A prognostic 10-lncRNA expression signature for predicting the risk of tumour recurrence in breast cancer patients

Silencing of long noncoding RNA MIAT inhibits the viability and proliferation of breast cancer cells by promoting miR-378a-5p expression

Myocardial infarction–associated transcript (MIAT) is a long noncoding RNA that plays a critical role in a variety of diseases. Accordingly, this study probed into the possible interaction mechanism between MIAT and miR-378a-5p in breast cancer. Concretely, MIAT and miR-378a-5p expressions in breast cancer tissues and cells were measured. After transfection with siMIAT and miR-378a-5p inhibitor, the viability and proliferation of breast cancer cells were examined by cell counting kit-8 and colony formation assays. The expressions of apoptosis-related proteins were detected. According to the results, MIAT was highly expressed in breast cancer tissues and cells. MIAT silencing could decrease Bcl-2 expression, viability, and proliferation of breast cancer cells and increase the expressions of cleaved caspase-3 and Bax. MIAT and miR-378a-5p could directly bind to each other, and MIAT silencing promoted the expression of miR-378a-5p. miR-378a-5p expression was low in breast cancer tissues. The miR-378a-5p inhibitor enhanced the viability and proliferation of breast cancer cells and partially reversed the effects of MIAT silencing on the breast cancer cells. In conclusion, MIAT silencing inhibits the viability and proliferation of breast cancer cells by promoting miR-378a-5p, indicating the potential of MIAT as a new target for the treatment of breast cancer.

Identification of genomic instability related lncRNA signature with prognostic value and its role in cancer immunotherapy in pancreatic cancer

Background: Increasing evidence suggested the critical roles of lncRNAs in the maintenance of genomic stability. However, the identification of genomic instability-related lncRNA signature (GILncSig) and its role in pancreatic cancer (PC) remains largely unexplored.

Methods: In the present study, a systematic analysis of lncRNA expression profiles and somatic mutation profiles was performed in PC patients from The Cancer Genome Atlas (TCGA). We then develop a risk score model to describe the characteristics of the model and verify its prediction accuracy. ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and CIBERSORT analysis were employed to reveal the correlation between tumor immune microenvironment, immune infiltration, immune checkpoint blockade (ICB) therapy, and GILncSig in PC.

Results: We identified 206 GILnc, of which five were screened to develop a prognostic GInLncSig model. Multivariate Cox regression analysis and stratified analysis revealed that the prognostic value of the GILncSig was independent of other clinical variables. Receiver operating characteristic (ROC) analysis suggested that GILncSig is better than the existing lncRNA-related signatures in predicting survival. Additionally, the prognostic performance of the GILncSig was also found to be favorable in patients carrying wild-type KRAS, TP53, and SMAD4. Besides, a nomogram exhibited appreciable reliability for clinical application in predicting the prognosis of patients. Finally, the relationship between the GInLncSig model and the immune landscape in PC reflected its application value in clinical immunotherapy.

Conclusion: In summary, the GILncSig identified by us may serve as novel prognostic biomarkers, and could have a crucial role in immunotherapy decisions for PC patients.

A Potential Nine-lncRNAs Signature Identification and Nomogram Diagnostic Model Establishment for Papillary Thyroid Cancer

The purpose of our current study was to establish a long non-coding RNA(lncRNA) signature and assess its prognostic and diagnostic power in papillary thyroid cancer (PTC). LncRNA expression profiles were obtained from the Cancer Genome Atlas (TCGA). The key module and hub lncRNAs related to PTC were determined by weighted gene co-expression network analysis (WGCNA) and LASSO Cox regression analyses, respectively. Functional enrichment analyses, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis were implemented to analyze the possible biological processes and signaling pathways of hub lncRNAs. Associations between key lncRNA expressions and tumor-infiltrating immune cells were identified using the TIMER website, and proportions of immune cells in high/low risk score groups were compared. Kaplan-Meier Plotter was used to evaluate the prognostic significance of hub genes in PTC. A diagnostic model was conducted with logistic regression analysis, and its diagnostic performance was assessed by calibration/receiver operating characteristic curves and principal component analysis. A nine-lncRNAs signature (SLC12A5-AS1, LINC02028, KIZ-AS1, LINC02019, LINC01877, LINC01444, LINC01176, LINC01290, and LINC00581) was established in PTC, which has significant diagnostic and prognostic power. Functional enrichment analyses elucidated the regulatory mechanism of the nine-lncRNAs signature in the development of PTC. This signature and expressions of nine hub lncRNAs were correlated with the distributions of tumor infiltrating immune cells. A diagnostic nomogram was also established for PTC. By comparing with the published models with less than or equal to nine lncRNAs, our signature showed a preferable performace for prognosis prediction. In conclusion, our present research established an innovative nine-lncRNAs signature and a six-lncRNAs nomogram that might act as a potential indicator for PTC prognosis and diagnosis, which could be conducive to the PTC treatment.

