Identification of lncRNAs associated with early-stage breast cancer and their prognostic implications.
Ontology highlight
ABSTRACT:
SUBMITTER: Deva Magendhra Rao AK
PROVIDER: S-EPMC6547626 | biostudies-literature | 2019 Jun
REPOSITORIES: biostudies-literature
Publications
Identification of lncRNAs associated with early-stage breast cancer and their prognostic implications.
Deva Magendhra Rao Arunagiri Kuha AK Patel Krishna K Korivi Jyothiraj Suneetha S Meenakumari Balaiah B Sundersingh Shirley S Sridevi Velusami V Rajkumar Thangarajan T Pandey Akhilesh A Chatterjee Aditi A Gowda Harsha H Mani Samson S
Molecular oncology 20190508 6
Breast cancer is the most common malignancy among women, with the highest incidence rate worldwide. Dysregulation of long noncoding RNAs during the preliminary stages of breast carcinogenesis is poorly understood. In this study, we performed RNA sequencing to identify long noncoding RNA expression profiles associated with early-stage breast cancer. RNA sequencing was performed on six invasive ductal carcinoma (IDC) tissues along with paired normal tissue samples, seven ductal carcinoma in situ t ...[more]
Similar Datasets
Project description:BackgroundIncreased breast cancer screening over the past four decades has led to a substantial rise in the diagnosis of ductal carcinoma in situ (DCIS). Although DCIS lesions precede invasive ductal carcinoma (IDC), they do not always transform into cancer. The current standard-of-care for DCIS is an aggressive course of therapy to prevent invasive and metastatic disease resulting in over-diagnosis and over-treatment. Thus, there is a critical need to identify functional determinants of progression of DCIS to IDC to allow discrimination between indolent and aggressive disease. Recent studies show that super-enhancers, in addition to promoting other gene transcription, are themselves transcribed producing super-enhancer associated long noncoding RNAs (SE-lncRNAs). These SE-lncRNAs can interact with their associated enhancer regions in cis and influence activities and expression of neighboring genes. Furthermore, they represent a novel, untapped group of therapeutic targets.MethodsWith an integrative analysis of enhancer loci with global expression of SE-lncRNAs in the MCF10A progression series, we have identified differentially expressed SE-lncRNAs which can identify mechanisms for DCIS to IDC progression. Furthermore, cross-referencing these SE-lncRNAs with patient samples in the The Cancer Genome Atlas (TCGA) database, we have unveiled 27 clinically relevant SE-lncRNAs that potentially interact with their enhancer to regulate nearby gene expression. To complement SE-lncRNA expression studies, we conducted an unbiased global analysis of super-enhancers that are acquired or lost in progression.ResultsHere we designate SE-lncRNAs RP11-379F4.4 and RP11-465B22.8 as potential markers of progression of DCIS to IDC through regulation of the expression of their neighboring genes (RARRES1 and miR-200b, respectively). Moreover, we classified 403 super-enhancer regions in MCF10A normal cells, 627 in AT1, 1053 in DCIS, and 320 in CA1 cells. Comparison analysis of acquired/lost super-enhancer regions with super-enhancer regions classified in 47 ER positive patients, 10 triple negative breast cancer (TNBC) patients, and 11 TNBC cell lines reveal critically acquired pathways including STAT signaling and NF-kB signaling. In contrast, protein folding, and local estrogen production are identified as major pathways lost in progression.ConclusionCollectively, these analyses identify differentially expressed SE-lncRNAs and acquired/lost super-enhancers in progression of breast cancer important for promoting DCIS lesions to IDC.
