lncRNAs In Breast Cancer Research Articles

Expression analysis of Rho GTPase-related lncRNAs in breast cancer

The Rho GTPases have prominent roles in cell cycle transition and cell migration. Some members of this family have been found to be mutated in cancers. Moreover, alterations in expression levels and/or activity of these proteins have been reported in many types of cancers. Thus, Rho GTPases are involved in the carcinogenesis. Rho GTPases regulate growth, motility, invasiveness and metastatic ability of breast cancer cells. Long non-coding RNAs (lncRNAs) have been revealed to exert significant effect in the regulation of these proteins via direct routes or through sequestering microRNAs that inhibit Rho GTPases. We aimed to assess expression levels of four Rho GTPase-related lncRNAs, namely NORAD, RAD51-AS1, NRAV and DANCR in breast cancer samples versus non-cancerous specimens from the same individuals. Expression levels of NORAD were shown to be elevated in tumoral tissues compared with non-tumoral tissues (Expression ratio (95% CI)= 5.85 (3.16–10.83), Standard error of mean (SEM)= 0.44, P value< 0.0001). NRAV expression was also higher in tumoral tissues compared with control tissues (Expression ratio=2.85 (1.52–5.35), SEM= 0.45, P value= 0.0013). Similar to these lncRNAs, RHOA was demonstrated to be up-regulated in malignant tissues (Expression ratio=6.58 (3.17–13.63), SEM= 0.52, P value< 0.0001). Although expression ratio values showed up-regulation of RAD51-AS1 and DANCR in cancerous tissues (Expression ratio (95% CI)= 2.2 (1.05–4.6) and 1.35 (0.72–2.53), respectively), P values did not reach significance level (P values=0.0706 and 0.3746, respectively). There were significant associations between expression level of NRAV gene in tumor tissues and a number of parameters including age, histological tumor grade and tubule formation. Taken together, the current study shows dysregulation of a number of RHOA-related lncRNAs in breast cancer in association with abnormal up-regulation of this member of Rho GTPase family and suggests conduction of additional functional studies to unravel their mode of participation in the breast carcinogenesis.

LINC00365 functions as a tumor suppressor by inhibiting HIF-1α-mediated glucose metabolism reprogramming in breast cancer

Long non-coding RNAs (lncRNAs) play an important role in regulating several physiological processes and have been implicated in several pathologies including cancer. LncRNAs have been found to regulate key cellular pathways involved in cancer development, and their aberrant expression plays critical roles in the onset or progression of disease. The role of lncRNAs in breast cancer (BC) has become a hot topic of research in recent years. We previously showed that LINC00365 inhibits BC survival. In the current study, based on the important role of energy metabolism and HIF-1α for tumor cell proliferation, we investigated the role and mechanism of the LINC00365/HIF-1α axis in affecting tumor growth through glycolysis using the breast cancer cell lines MCF-7 and HCC-1937. We found that LINC00365 inhibited BC cell proliferation. Furthermore, LINC00365 overexpression suppressed aerobic glycolysis in BC cells. RNA-sequencing identified hypoxia-inducible factor-1α (HIF-1α), which has been linked with glycolysis and upregulates glycolysis-related genes, as a potential target gene of LINC00365. Accordingly, we found that LINC00365 overexpression resulted in decreased expression of key glycolytic enzymes such as downstream hexokinase 2 (HK2), recombinant pyruvate kinase isozymes M2 (PKM2) and lactate dehydrogenase A (LDHA). Our results suggest that targeting LINC00365 may reverse the glucose metabolism pattern of BC and effectively inhibit BC survival both in vitro and in vivo.

Application value of circulating LncRNA in diagnosis, treatment, and prognosis of breast cancer.

Breast cancer is the malignant tumor with the highest incidence in women worldwide. It is highly heterogeneous, has a high incidence of drug resistance, recurrence, and metastasis, and is one of the malignant tumors with the highest mortality rate. The early diagnosis, treatment monitoring, and prognosis assessment of breast cancer are the key factors affecting the survival of patients. However, due to the lack of specific biomarkers, breast cancer is still an essential factor affecting women's quality of life and physical and mental health. Long non-coding RNA can regulate various genes and different signaling pathways and plays an essential role in the occurrence and development of tumors. Recent studies have found that the abnormal expression of circulating long non-coding RNA in serum, saliva, and other biological body fluids plays a significant role in early diagnosis, pathological classification, stage, therapeutic effect monitoring, and prognosis evaluation of breast cancer. This article will review the potential application value of circulating lncRNA in breast cancer.