Modular signature of long non-coding RNA association networks as a prognostic biomarker in lung cancer

BACKGROUND

Increasing amount of long non-coding RNAs (lncRNAs) have been found involving in many biological processes and played salient roles in cancers. However, up until recently, functions of most lncRNAs in lung cancer have not been fully discovered, particularly in the co-regulated lncRNAs. Thus, this study aims to investigate roles of lncRNA modules and uncover a module-based biomarker in lung adenocarcinoma (LUAD).

RESULTS

We used gene expression profiles from The Cancer Genome Atlas (TCGA) to construct the lncRNA association networks, from which the highly-associated lncRNAs are connected as modules. It was found that the expression of some modules is significantly associated with patient's survival, including module N1 (HR = 0.62, 95% CI = 0.46-0.84, p = 0.00189); N2 (HR = 0.68, CI = 0.50-0.93, p = 0.00159); N4 (HR = 0.70, CI = 0.52-0.95, p = 0.0205) and P3 (HR = 0.68, CI = 0.50-0.92, p = 0.0123). The lncRNA signature consisting of these four prognosis-related modules, a 4-modular lncRNA signature, is associated with favourable prognosis in TCGA-LUAD (HR = 0.51, CI = 0.37-0.69, p value = 2.00e-05). Afterwards, to assess the performance of the generic modular signature as a prognostic biomarker, we computed the time-dependent area under the receiver operating characteristics (AUC) of this 4-modular lncRNA signature, which showed AUC equals 68.44% on 336th day. In terms of biological functions, these modules are correlated with several cancer hallmarks and pathways, including Myc targets, E2F targets, cell cycle, inflammation/immunity-related pathways, androgen/oestrogen response, KRAS signalling, DNA repair and epithelial-mesenchymal transition (EMT).

CONCLUSION

Taken together, we identified four novel LUAD prognosis-related lncRNA modules, and assessed the performance of the 4-modular lncRNA signature being a prognostic biomarker. Functionally speaking, these modules involve in oncogenic hallmarks as well as pathways. The results unveiled the co-regulated lncRNAs in LUAD and may provide a framework for further lncRNA studies in lung cancer.

Estrogen receptor-positive breast cancer survival prediction and analysis of resistance-related genes introduction

Background

In recent years, ER+ and HER2- breast cancer of adjuvant therapy has made great progress, including chemotherapy and endocrine therapy. We found that the responsiveness of breast cancer treatment was related to the prognosis of patients. However, reliable prognostic signatures based on ER+ and HER2- breast cancer and drug resistance-related prognostic markers have not been well confirmed, This study in amied to establish a drug resistance-related gene signature for risk stratification in ER+ and HER2- breast cancer.

Methods

We used the data from The Cancer Genoma Atlas (TCGA) breast cancer dataset and gene expression database (Gene Expression Omnibus, GEO), constructed a risk profile based on four drug resistance-related genes, and developed a nomogram to predict the survival of patients with I-III ER+ and HER2- breast cancer. At the same time, we analyzed the relationship between immune infiltration and the expression of these four genes or risk groups.

Results

Four drug resistance genes (AMIGO2, LGALS3BP, SCUBE2 and WLS) were found to be promising tools for ER+ and HER2- breast cancer risk stratification. Then, the nomogram, which combines genetic characteristics with known risk factors, produced better performance and net benefits in calibration and decision curve analysis. Similar results were validated in three separate GEO cohorts. All of these results showed that the model can be used as a prognostic classifier for clinical decision-making, individual prediction and treatment, as well as follow-up.

Predicting the immune landscape of invasive breast carcinoma based on the novel signature of immune-related lncRNA

Background

The composition of the population of immune‐related long non‐coding ribonucleic acid (irlncRNA) generates a signature, irrespective of expression level, with potential value in predicting the survival status of patients with invasive breast carcinoma.