| S-EPMC8557595 | biostudies-literature
Project description:BackgroundIncreasing evidence has demonstrated that long non-coding RNAs (lncRNAs) play a crucial part in maintaining genomic instability. We therefore identified genome instability-related lncRNAs and constructed a prediction signature for early stage lung adenocarcinoma (LUAD) as well in order for classification of high-risk group of patients and improvement of individualized therapies.MethodsEarly stage LUAD RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were randomly divided into training set (n = 177) and testing set (n = 176). A total of 146 genomic instability-associated lncRNAs were identified based on somatic mutation profiles combining lncRNA expression profiles from TCGA by the "limma R" package. We performed Cox regression analysis to develop this predictive indicator. We validated the prognostic signature by an external independent LUAD cohort with microarray platform acquired from the Gene Expression Omnibus (GEO).ResultsA genome instability-related six-lncRNA-based gene signature (GILncSig) was established to divide subjects into high-risk and low-risk groups with different outcomes at statistically significant levels. According to the multivariate Cox regression and stratification analysis, the GILncSig was an independent predictive factor. Furthermore, the six-lncRNA signature achieved AUC values of 0.745, 0.659, and 0.708 in the training set, testing set, and TCGA set, respectively. When compared with other prognostic lncRNA signatures, the GILncSig also exhibited better prediction performance.ConclusionThe prognostic lncRNA signature is a potent tool for risk stratification of early stage LUAD patients. Our study also provided new insights for identifying genome instability-related cancer biomarkers.
| S-EPMC8324209 | biostudies-literature
Project description:This study aimed to identify microRNAs associated with histological grade using comprehensive microRNA analysis data obtained by next-generation sequencing from early-stage invasive breast cancer. RNA-seq data from normal breast and breast cancer samples were compared to identify candidate microRNAs with differential expression using bioinformatics. A total of 108 microRNAs were significantly differentially expressed in normal breast and breast cancer tissues. Using clinicopathological information and microRNA sequencing data of 430 patients with breast cancer from The Cancer Genome Atlas (TCGA), the differences in candidate microRNAs between low- and high-grade tumors were identified. Comparing the expression of the 108 microRNAs between low- and high-grade cases, 25 and 18 microRNAs were significantly upregulated and downregulated, respectively, in high-grade cases. Clustering analysis of the TCGA cohort using these 43 microRNAs identified two groups strongly predictive of histological grade. miR-3677 is a microRNA upregulated in high-grade breast cancer. The outcome analysis revealed that patients with high miR-3677 expression had significantly worse prognosis than those with low miR-3677 expression. This study shows that microRNAs are associated with histological grade in early-stage invasive breast cancer. These findings contribute to the elucidation of a new mechanism of breast cancer growth regulated by specific microRNAs.
| S-EPMC10779190 | biostudies-literature
Project description:The pathophysiology of ovarian cancer (OV) is complex and depends on multiple biological processes and pathways. Therefore, there is an urgent need to identify reliable prognostic biomarkers for predicting clinical outcomes and helping personalize treatment of OV. A long non-coding RNA (lncRNA)-based risk score model was constructed to infer the prognostic efficacy of transcription factors (TFs) based on the OV dataset from The Cancer Genome Atlas. The risk score model was further validated in other independent cohorts from Gene Expression Omnibus. Time-dependent receiver operating characteristic curves were used to evaluate the survival prediction performance in comparison with other clinical and molecular variables. Our results revealed that the top-ranked TF-associating lncRNAs were significantly associated with overall survival, progression-free survival and disease-free survival. Stratification analysis according to clinical variables indicated the prognostic independence of POLR2A-associating lncRNAs. In comparison, the signature of POLR2A-associating lncRNAs was more sensitive and specific than existing clinical and molecular signatures. Functional and experimental analysis suggested that POLR2A-associating lncRNAs may be involved in known biological processes and pathways of OV. Our findings revealed that the lncRNA-based risk score model can provide helpful information on OV prognosis stratification and discovery of therapeutic biomarkers.