Down-regulation of SLC16A-AS1 and LINC00900 lncRNAs in Iranian patients with breast cancer.

Long non-coding RNAs (lncRNAs) influence pathetiology of breast cancer. Besides, VDR and ESR1 signaling pathways are two important pathways in this malignancy. In the present mixed bioinformatics and expression assay study, we have identified lncRNAs that are co-expressed with VDR and ESR1 in breast cancer tissues and analyzed their expression in 42 paired breast cancer and non-cancerous specimens. Expression of SLC16A-AS1 was significantly lower in breast cancer tissues compared with paired non-cancerous samples (expression ratio = 0.27, P value < 0.001). Similarly, LINC00900 was down-regulated in cancer tissues compared with non-cancerous ones (expression ratio = 0.26, P value = 0.01). There were no significant differences in the expressions of VDR and AATBC between these two sets of samples. Expression levels of VDR and AATBC were associated with histological grade (P values = 0.02 and 0.03, respectively). Moreover, expression of VDR was associated with tumor size (Pvalue = 0.02). Finally, expression levels of SLC16A-AS1 were associated with first pregnancy age (P value = 0.006). In brief, the results of current study further support involvement of VDR and ESR1-associated lncRNAs in breast cancer.

Dysregulated ceRNA network modulated by copy number variation-driven lncRNAs in breast cancer: A comprehensive analysis.

Breast cancer is a malignancy harmful to physical and mental health in women, with quite high mortality. Copy number variations (CNVs) are vital factors affecting the progression of breast cancer. Detecting CNV in breast cancer to predict the prognosis of patients has become a promising accurate treatment in recent years. The differential analysis was performed on CNV of lncRNAs as well as the expression of lncRNAs, miRNAs and mRNAs in the normal tissue and breast tumor tissue based on The Cancer Genome Atlas (TCGA) database. And the CNV-driven lncRNAs were identified by Kruskal-Wallis test. Meanwhile, a competitive endogenous RNA (ceRNA) network regulated by CNV-driven lncRNA was constructed. As the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed, the mRNAs in the dysregulated ceRNA network were mainly enriched in the biological functions and signaling pathways including Focal Adhesion-PI3K-Akt-mTOR-signaling pathway, Neuronal System, Metapathway biotransformation Phase I and II and blood circulation, etc. The relationship between CNVs of 5 lncRNAs and their gene expression in the ceRNA network was analyzed via Chi-square test, which confirmed that except LINC00243, the expression of 4 lncRNAs was notably correlated with CNVs. The survival analysis revealed that only copy number gain of LINC00536 was evidently related to poor prognosis of patients. CIBERSORT algorithm showed that 5 lncRNAs were correlated with the abundance of immune cell infiltration and immune checkpoints. In a word, by analyzing CNV-driven lncRNAs and the ceRNA network regulated by these lncRNAs, this study explored the mechanism of breast cancer and provided novel insights into new biomarkers.

Characteristics of m6A-related LncRNAs in breast cancer as prognostic biomarkers and immunotherapy.