Methods

The current study uses univariate analysis to identify differentially expressed irlncRNA (DEirlncRNA) pairs from RNA‐Seq data from The Cancer Genome Atlas (TCGA). 36 pairs of DEirlncRNA pairs were identified. Using various algorithms to construct a model, we have compared the area under the curve and calculated the 5‐year curve of Akaike information criterion (AIC) values, which allows determination of the threshold indicating the maximum value for differentiation. Through cut‐off point to establish the optimal model for distinguishing high‐risk or low‐risk groups among breast cancer patients. We assigned individual patients with invasive breast cancer to either high risk or low risk groups depending on the cut‐off point, re‐evaluated the tumor immune cell infiltration, the effectiveness of chemotherapy, immunosuppressive biomarkers, and immunotherapy.

Results

After re‐assessing patients according to the threshold, we demonstrated an effective means of distinguish the severity of the disease, and identified patients with different clinicopathological characteristics, specific tumor immune infiltration states, high sensitivity to chemotherapy,wellpredicted response to immunotherapy and thus a more favorable survival outcome.

Conclusions

The current study presents novel findings regarding the use of irlncRNA without the need to predict precise expression levels in the prognosis of breast cancer patients and to indicate their suitability for anti‐tumor immunotherapy.

Identification and Roles of miR-29b-1-3p and miR29a-3p-Regulated and Non-Regulated lncRNAs in Endocrine-Sensitive and Resistant Breast Cancer Cells

Despite improvements in the treatment of endocrine-resistant metastatic disease using combination therapies in patients with estrogen receptor α (ERα) primary tumors, the mechanisms underlying endocrine resistance remain to be elucidated. Non-coding RNAs (ncRNAs), including microRNAs (miRNA) and long non-coding RNAs (lncRNA), are targets and regulators of cell signaling pathways and their exosomal transport may contribute to metastasis. Previous studies have shown that a low expression of miR-29a-3p and miR-29b-3p is associated with lower overall breast cancer survival before 150 mos. Transient, modest overexpression of miR-29b1-3p or miR-29a-3p inhibited MCF-7 tamoxifen-sensitive and LCC9 tamoxifen-resistant cell proliferation. Here, we identify miR-29b-1/a-regulated and non-regulated differentially expressed lncRNAs in MCF-7 and LCC9 cells using next-generation RNA seq. More lncRNAs were miR-29b-1/a-regulated in LCC9 cells than in MCF-7 cells, including DANCR, GAS5, DSCAM-AS1, SNHG5, and CRND. We examined the roles of miR-29-regulated and differentially expressed lncRNAs in endocrine-resistant breast cancer, including putative and proven targets and expression patterns in survival analysis using the KM Plotter and TCGA databases. This study provides new insights into lncRNAs in endocrine-resistant breast cancer.

A panel of 8-lncRNA predicts prognosis of breast cancer patients and migration of breast cancer cells

BACKGROUND

Breast cancer (BCa) is the most commonly diagnosed cancer and the leading cause of cancer death among females around the world. Recent studies have indicated that long non-coding RNAs (lncRNAs) can serve as an independent biomarker for diagnosis and prognosis in many types of cancer, including pancreatic adenocarcinoma, gastric cancer, liver cancer, and lung cancer. Previous studies have shown that many lncRNAs are associated with the occurrence and development of BCa. However, few studies have combined multiple lncRNAs to predict the prognosis of early-stage BCa patients.

METHODS

Systematic and comprehensive analysis of data from The Cancer Genome Atlas (TCGA) was conducted to identify lncRNA signatures with prognostic value in BCa. Additionally, the relative expression levels of the 8 lncRNA of several BCa cell lines were detected by quantitative real-time PCR (qPCR) and the results were substituted into a risk score formula. Finally, migration assays were used to verify the result from prognostic analysis according to the risk scores among cell lines with different risk scores.

RESULTS

Our study included 808 BCa patients with complete clinical data. A panel of 8 lncRNAs was identified using Wilcox tests as different between normal and tumor tissue of the BCa patients. This panel was used to analyze the survival of BCa patients. Patients with low risk scores had greater overall survival (OS) than those with high risk scores. Multivariate Cox regression analyses demonstrated that the lncRNA signature was an independent prognostic factor. Gene Set Enrichment Analysis (GSEA) suggested that the lncRNAs might be involved in several molecular signaling pathways implicated in BCa such as the DNA replication pathway, the cell cycle pathway, and the pentose phosphate pathway. Validation experiments in breast cancer cells to test cell migration by using wound-healing assays supported the results of the model.