| S-EPMC6378234 | biostudies-literature
Project description:To examine the role of germline genetic variations in inflammatory pathways as modifiers of time to recurrence (TTR) in patients with early stage breast cancer (BC), DNA from 997 early stage BC patients was genotyped for 53 tagging single nucleotide polymorphisms (SNPs) in 12 genes involved in inflammation. SNPs were analyzed separately for Caucasians versus African-Americans and Hispanics. Cox proportional hazards models were used to evaluate the association between SNPs in the inflammatory genes and TTR, adjusted for clinical and pathologic covariates. In univariable analyses of Caucasian women, the homozygous genotype of 12 SNPs, including 6 NFKB1 SNPs, 4 IL4 SNPs, and 2 IL13 SNPs, were significantly associated with a decrease in TTR compared with the heterozygous and/or corresponding homozygous genotype (P < 0.05). The significant NFKB1 and IL4 SNPs were in an area of high linkage disequilibrium (D' > 0.8). After adjusting for stage, age, and treatment, carriage of the homozygous genotypes for NFKB1 rs230532 and IL13rs1800925 were independently associated with a shorter TTR (P = 0.001 and P = 0.034, respectively). In African-American and Hispanic patients, expression of NFKB1 rs3774932, TNFrs1799964, and IL4rs3024543 SNPs were associated with a shorter TTR in univariable model. Only NFKB1 rs3774932 (P = 0.02) and IL4Rrs3024543 (P = 0.03) had independent prognostic value in the multivariable model These data support the existence of host genetic susceptibility as a component in recurrence risk mediated by pro-inflammatory and immune factors, and suggest the potential for drugs which modify immune responses and inflammatory genes to improve prognosis in early stage BC.
| S-EPMC3746974 | biostudies-literature
Project description:Long noncoding RNAs (lncRNAs) play a critical role in the immune regulation and tumor microenvironment of pancreatic cancer (PaCa). To construct a novel immune-related prognostic risk model for PaCa and evaluate the prognostic prediction of lncRNAs, essential immune-related lncRNAs (IRlncRNAs) were identified by Pearson correlation analysis of differentially expressed immune-related genes (IRGs) and IRlncRNAs in PaCa from The Cancer Genome Atlas (TCGA) and GTEx databases. Least absolute shrinkage and selection operator (LASSO) regression was also applied to construct a prognostic risk model of IRlncRNAs, and gene set enrichment analysis (GSEA) was further applied for functional annotation for these IRlncRNAs. A total of 148 IRlncRNAs were identified in PaCa to construct a prognostic risk model. Among them, lncRNA LINC02325, FNDC1-AS1, and ZEB2-AS1 were significantly upregulated in 69 pairs of PaCa tissues by qRT-PCR. ROC analyses showed that LINC02325 (AUC = 0.80), FNDC1-AS1 (AUC = 0.76), and ZEB2-AS1 (AUC = 0.75) had a good predictive effect on 5-year survival prognosis. We demonstrated that high expression levels of ZEB2-AS1 and LINC02325 were not only positively associated with tumor size and CA199, but elevated levels of ZEB2-AS1 and FNDC1-AS1 were also positively correlated with tumor stage. GSEA further revealed that immune-related pathways were mainly enriched in the high-risk groups. Several immune-related algorithms demonstrated that four IRlncRNAs were related to immune infiltration, immune checkpoints, and immune-related functions. Thus, the prognostic risk model based on IRlncRNAs in Paca indicates that the four IRlncRNA signatures may serve as predictors of survival and potential predictive biomarkers of the pancreatic tumor immune response.