N6-methyladenosine (m6A) is a common RNA modification in coding and non-coding RNAs and plays an important role in the occurrence and development of breast cancer (BC). However, the role of m6A-related lncRNAs in breast cancer prognosis is unclear. This study aimed to help verify the biological function of m6A-related lncRNAs in breast cancer prognosis through bio-informatics techniques. First, we screened 18 m6A-related lncRNAs from the TCGA database: AL137847.1, AC137932.2, OTUD6B-AS1, MORF4L2-AS1, AC078846.1, AC012442.1, AL118556.1, AL138955.1, AC009754.1, AC024257.4, AL391095.1, AC024270.3, AC087392.1, LINC02649, AC090948.2, AL158212.1, ITGA6-AS1, AL133243.2 and constructed a risk-prognosis model based on this. Based on the model's median risk score, BC patients were divided into high-risk and low-risk groups. Then, the predictive value of the model was verified by Cox regression, Lasso regression, Kaplan-Meier curve and ROC curve analysis, and biological differences between the two groups were verified by GO enrichment analysis, tumor mutation burden, immune indications and in vitro tests. Importantly, the risk score of this prognostic model is an excellent independent prognostic factor, and m6A regulators are differentially expressed in patients with different risks. In addition, based on patients' different sensitivities to drugs, some drug candidates for different risk populations are screened to provide targets for breast cancer treatment. The difference in immune function between high-risk and low-risk patients also affected the sensitivity to immunotherapy. In the validation of clinical samples, we analyzed the expression of relevant lncRNAs in different risk groups and speculated the possible impact on the prognosis of breast cancer patients. The risk assessment tool built based on the full analysis of these m6A-related genes and m6A-related lncRNA libraries, as well as the m6A-related lncRNAs, has a high prognostic prediction ability, which may provide a supplementary screening method for accurately judging the prognosis of BC and a new perspective for personalized treatment of breast cancer patients.

Expression of Treg-associated lncRNAs in breast cancer.

Regulatory T cells (Tregs) have important functions in tumor microenvironment, particularly for induction of immune evasion. In order to find the underlying mechanism of dysregulation of Tregs in breast cancer tissues, we designed the current study to appraise expression of five Treg-related long non-coding RNAs (lncRNAs), namely FLICR (FOXP3 Regulating Long Intergenic Non-Coding RNA), NEST (IFNG-AS1), RMRP (RNA Component of Mitochondrial RNA Processing Endoribonuclease), MAFTRR (MAF Transcriptional Regulator RNA) and TH2-LCR (Th2 Cytokine Locus Control Region) in paired breast cancer and nearby noncancerous tissues. Expression levels of RMRP, TH2-LCR, MAFTRR and GATA3-AS1 were significantly higher in breast cancer samples compared with non-tumoral tissues. The calculated AUC values for GATA3-AS1, TH2-LCR, RMRP and MAFTRR were 0.66, 0.63, 0.63 and 0.60, respectively. There were significant positive associations between expression level of RMRP gene in tumor tissues and nuclear grade, tubule formation and tumor sizes. In addition, there was a significant positive association between expression levels of MAFTRR genes in tumor tissues and nuclear grade. Besides, expression levels of FLICR were different among tumors with different levels of HER2/neu receptor. Taken together, Treg-associated lncRNAs might contribute to the pathogenesis of breast cancer.

H3K27 acetylation activated long noncoding RNA RP11-162G10.5 promotes breast cancer progression via the YBX1/GLO1 axis.

Long noncoding RNAs (lncRNAs) orchestrate critical roles in human tumorigenesis. However, the regulatory mechanism of lncRNAs in tissue-specific expressions in breast cancer (BC) remains poorly understood. This study aims to investigate lncRNA role and mechanisms in BC. RNA sequencing was used to explore differentially expressed lncRNAs in BC and adjacent tissues. H3K27 acetylation (H3K27ac) chromatin immune-precipitation sequencing (ChIP-seq) data of BC cells from the GEO dataset (GSE85158) was retrieved to identify the H3K27ac activated lncRNAs that were involved in tumorigenesis. RP11-162G10.5 was selected as the target lncRNA for further functional and mechanism study. In this study, we identified a novel lncRNA RP11-162G10.5, whose overexpression was specifically driven by H3K27ac in luminal breast cancer. And increased RP11-162G10.5 in BC is correlated with poor patient outcomes. RP11-162G10.5 promotes tumor cell proliferation in vitro and in vivo. Mechanistically, RP11-162G10.5 recruits transcriptional factor YBX1 to the GLO1 promoter, consequently activating GLO1 transcription to modulate the progression of BC. Our findings suggest that the histone modification-activated lncRNA contributes to the oncogenesis of BC. Also, our data reveal a role for RP11-162G10.5 in BC tumorigenesis and may supply a strategy for targeting the RP11-162G10.5 as a potential biomarker and a therapeutic target for breast cancerpatients.

A 5-Pathway Signature Predicts Prognosis Based on Immune-Derived lncRNAs in Patients with Breast Cancer.