CONCLUSION

Our study demonstrated that a panel of 8 lncRNAs has the potential to be used as an independent prognostic biomarker of BCa.

Identification and Validation of a Prognostic 5-Protein Signature for Biochemical Recurrence Following Radical Prostatectomy for Prostate Cancer

Background: Biochemical recurrence (BCR) is an indicator of prostate cancer (PCa)-specific recurrence and mortality. However, there is a lack of an effective prediction model that can be used to predict prognosis and to determine the optimal method of treatment for patients with BCR. Hence, the aim of this study was to construct a protein-based nomogram that could predict BCR in PCa.

Methods: Protein expression data of PCa patients was obtained from The Cancer Proteome Atlas (TCPA) database. Clinical data on the patients was downloaded from The Cancer Genome Atlas (TCGA) database. Lasso and Cox regression analyses were conducted to select the most significant prognostic proteins and formulate a protein signature that could predict BCR. Subsequently, Kaplan–Meier survival analysis and Cox regression analyses were conducted to evaluate the performance of the prognostic protein-based signature. Additionally, a nomogram was constructed using multivariate Cox regression analysis.

Results: We constructed a 5-protein-based prognostic prediction signature that could be used to identify high-risk and low-risk groups of PCa patients. The survival analysis demonstrated that patients with a higher BCR showed significantly worse survival than those with a lower BCR (p < 0.0001). The time-dependent receiver operating characteristic curve showed that the signature had an excellent prognostic efficiency for 1, 3, and 5-year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariate and multivariate analyses indicated that this 5-protein signature could be used as independent prognosis marker for PCa patients. Moreover, the concordance index (C-index) confirmed the predictive value of this 5-protein signature in 3, 5, and 10-year BCR overall survival (C-index: 0.764, 95% confidence interval: 0.701–0.827). Finally, we constructed a nomogram to predict BCR of PCa.

Conclusions: Our study identified a 5-protein-based signature and constructed a nomogram that could reliably predict BCR. The findings might be of paramount importance for the prediction of PCa prognosis and medical decision-making.

Subjects: Bioinformatics, oncology, urology.

Extracellular Matrix-Related Six-lncRNA Signature as a Novel Prognostic Biomarker for Bladder Cancer

Introduction

Bladder cancer (BC) is the fourth-commones cancer and the sixth-leading cause of cancer-related death among men. However, a lack of reliable biomarkers remains a problem forprognosis and treatment of BC. lncRNAs have been shown to play important roles in various cancers, and have emerged as promising biomarkers for cancer prognosis and treatment.

Methods

In this study, using univariate and multivariate Cox regression analysis, we examined the differential expression profiles of 1,651 lncRNAs in the TCGA BLCA cohort and created a prognostic gene signature composed of six lncRNAs (for SNHG12, MAFG-DT, ASMTL-AS1, LINC02321, LINC01322, and LINC00922), designed the SMALLL signature.

Results

The SMALLL signature displayed significant prognostic power for overall survival for BC patients in multiple cohorts. Gene Ontology analysis showed that genes coexpressed with the SMALLL signature were associated with the extracellular matrix network, and immune cell–infiltration analysis showed that activated naïve B cells, regulatory T cells, M0 macrophages, eosinophils, resting memory CD4 T cells and resting NK cells were significantly different in high- and low-risk groups. We also confirmed differential expression of the lncRNAs of the SMALLL signature in BC tissue and paracancer normal tissue by qRT-PCR analysis. Cell-invasion and -migration experiments showed that MAFG-AS1, ASMTL-AS1, LINC02321, and LINC00922 significantly affected cell invasion and migration.

Conclusion

Our study revealed that the lncRNA signature is an important predictive factor of prognosis and provides a promising biomarker for BC.

Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature

Background

Accumulating evidence has demonstrated that immune-related lncRNAs (IRLs) are commonly aberrantly expressed in breast cancer (BC). Thus, we aimed to establish an IRL-based tool to improve prognosis prediction in BC patients.

Methods

We obtained IRL expression profiles in large BC cohorts (N = 911) from The Cancer Genome Atlas (TCGA) database. Then, in light of the correlation between each IRL and recurrence-free survival (RFS), we screened prognostic IRL signatures to construct a novel RFS nomogram via a Cox regression model. Subsequently, the performance of the IRL-based model was evaluated through discrimination, calibration ability, risk stratification ability and decision curve analysis (DCA).