| S-EPMC9676238 | biostudies-literature
Project description:Breast cancer is a major cause of cancer-related death in women worldwide. Non-coding RNAs are a potential resource to be used as an early diagnostic biomarker for breast cancer. Circular RNAs are a recently identified group of non-coding RNA with a significant role in disease development with potential utility in diagnosis/prognosis in cancer. In this study, we identified 26 differentially expressed circular RNAs associated with early-stage breast cancer. RNA sequencing and two circRNA detection tools (find_circ and DCC) were used to understand the circRNA expression signature in breast cancer. We identified hsa_circ_0006743 (circJMJD1C) and hsa_circ_0002496 (circAPPBP1) to be significantly up-regulated in early-stage breast cancer tissues. Co-expression analysis identified four pairs of circRNA-miRNA (hsa_circ_0023990 : hsa-miR-548b-3p, hsa_circ_0016601 : hsa_miR-1246, hsa_circ_0001946 : hsa-miR-1299 and hsa_circ_0000117:hsa-miR-502-5p) having potential interaction. The miRNA target prediction and network analysis revealed mRNA possibly regulated by circRNAs. We have thus identified circRNAs of diagnostic implications in breast cancer and also observed circRNA-miRNA interaction which could be involved in breast cancer development.
| S-EPMC8051735 | biostudies-literature
Project description:BackgroundLong non-coding RNAs (lncRNAs) participate in the regulation of immune response and carcinogenesis, shaping tumor immune microenvironment, which could be utilized in the construction of prognostic signatures for non-small cell lung cancer (NSCLC) as supplements.MethodsData of patients with stage I-III NSCLC was downloaded from online databases. The least absolute shrinkage and selection operator was used to construct a lncRNA-based prognostic model. Differences in tumor immune microenvironments and pathways were explored for high-risk and low-risk groups, stratified by the model. We explored the potential association between the model and immunotherapy by the tumor immune dysfunction and exclusion algorithm.ResultsOur study extracted 15 immune-related lncRNAs to construct a prognostic model. Survival analysis suggested better survival probability in low-risk group in training and validation cohorts. The combination of tumor, node, and metastasis staging systems with immune-related lncRNA signatures presented higher prognostic efficacy than tumor, node, and metastasis staging systems. Single sample gene set enrichment analysis showed higher infiltration abundance in the low-risk group, including B cells (p<0.001), activated CD8+ T cells (p<0.01), CD4+ T cells (p<0.001), activated dendritic cells (p<0.01), and CD56+ Natural killer cells (p<0.01). Low-risk patients had significantly higher immune scores and estimated scores from the ESTIMATE algorithm. The predicted proportion of responders to immunotherapy was higher in the low-risk group. Critical pathways in the model were enriched in immune response and cytoskeleton.ConclusionsOur immune-related lncRNA model could describe the immune contexture of tumor microenvironments and facilitate clinical therapeutic strategies by improving the prognostic efficacy of traditional tumor staging systems.
| S-EPMC8564147 | biostudies-literature
Project description:ImportanceIt is unclear whether ERBB2-low breast cancer should be considered an individual biologic subtype distinct from ERBB2-0 breast cancer.ObjectiveTo investigate whether low ERBB2 expression is associated with distinct clinicopathologic characteristics and prognosis among patients with hormone receptor (HR)-positive and triple-negative breast cancer (TNBC).Design, setting, and participantsThis cohort study was conducted using data from a prospectively maintained institutional database on all consecutive patients with breast cancer undergoing surgery between January 2016 and March 2021 at Dana-Farber Brigham Cancer Center. The study included 5235 patients with stage I through III, ERBB2-negative invasive breast cancer. Tumors were classified as ERBB2-low if they had an ERBB2 immunohistochemical (IHC) score of 1+ or 2+ with negative in situ hybridization assay and ERBB2-0 if they had an ERBB2 IHC score of 0. Data were analyzed from September 2021 through January 2022.ExposuresStandard treatment according