Currently, predictive models were not developed based on the signaling pathway signatures of immune-related lncRNAs in breast cancer (BRCA) patients. We selected unsupervised hierarchical clustering algorithm to classify patients with BRCA based on the significant immune-derived lncRNAs from the TCGA dataset. And different methods including ESTIMATE, ImmuneCellAI, and CIBERSORT were performed to evaluate the immune infiltration of tumor microenvironment. Using Lasso regression algorithm, we filtered the significant signaling pathways enriched by GSEA, GSVA, or PPI analysis to develop a prognostic model. And a nomogram integrated with clinical factors and significant pathways was constructed to predict the precise probability of overall survival (OS) of BRCA patients in the TCGA dataset (n = 1,098) and another two testing sets (n = 415). BRCA patients were stratified into the PC (n = 571) and GC (n = 527) subgroup with significantly different prognosis with 550 immune-related lncRNAs in the TCGA dataset. Integrated analysis revealed different immune response, oncogenic signaling, and metabolic reprograming pathways between these two subgroups. And a 5-pathway signature could predict the prognosis of BRCA patients between these two subgroups independently in the TCGA dataset, which was confirmed in another two cohorts from the GEO dataset. In the TCGA dataset, 5-year OS rate was 78% (95% CI: 73-84) vs. 82% (95% CI: 77-87) for the PC and GC group (HR = 1.63 (95% CI: 1.17-2.28), p = 0.004). The predictive power was similar in another two testing sets (HR > 1.20, p < 0.01). Finally, a nomogram is developed for clinical application, which integrated this signature and age to accurately predict the survival probability in BRCA patients. This 5-pathway signature correlated with immune-derived lncRNAs was able to precisely predict the prognosis for patients with BRCA and provided a rich source characterizing immune-related lncRNAs and further informed strategies to target BRCA vulnerabilities.

Glucose metabolism and lncRNAs in breast cancer: Sworn friend.

Glucose metabolism disorder is a common feature in cancer. Cancer cells generate much energy through anaerobic glycolysis, which promote the development of tumors. However, long non-coding RNA may play an important role in this process. Our aim is to explore a prognostic risk model based on the glucose metabolism-related lncRNAs which provides clues that lncRNAs predict a clinical outcome through glucose metabolism in breast cancer. 1222 RNA-seq were extracted from the TCGA database, and 74 glucose metabolism-related genes were loaded from the GSEA website. Then, 7 glucose metabolism-related lncRNAs risk score model was developed by univariate, Lasso, and multivariate regression analysis. The lncRNA risk model showed that high-risk patients predict a poor clinical outcome with high reliability (P=2.838×10-6). Univariate and multivariate independent prognostic analysis and ROC curve analysis proved that the risk score was an independent prognostic factor in breast cancer with an AUC value of 0.652. Finally, Gene set enrichment analysis showed that cell cycle-related pathways were significantly enriched in a high-risk group. Our results showed that glucose metabolism-related lncRNAs can affect breast cancer progression. 7 glucose metabolism-related lncRNAs prognostic signature was established to evaluate the OS of patients with breast cancer. PICSAR, LINC00839, AP001505.1, LINC00393 were risk factors and expressed highly in the high-risk group. A Nomogram was made based on this signature to judge patients' living conditions and prognosis. 7 glucose metabolism-related lncRNAs risk score model had a high prognostic value in breast cancer. PICSAR, LINC00839, AP001505.1, LINC00393 were risk factors. AP001505.1 expression was increased in most triple-negative breast cancer cells treated with high glucose, which may also take part in breast cancer progression and potential therapeutic targets.

The Potential of NORAD-PUMILIO-RALGAPB Regulatory Axis as a Biomarker in Breast Cancer.

Introduction: Long non-coding RNAs (LncRNA) represent a heterogeneous family of RNAs that have emerged as regulators of various biological processes through their association with proteins in ribonucleoproteins complexes. The dynamic of these interactions can affect cell metabolism, including cancer development. Annually, breast cancer causes thousands of deaths worldwide, and searching for new biomarkers is pivotal for better diagnosis and treatment. Methods: Based on in silico prediction analysis, we focus on LncRNAs that have binding sites for PUMILIO, an RBP family involved in post-transcriptional regulation and associated with cancer progression. We compared the expression levels of these LncRNAs in breast cancer and non-tumor samples from the TCGA database. We analyzed the impact of overall and disease-free survival associated with the expression of the LncRNAs and co-expressed genes and targets of PUMILIO proteins. Results: Our results found NORAD as the most relevant LncRNA with a PUMILIO binding site in breast cancer, differently expressed between Luminal A and Basal subtypes. Additionally, NORAD was co-expressed in a Basal-like subtype (0.55) with the RALGAPB gene, a target gene of PUMILIO related to chromosome stability during cell division. Conclusion: These data suggest that this molecular axis may provide insights for developing novel therapeutic strategies for breast cancer.