Results

A total of 52 IRLs were obtained from TCGA. Based on multivariate Cox regression analyses, four IRLs (A1BG-AS1, AC004477.3, AC004585.1 and AC004854.2) and two risk parameters (tumor subtype and TNM stage) were utilized as independent indicators to develop a novel prognostic model. In terms of predictive accuracy, the IRL-based model was distinctly superior to the TNM staging system (AUC: 0.728 VS 0.673, P = 0.010). DCA indicated that our nomogram had favorable clinical practicability. In addition, risk stratification analysis showed that the IRL-based tool efficiently divided BC patients into high- and low-risk groups (P < 0.001).

Conclusions

A novel IRL-based model was constructed to predict the risk of 5-year RFS in BC. Our model can improve the predictive power of the TNM staging system and identify high-risk patients with tumor recurrence to implement more appropriate treatment strategies.

Upregulated N6-Methyladenosine RNA in Peripheral Blood: Potential Diagnostic Biomarker for Breast Cancer

Purpose

An effective biomarker for the diagnosis of breast cancer (BC) and benign breast diseases (BBD) is crucial for improving the prognosis. We investigated whether N6-methyladenosine (m⁶A) can be a diagnostic biomarker of BC.

Materials and Methods

We detected the contents of peripheral blood m⁶A in 62 patients with BC, 41 patients with BBD, and 41 normal controls (NCs) using the colorimetric method. The relative expression of the m⁶A regulated genes methyltransferase-like 14 (METTL14) and fat mass and obesity-associated (FTO) was analyzed using quantitative real-time polymerase chain reaction.

Results

m⁶A in peripheral blood RNA was significantly higher in patients with BC than that in patients with BBD (p < 0.001) or the NCs (p < 0.001). m⁶A was closely associated with the disease stage (from stage 0 to stage I-IV, p=0.003). The receiver operating characteristic curve of m⁶A contained an area under the curve (AUC) value of 0.887 in BC, which was greater than that of carcinoembryonic antigen (CEA) or carbohydrate antigen 153 (CA153). The combination of m⁶A, CEA, and CA153 improved the AUC to 0.914. The upregulated and downregulated mRNA expression of METTL14 and FTO, respectively, might contribute to the increase of m⁶A in patients with BC. m⁶A combined with METTL14 and FTO improved the AUC to 0.929 with a specificity of 97.4% in the peripheral blood of patients with BC.

Conclusion

The peripheral blood RNA of m⁶A might be a valuable biomarker for the diagnosis of BC.

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

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

Identification of Candidate Genes Associated with Breast Cancer Prognosis

Breast cancer (BC) is the most malignant tumor in women. The molecular mechanisms underlying tumorigenesis still need to be further elucidated. It is necessary to investigate novel candidate genes involved in breast cancer progression and prognosis. In this study, we commit to explore candidate genes that associate with prognosis and therapy in BC by a comprehensive bioinformatic analysis. Four GEO datasets (GSE5764, GSE7904, GSE20711, and GSE29431) and the BC-related transcriptome data in TCGA database were downloaded and used to identify the differently expressed genes (DEGs). The function of DEGs was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis. The protein-protein interaction (PPI) network of DEGs was constructed to identify hub genes. Prognostic candidate genes were identified through survival analysis. In addition, potential therapeutic targets were identified by constructed gene-drug interaction network through Comparative Toxicogenomics Database. A total of 547 DEGs (302 up and 245 down) were identified. Three core-subnetwork and 25 hub genes were identified in PPI network. Seven genes (namely COL12A1, QPRT, MRPL13, KRT14, KRT15, LAMB3, and MYBPC1) were identified as crucial prognostic candidate genes, which significantly associated with breast cancer overall survival. Furthermore, two representative candidate genes (COL12A1 and LAMB3) were optionally chosen for verification by reverse transcription and quantitative real-time polymerase chain reaction (RT-PCR). What's more, the gene-drugs interaction analysis indicates several antitumor drugs that could affect the expression of these prognostic markers, such as doxorubicin, cisplatin, and tamoxifen. These results identified seven crucial candidate genes that may serve as prognosis biomarkers and novel therapeutic targets of breast cancer, which may facilitate further understanding the molecular pathogenesis and providing potential therapeutic strategies for BC.

