by recruiting SP3 to inhibit CBY1 expression and β-catenin accumulation in nucleus to maintain stemness properties of BCSCs. Lnc408-CBY1-β-catenin signaling axis might serve as a new diagnostic and therapeutic target for breast cancer.
Project description:<h4>Background</h4>Colon adenocarcinoma (COAD) is the most common type of colon cancer. To date, however, the prognostic values of m6A RNA methylation-related long non-coding RNAs (lncRNAs) in COAD are largely unknown.<h4>Materials and methods</h4>The m6A-related lncRNAs were identified from The Cancer Genome Atlas (TCGA) data set. Univariate and multivariate Cox regression analyses were performed to explore the prognostic m6A-related lncRNAs. Consistent clustering analysis was performed to classify the COAD patients into different subgroups based on the expression of m6A-related lncRNAs. The potential biological functions as well as differences in the stemness index and tumor immune microenvironment between different subgroups were analyzed. The prognostic m6A-related lncRNAs were used to establish an m6A-related lncRNA risk model to predict prognosis and survival status.<h4>Results</h4>We identified 31 m6A-associated lncRNAs with prognostic values from the TCGA data set. Based on the expression of prognostic m6A-associated lncRNAs, TCGA-COAD patients were classified into three clusters using consistent clustering analysis. There was a low correlation of tumor stemness between the three clusters but a significant correlation with the tumor immune microenvironment as well as the tumor mutational load. Thirty-one prognostic-related m6A-associated lncRNAs were used to construct a risk model, which was further determined by survival analysis, receiver operating characteristic (ROC) curve, and univariate and multifactor Cox analysis. The m6A-related risk model demonstrates good performance in predicting prognosis and survival status. The model-based high-risk group exhibited poorer overall survival (OS) compared with the low-risk group.<h4>Conclusion</h4>In this study, we construct a risk model that consists of 31 m6A-related lncRNAs with independent prognostic values in COAD. Our study shows the critical roles of these 31 m6A-related lncRNAs in the tumor immune microenvironment, indicating the prospect of informing prognostic stratification and the development of immunotherapeutic strategies for COAD patients.
Project description:Glycolysis is critical for cancer stem cell reprogramming; however, the underlying regulatory mechanisms remain elusive. Here, we show that pyruvate dehydrogenase kinase 1 (PDK1) is enriched in breast cancer stem cells (BCSCs), whereas depletion of PDK1 remarkably diminishes ALDH<sup>+</sup> subpopulations, decreases stemness-related transcriptional factor expression, and inhibits sphere-formation ability and tumor growth. Conversely, high levels of PDK1 enhance BCSC properties and are correlated with poor overall survival. In mouse xenograft tumor, PDK1 is accumulated in hypoxic regions and activates glycolysis to promote stem-like traits. Moreover, through screening hypoxia-related long non-coding RNAs (lncRNAs) in PDK1-positive tissue, we find that lncRNA H19 is responsible for glycolysis and BCSC maintenance. Furthermore, H19 knockdown decreases PDK1 expression in hypoxia, and ablation of PDK1 counteracts H19-mediated glycolysis and self-renewal ability in vitro and in vivo. Accordingly, H19 and PDK1 expression exhibits strong correlations in primary breast carcinomas. H19 acting as a competitive endogenous RNA sequesters miRNA let-7 to release Hypoxia-inducible factor 1α, leading to an increase in PDK1 expression. Lastly, aspirin markedly attenuates glycolysis and cancer stem-like characteristics by suppressing both H19 and PDK1. Thus, these novel findings demonstrate that the glycolysis gatekeeper PDK1 has a critical role in BCSC reprogramming and provides a potential therapeutic strategy for breast malignancy.
Project description:Therapeutic resistance is a major clinical challenge in oncology. Evidence identifies cancer stem cells (CSCs) as a driver of tumor evolution. Accordingly, the key stemness property unique to CSCs may represent a reservoir of therapeutic target to improve cancer treatment. Here, we carried out a genome-wide RNA interference screen to identify genes that regulate breast CSCs-fate (bCSC). Using an interactome/regulome analysis, we integrated screen results in a functional mapping of the CSC-related processes. This network analysis uncovered potential therapeutic targets controlling bCSC-fate. We tested a panel of 15 compounds targeting these regulators. We showed that mifepristone, salinomycin, and JQ1 represent the best anti-bCSC activity. A combination assay revealed a synergistic interaction of salinomycin/JQ1 association to deplete the bCSC population. Treatment of primary breast cancer xenografts with this combination reduced the tumor-initiating cell population and limited metastatic development. The clinical relevance of our findings was reinforced by an association between the expression of the bCSC-related networks and patient prognosis. Targeting bCSCs with salinomycin/JQ1 combination provides the basis for a new therapeutic approach in the treatment of breast cancer.