6P Deep learning radiomics supports perioperative treatment decisions and is associated with LncRNAs in breast cancer: A multicenter study

Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. This study aims to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological underpinning.

A novel prognostic signature based on N7-methylguanosine-related long non-coding RNAs in breast cancer.

Long non-coding RNA (lncRNA) are closely associated with the occurrence and progression of tumors. However, research on N7-methylguanosine (m7G)-related lncRNA in breast cancer is lacking. Therefore, the present study explored the prognostic value, gene expression characteristics, and effects of m7G-related lncRNA on tumor immune cell infiltration and tumor mutational burden (TMB) in breast cancer. lncRNA expression matrices and clinical follow-up data of patients with breast cancer were obtained from The Cancer Genome Atlas, revealing eight significantly differentially expressed and prognostically relevant m7G-related lncRNAs in breast cancer tissues: BAIAP2-DT, COL4A2-AS1, FARP1-AS1, RERE-AS1, NDUFA6-DT, TFAP2A-AS1, LINC00115, and MIR302CHG. A breast cancer prognostic signature was created based on these m7G-related lncRNAs according to least absolute shrinkage and selection operator Cox regression. The prognostic signature combined with potential prognostic factors showed independent prognostic value, reliability, and specificity. Meanwhile, we constructed a risk score-based nomogram to assist clinical decision-making. Gene set enrichment analysis revealed that low- and high-risk group were associated with metabolism-related pathways. Our study demonstrated the association between tumor immune cell infiltration based on analyses with the CIBERSORT algorithm and prognostic signature. We also assessed the correlation between prognostic signature and TMB. Lastly, quantitative real-time polymerase chain reaction analysis was performed to validate differentially expressed lncRNAs. The effective prognostic signature based on m7G-related lncRNAs has the potential to predict the survival prognosis of patients with breast cancer. The eight m7G-related lncRNAs identified in this study might represent potential biomarkers and therapeutic targets of breast cancer.

Identification of novel cuproptosis-related lncRNA signatures to predict the prognosis and immune microenvironment of breast cancer patients.

BackgroundCuproptosis is a new modality of cell death regulation that is currently considered as a new cancer treatment strategy. Nevertheless, the prognostic predictive value of cuproptosis-related lncRNAs in breast cancer (BC) remains unknown. Using cuproptosis-related lncRNAs, this study aims to predict the immune microenvironment and prognosis of BC patients. and develop new therapeutic strategies that target the disease.MethodsThe Cancer Genome Atlas (TCGA) database provided the RNA-seq data along with the corresponding clinical and prognostic information. Univariate and multivariate Cox regression analyses were performed to acquire lncRNAs associated with cuproptosis to establish predictive features. The Kaplan-Meier method was used to calculate the overall survival rate (OS) in the high-risk and low-risk groups. High risk and low risk gene sets were enriched to explore functional discrepancies among risk teams. The mutation data were analyzed using the “MAFTools” r-package. The ties of predictive characteristics and immune status had been explored by single sample gene set enrichment analysis (ssGSEA). Last, the correlation between predictive features and treatment condition in patients with BC was analyzed. Based on prognostic risk models, we assessed associations between risk subgroups and immune scores and immune checkpoints. In addition, drug responses in at-risk populations were predicted.ResultsWe identified a set of 11 Cuproptosis-Related lncRNAs (GORAB-AS1, AC 079922.2, AL 589765.4, AC 005696.4, Cytor, ZNF 197-AS1, AC 002398.1, AL 451085.3, YTH DF 3-AS1, AC 008771.1, LINC 02446), based on which to construct the risk model. In comparison to the high-risk group, the low-risk patients lived longer (p < 0.001). Moreover, cuproptosis-related lncRNA profiles can independently predict prognosis in BC patients. The AUC values for receiver operating characteristics (ROC) of 1-, 3-, and 5-year risk were 0.849, 0.779, and 0.794, respectively. Patients in the high-risk group had lower OS than those in the low-risk group when they were divided into groups based on various clinicopathological variables. The tumor burden mutations (TMB) correlation analysis showed that high TMB had a worse prognosis than low-TMB, and gene mutations were found to be different in high and low TMB groups, such as PIK3CA (36% versus 32%), SYNE1 (4% versus 6%). Gene enrichment analysis indicated that the differential genes were significantly concentrated in immune-related pathways. The predictive traits were significantly correlated with the immune status of BC patients, according to ssGSEA results. Finally, high-risk patients showed high sensitivity in anti-CD276 immunotherapy and conventional chemotherapeutic drugs such as imatinib, lapatinib, and pazopanib.ConclusionWe successfully constructed of a cuproptosis-related lncRNA signature, which can independently predict the prognosis of BC patients and can be used to estimate OS and clinical treatment outcomes in BRCA patients. It will serve as a foundation for further research into the mechanism of cuproptosis-related lncRNAs in breast cancer, as well as for the development of new markers and therapeutic targets for the disease.