The Missing Lnc: The Potential of Targeting Triple-Negative Breast Cancer and Cancer Stem Cells by Inhibiting Long Non-Coding RNAs

Treatment decisions for breast cancer are based on staging and hormone receptor expression and include chemotherapies and endocrine therapy. While effective in many cases, some breast cancers are resistant to therapy, metastasize and recur, leading to eventual death. Higher percentages of tumor-initiating cancer stem cells (CSCs) may contribute to the increased aggressiveness, chemoresistance, and worse outcomes among breast cancer. This may be particularly true in triple-negative breast cancers (TNBCs) which have higher percentages of CSCs and are associated with worse outcomes. In recent years, increasing numbers of long non-coding RNAs (lncRNAs) have been identified as playing an important role in breast cancer progression and some of these have been specifically associated within the CSC populations of breast cancers. LncRNAs are non-protein-coding transcripts greater than 200 nucleotides which can have critical functions in gene expression regulation. The preclinical evidence regarding lncRNA antagonists for the treatment of cancer is promising and therefore, presents a potential novel approach for treating breast cancer and targeting therapy-resistant CSCs within these tumors. Herein, we summarize the lncRNAs that have been identified as functionally relevant in breast CSCs. Furthermore, our review of the literature and analysis of patient datasets has revealed that many of these breast CSC-associated lncRNAs are also enriched in TNBC. Together, this suggests that these lncRNAs may be playing a particularly important role in TNBC. Thus, certain breast cancer-promoting/CSC-associated lncRNAs could be targeted in the treatment of TNBCs and the CSCs within these tumors should be susceptible to anti-lncRNA therapy.

Diagnostic value of seven biomarkers for breast cancer: an overview with evidence mapping and indirect comparisons of diagnostic test accuracy

Several meta-analyses have evaluated the value of biomarkers in diagnosing breast cancer, but which biomarker has the optimal diagnostic value remains unclear. This overview aimed to compare the accuracy of different biomarkers in diagnosing breast cancer. PubMed, Embase.com, the Cochrane Library of Systematic Reviews, and Web of Science were searched. The assessment of multiple systematic reviews-2 (AMSTAR-2) was used to assess the methodological quality and preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy (PRISMA-DTA) for reporting quality. Pairwise meta-analyses were performed to estimate the pooled results for each biomarker, and indirect comparisons were conducted to compare diagnostic accuracy between biomarkers. Eleven systematic reviews (SRs) involving 218 original studies were included. All SRs were of critically low methodological quality, 3 SRs had minimal reporting flaws and 8 SRs had minor flaws. The pooled sensitivity and specificity were 0.77 and 0.87 for miRNA, 0.70 and 0.87 for circulating cell-free DNA, 0.29 and 0.96 for APC gene promoter methylation, 0.69 and 0.99 for 14-3-3σ promoter methylation, 0.63 and 0.82 for CA153, 0.58 and 0.87 for CEA, and 0.73 and 0.56 for PSA. Compared with CA153 and PSA, miRNA had a higher sensitivity and specificity. The sensitivity of miRNA was higher than circulating cell-free DNA and CEA, although they had the same specificities. APC gene promoter methylation and 14-3-3σ promoter methylation were more specific than miRNA, but they had unacceptably low sensitivity. In conclusion, miRNA had better diagnostic accuracy than the other six biomarkers. But due to the low quality of included SRs, the results need to be interpreted with caution. Further study should investigate the diagnostic accuracy of different biomarkers in direct comparisons and focus on the value of combined biomarkers.

A prognostic 10-lncRNA expression signature for predicting the risk of tumour recurrence in breast cancer patients

Breast cancer is one of the most frequently diagnosed malignancies and a leading cause of cancer death among females. Multiple molecular alterations are observed in breast cancer. LncRNA transcripts were proved to play important roles in the biology of tumorigenesis. In this study, we aimed to identify lncRNA expression signature that can predict breast cancer patient survival. We developed a 10‐lncRNA signature‐based risk score which was used to separate patients into high‐risk and low‐risk groups. Patients in the low‐risk group had significantly better survival than those in the high‐risk group. Receiver operating characteristic analysis indicated that this signature exhibited excellent diagnostic efficiency for 1‐, 3‐ and 5‐year disease‐relapse events. Moreover, multivariate Cox regression analysis demonstrated that this 10‐lncRNA signature was an independent risk factor when adjusting for several clinical signatures such as age, tumour size and lymph node status. The prognostic value of risk scores was validated in the validation set. In addition, a nomogram was established and the calibration plots analysis indicated the good performance and clinical utility of the nomogram. In conclusion, our results demonstrated that this 10‐lncRNA signature effectively grouped patients at low and high risk of disease recurrence.