to institutional guidelines.Main outcomes and measuresComparison of clinicopathologic characteristics and disease outcomes (pathologic complete response rate [pCR], disease-free survival, distant disease-free survival, and overall survival) between patients with ERBB2-low and ERBB2-0 breast cancer.ResultsAmong 5235 patients with ERBB2-negative invasive breast cancer (5191 [99.2%] women; median [range] age at primary surgery, 59.0 [21.0-95.0] years), 2917 patients (55.7%) and 2318 patients (44.3%) had ERBB2-low and ERBB2-0 tumors, respectively. Expression of HR was significantly more common among ERBB2-low compared with ERBB2-0 tumors (2643 patients [90.6%] vs 1895 patients [81.8%]; P < .001). The rate of ERBB2-low tumors increased progressively, from 296 of 739 estrogen receptor (ER)-negative tumors (40.1%) to 31 of 67 ER-low (ie, ER 1%-9%) tumors (46.3%), 37 of 67 ER-moderate (ie, ER, 10%-49%) tumors (55.2%), 2047 of 3542 ER-high (ie, ER, 50%-95%) tumors (57.8%), and 499 of 803 ER-very high (ie, ER > 95%) tumors (62.1%) (P < .001). Among 675 patients receiving neoadjuvant chemotherapy, those with ERBB2-0 tumors experienced higher pCR rates (95 patients [26.8%] vs 53 patients [16.6%]; P = .002). However, there were no statistically significant differences in pCR rate between ERBB2-low and ERBB2-0 tumors when separately analyzing HR-positive, ER-low, HR-positive without ER-low, or TNBC tumors. In exploratory survival analysis, no differences by ERBB2-low expression in disease-free survival, distant disease-free survival, or overall survival were observed among patients with HR-positive tumors or TNBC.Conclusions and relevanceThe results of this cohort study did not support the interpretation of ERBB2-low breast cancer as a distinct biologic subtype. ERBB2-low expression was positively associated with level of ER expression, and ER-low tumors were enriched among ERBB2-0 tumors, suggesting that, given the worse prognosis of ER-low tumors, they may be associated with confounding of prognostic analyses of ERBB2-low expression.
| S-EPMC9227690 | biostudies-literature
Project description:Methylation of RNA plays an important role in cancer. Classical forms of such modifications include N6-methyladenine (m6A), 5-methylcytosine (m5C), and N1-methyladenine (m1A). Methylation-regulated long non-coding (lnc) RNAs are involved in various biological processes, such as tumor proliferation, apoptosis, immune escape, invasion, and metastasis. Therefore, we performed an analysis of transcriptomic and clinical data of pancreatic cancer samples in The Cancer Genome Atlas (TCGA). Using the co-expression method, we summarized 44 m6A/m5C/m1A-related genes and obtained 218 methylation-associated lncRNAs. Next, with COX regression, we screened 39 lncRNAs that are strongly associated with prognosis and found that their expression differed significantly between normal tissues and pancreatic cancer samples (P < 0.001). We then used the least absolute shrinkage and selection operator (LASSO) to construct a risk model comprising seven lncRNAs. In validation set, the nomogram generated by combining clinical characteristics accurately predicted the survival probability of pancreatic cancer patients at 1, 2, and 3 years after diagnosis (AUC = 0.652, 0.686, and 0.740, respectively). Tumor microenvironment analysis showed that the high-risk group had significantly more resting memory CD4 T cells, M0 macrophages, and activated dendritic cells and fewer naïve B cells, plasma cells, and CD8 T cells than the low-risk group (both P < 0.05). Most immune-checkpoint genes were significantly different between the high- and low-risk groups (P < 0.05). The Tumor Immune Dysfunction and Exclusion score showed that high-risk patients benefited more from treatment with immune checkpoint inhibitors (P < 0.001). Overall survival was also lower in high-risk patients with more tumor mutations than in low-risk patients with fewer mutations (P < 0.001). Finally, we explored the sensitivity of the high- and low-risk groups to seven candidate drugs. Our findings indicated that m6A/m5C/m1A-associated lncRNAs are potentially useful biomarkers for the early diagnosis and estimating the prognosis of, and ascertaining the responses to immunotherapy in, patients with pancreatic cancer.
| S-EPMC9985641 | biostudies-literature