LncRNA SEMA3B-AS1 inhibits breast cancer progression by targeting miR-3940/KLLN axis

Long noncoding RNAs (lncRNAs) play crucial regulatory roles in the progression of various cancers. However, the functional roles of lncRNAs in breast cancer remain unclear. In this study, we investigated the functional role of a novel long noncoding RNA SEMA3B-AS1 (lncRNA SEAS1) in breast cancer progression and the underlying mechanisms. SEAS1 was downregulated in the triple-negative breast cancer (TNBC) tissues compared with the para-carcinoma tissues, which was associated with poor prognosis of TNBC patients. We demonstrated that SEAS1 knockdown significantly increased the proliferation, migration, and invasion of TNBC cell lines, whereas SEAS1 overexpression reversed these effects. Bioinformatics analysis demonstrated that microRNA (miR)-3940-3p was a potential target of SEAS1. Mechanistically, RNA immunoprecipitation (RIP) and luciferase reporter assays confirmed that lncRNA SEMA3B-AS1 acted as sponge for miR-3940-3p, preventing the degradation of its target gene KLLN, which acts as a tumor-inhibiter in TNBC. Moreover, RNA pulldown, mass spectrometry, ChIP, and luciferase reporter assays confirmed that SMAD3 directly interacted with the promoter of SEAS1 and suppressed its transcription, thereby promoting TNBC progression. The clinical samples of TNBC confirmed SEAS1 was correlated inversely with lymphatic and distant metastasis. In conclusion, our findings reveal a novel pathway for TNBC progression via SMAD3/lncRNA SEAS1/miR-3940-3p/KLLN axis, and suggest that SEAS1 may serve as a potential biomarker and therapeutic target for TNBC.

A novel ferroptosis-related long noncoding RNA signature for relapse free survival prediction in patients with breast cancer.

Ferroptosis is the process of cell death dependent on iron. Growing evidence suggests that ferroptosis plays vital roles in the biological process of many cancers. However, just a small number of ferroptosis-related lncRNAs have been explored in depth.Ferroptosis-related lncRNAs in breast cancer (BC) were identified by co-expression analysis based on The Cancer Genome Atlas database (TCGA). The whole set was divided into a training set and a test set with a 1:1 ratio. Univariate Cox regression and LASSO analyses were performed to establish a signature in the 3 sets. Kaplan-Meier analysis and receiver operating characteristic (ROC) for the 3 sets validated the effectiveness and robustness of the signature. Besides, we also explore the relationship between this and clinical characteristics, immune cell infiltration and tumor microenvironment. Meanwhile, the nomogram was drawn by screening indicators of independent recurrent prediction. Finally, we evaluated the relationships between the signature and tumor microenvironment.We identified 391 ferroptosis-related lncRNAs and constructed a 5 lncRNAs-based signature in the training, test, and whole sets, stratifying patients into high-risk and low-risk groups. According to survival analysis, patients in the high-risk groups had worse relapse free survival (RFS) compared to the low risk-groups. The ROC curves indicated that the recurrent signature had a promising predictive capability for BC patients. Moreover, an independent factors-based nomogram model could offer the quantitative prediction and net benefit for the recurrence of BC patients. Finally, the microenvironment, including tumor mutational burden (TMB), immune cell functions and immune checkpoints, showed big differences between the 2 groups.The 5 ferroptosis-related lncRNAs and their signature might be novel promising biomarkers and immunotherapy targets for patients with BC.

Identification of expression of CCND1-related lncRNAs in breast cancer

Cyclin D1 has been shown to participate in the pathogenesis of breast cancer. This cell cycle-related protein has direct or indirect interactions with long non-coding RNAs (lncRNAs). In the present two-step study, we first identified CCND1-related lncRNAs through an in silico approach. Then, we measured expression of CCND1 mRNA and five lncRNAs in paired breast cancer samples and their matched non-cancerous samples obtained from adjacent tissues. HOTTIP expression was significantly higher in breast cancer tissues compared with adjacent tissues (expression ratio (95% CI)= 4.63 (1.56–13.76), P value= 0.0070). Similarly, CBR3-AS1 was up-regulated in cancerous tissues compared with control tissues (expression ration (95% CI)= 3.26 (1.35–7.86), P value= 0.0122). Expression of HOTTIP was higher in estrogen receptor (ER) negative samples compared with ER positive ones (−4.35 ± 1.33 versus −4.63 ± 0.62, P value=0.002). CBR3-AS1 could differentiate between these two sets of samples with AUC±SD, sensitivity, specificity and P values of 0.7 ± 0.05, 0.9, 0.49 and 0.003, respectively. These values were 0.68 ± 0.04, 0.87, 0.34 and 0.04 for HOTTIP. Although we could not find difference in expression of CCND1 between these two sets of samples, we reported up-regulation of two CCND1-related lncRNAs in breast cancer samples. These lncRNAs are putative markers for breast cancer.

Expression analysis of CDKN2C-related lncRNAs in breast cancer

Cell cycle related genes are implicated in the pathogenesis of breast cancer. In the present study, we aimed at identification of the role of a cell cycle related gene and functionally associated long non-coding RNAs in breast cancer. Thus, we studied expression levels of CDKN2C gene and three related long non-coding RNAs, namely PANDAR, AATBC and PANTR1 in breast cancer samples compared with adjacent tissues. Expression of CDKN2C was significantly lower in breast tumor samples compared with control samples (expression ratio (95% CI) = 0.49 (0.3–0.8), P values = 0.0049). On the other hand, PANTR1 was up-regulated in cancerous samples compared with control samples (expression ratio (95% CI) = 4.25 (1.25–14.38), P values = 0.033). While CDKN2C could separate these sets of samples with AUC ± SD, sensitivity, specificity and P values of 0.75 ± 0.05, 0.68, 0.86 and 0.0002, respectively; PANTR1 could not distinguish between cancerous and non-cancerous samples (P value = 0.6). AUC ± SD, sensitivity and specificity values for PANTR1 were 0.53 ± 0.07, 0.97 and 0.36, respectively. There were significant differences in the correlation patterns of CDKN2C/PANDAR, CDKN2C/AATBC and CDKN2C/PANTR1 pairs between cancerous and non-cancerous tissues. In fact, expression of CDKN2C was correlated with all lncRNAs in cancerous samples, in spite of lack of correlation in non-cancerous tissues. The present study shows possible role of CDKN2C in the pathoetiology of breast cancer.

Abstract 3372: Comprehensive analysis of estrogen-regulated testis specific lncRNAs in breast cancer

Abstract Long non-coding RNAs (lncRNAs) have transcripts longer than 200 nucleotides and recent studies have established they are abnormally expressed in cancer and have the potential to function as either oncogenes or tumor suppressors; Therefore, their potential utility as biomarkers and targets for different types cancer. Breast cancer is the most common cancer diagnosed (excluding skin cancers) among women in the United States, accounting for nearly 1 in 3 cancers. Thus, the need for effective targets and biomarkers to prevent/treat this disease. Available datasets to study gene expression in human diseases are a valuable asset to better understand complex disorders and phenotypes. In this study, we are using three publicly available datasets: The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Cancer Cell Line Encyclopedia (CCLE) to study breast cancer and identify coding and lncRNA transcripts differentially expressed in tumors and highly specific to testis. At the end of our analysis pipeline, we identified a total of 1,668 upregulated genes in breast cancer and 1,163 downregulated genes in breast cancer, from which a total of 31 were lncRNAs. Characterization of the lncRNAs show highly specificity in diverse types of breast cancer (Basal, Luminal A, Luminal B and HER2) compared to protein coding genes. Cell line analysis of breast cancer RNA-Seq samples by the CCLE- Broad Institute show clustering of gene expression for the identified lncRNAs according to molecular subtype. Moreover, categorization of the lncRNAs using TCGA is described by ER- and ER+ status, molecular subtype, comparison between breast tumor, breast normal and GTEx breast, and gene ontology analysis of KEGG Pathways using DAVID. Furthermore, Kaplan-Meier analysis is shown to estimate the potential survival outcome of patients with high/low expression of lncRNAs in breast cancer. Lastly, we compared results from qRT-PCR with our in-silico data for the top lncRNAs and findings show a good correlation between both platforms. Overall, we identify a total of 31 lncRNAs that are estrogen-regulated testis specific in breast cancer by differential expression analysis. In addition, we provide a full genomic characterization of the lncRNAs that show expression according to different molecular subtypes using data from TCGA, GTEx and CCLE. Finally, we show potential survival outcome of high/low expression of identified lncRNAs in breast cancer patients. Taken together, we present lncRNAs specific in breast cancer and high expression in testis, along with their comprehensive characterization of expression signatures. Further studies, are needed to assess their efficacy as potential biomarkers and therapeutic targets in breast cancer. S.S.G. is supported by a grant from the Cancer Prevention and Research Institute of Texas (CPRIT; RR170020). S.S.G. is also supported by a grant from Lizanell and Colbert Coldwell foundation. Citation Format: Enrique I. Ramos, Barbara Yang, Ramesh Choudhari, Melina J. Sedano, Shrikanth S. Gadad. Comprehensive analysis of estrogen-regulated testis specific lncRNAs in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3372.

Amino Acid Metabolism-Related lncRNA Signature Predicts the Prognosis of Breast Cancer.

Background and Purpose: Breast cancer (BRCA) is the most frequent female malignancy and is potentially life threatening. The amino acid metabolism (AAM) has been shown to be strongly associated with the development and progression of human malignancies. In turn, long noncoding RNAs (lncRNAs) exert an important influence on the regulation of metabolism. Therefore, we attempted to build an AAM-related lncRNA prognostic model for BRCA and illustrate its immune characteristics and molecular mechanism. Experimental Design: The RNA-seq data for BRCA from the TCGA-BRCA datasets were stochastically split into training and validation cohorts at a 3:1 ratio, to construct and validate the model, respectively. The amino acid metabolism-related genes were obtained from the Molecular Signature Database. A univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression, and a multivariate Cox analysis were applied to create a predictive risk signature. Subsequently, the immune and molecular characteristics and the benefits of chemotherapeutic drugs in the high-risk and low-risk subgroups were examined. Results: The prognostic model was developed based on the lncRNA group including LIPE-AS1, AC124067.4, LINC01655, AP005131.3, AC015802.3, USP30-AS1, SNHG26, and AL589765.4. Low-risk patients had a more favorable overall survival than did high-risk patients, in accordance with the results obtained for the validation cohort and the complete TCGA cohort. The elaborate results illustrated that a low-risk index was correlated with DNA-repair–associated pathways; a low TP53 and PIK3CA mutation rate; high infiltration of CD4+ T cells, CD8+ T cells, and M1 macrophages; active immunity; and less-aggressive phenotypes. In contrast, a high-risk index was correlated with cancer and metastasis-related pathways; a high PIK3CA and TP53 mutation rate; high infiltration of M0 macrophages, fibroblasts, and M2 macrophages; inhibition of the immune response; and more invasive phenotypes. Conclusion: In conclusion, we attempted to shed light on the importance of AAM-associated lncRNAs in BRCA. The prognostic model built here might be acknowledged as an indispensable reference for predicting the outcome of patients with BRCA and help identify immune and molecular characteristics.