These authors contributed equally to this work. BACKGROUND: Long non-coding RNA (lncRNA) H19 has been well studied playing an important role in breast cancer (BC) progress and the expression of H19 may service as a diagnostic target for BC. However,

Background: It is well known that HOX transcript antisense intergenic ribonucleic acid (HOTAIR) plays an important role in breast cancer (BC). However, whether circulating HOTAIR in plasma could be used for BC diagnosis and dynamic monitoring are unclear.Methods: We tested the expression levels of HOTAIR in 30 pairs of tissue samples and 148 plasma samples from BC patients by quantitative real time‐polymerase chain reaction, and the correlation between plasma HOTAIR levels and clinical features were analyzed. Receiver operating characteristic curve (ROC) was used to assess the diagnostic power of plasma HOTAIR for BC. Furthermore, we explored the monitoring values of plasma HOTAIR for BC and analyzed the correlation of HOTAIR levels between plasma and corresponding tissues of the same patients.Results: The expression levels of HOTAIR were significantly higher in BC tissues and plasma than in the control ( P < 0.05). The expression levels of plasma HOTAIR were correlated with lymph node metastasis ( P = 0.018), estrogen receptor (ER) ( P = 0.012), c‐erbB‐2 (P = 0.006) and triple positive ( P = 0.015). The area under the ROC curve of plasma HOTAIR was 0.80 (sensitivity 69.2%; specificity 93.3%), which was higher than the carcinoembryonic antigen and carbohydrate antigen 15‐3 values obtained. Moreover, plasma HOTAIR expression levels in postoperative patients were lower than those in preoperative patients ( P = 0.029) and were moderately correlated with the corresponding tissue levels of the same patients (r = 0.68, P < 0.0001).Conclusion: These results indicated that HOTAIR may be a potential biomarker for the diagnosis of BC.

Breast cancer (BC) is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways involved in BC tumorigenesis and progression. The present study aimed to elucidate potential key candidate genes and pathways in BC. Five gene expression profile data sets (GSE22035, GSE3744, GSE5764, GSE21422 and GSE26910) were downloaded from the Gene Expression Omnibus (GEO) database, which included data from 113 tumorous and 38 adjacent non‑tumorous tissue samples. Differentially expressed genes (DEGs) were identified using t‑tests in the limma R package. These DEGs were subsequently investigated by pathway enrichment analysis and a protein‑protein interaction (PPI) network was constructed. The most significant module from the PPI network was selected for pathway enrichment analysis. In total, 227 DEGs were identified, of which 82 were upregulated and 145 were downregulated. Pathway enrichment analysis results revealed that the upregulated DEGs were mainly enriched in ʻcell divisionʼ, the ʻproteinaceous extracellular matrix (ECM)ʼ, ʻECM structural constituentsʼ and ʻECM‑receptor interactionʼ, whereas downregulated genes were mainly enriched in ʻresponse to drugsʼ, ʻextracellular spaceʼ, ʻtranscriptional activator activityʼ and the ʻperoxisome proliferator‑activated receptor signaling pathwayʼ. The PPI network contained 174 nodes and 1,257 edges. DNA topoisomerase 2‑a, baculoviral inhibitor of apoptosis repeat‑containing protein 5, cyclin‑dependent kinase 1, G2/mitotic‑specific cyclin‑B1 and kinetochore protein NDC80 homolog were identified as the top 5 hub genes. Furthermore, the genes in the most significant module were predominantly involved in ʻmitotic nuclear divisionʼ, ʻmid‑bodyʼ, ʻprotein bindingʼ and ʻcell cycleʼ. In conclusion, the DEGs, relative pathways and hub genes identified in the present study may aid in understanding of the molecular mechanisms underlying BC progression and provide potential molecular targets and biomarkers for BC.

Background: Disulfidptosis is a newly identified variant of cell death characterized by disulfide accumulation, which is independent of ATP depletion. Accordingly, the latent influence of disulfidptosis on the prognosis of lung adenocarcinoma (LUAD) patients and the progression of tumors remains poorly understood.Methods: We conducted a multifaceted analysis of the transcriptional and genetic modifications in disulfidptosis regulators (DRs) specific to LUAD, followed by an evaluation of their expression configurations to define DR clusters. Harnessing the differentially expressed genes (DEGs) identified from these clusters, we formulated an optimal predictive model by amalgamating 10 distinct machine learning algorithms across 101 unique combinations to compute the disulfidptosis score (DS). Patients were subsequently stratified into high and low DS cohorts based on median DS values. We then performed an exhaustive comparison between these cohorts, focusing on somatic mutations, clinical attributes, tumor microenvironment, and treatment responsiveness. Finally, we empirically validated the biological implications of a critical gene, KYNU, through assays in LUAD cell lines.Results: We identified two DR clusters and there were great differences in overall survival (OS) and tumor microenvironment. We selected the "Least Absolute Shrinkage and Selection Operator (LASSO) + Random Survival Forest (RFS)" algorithm to develop a DS based on the average C-index across different cohorts. Our model effectively stratified LUAD patients into high- and low-DS subgroups, with this latter demonstrating superior OS, a reduced mutational landscape, enhanced immune status, and increased sensitivity to immunotherapy. Notably, the predictive accuracy of DS outperformed the published LUAD signature and clinical features. Finally, we validated the DS expression using clinical samples and found that inhibiting KYNU suppressed LUAD cells proliferation, invasiveness, and migration in vitro.Conclusions: The DR-based scoring system that we developed enabled accurate prognostic stratification of LUAD patients and provides important insights into the molecular mechanisms and treatment strategies for LUAD.

Background: Previous studies have indicated that large intergenic non‐coding RNA regulator of reprogramming (lincRNA‐ROR) plays an important role in regulating tumor carcinogenesis and metastasis; however, whether circulating lincRNA‐ROR could function as a potential biomarker for breast cancer (BC) diagnosis and monitoring is unknown. This study was conducted to investigate circulating lincRNA‐ROR in plasma as a potential biomarker for BC diagnosis and monitoring.Methods: We performed reverse transcription‐quantitative‐PCR to examine lincRNA‐ROR expression levels in cell lines, 24 pairs of BC tissue samples, and 94 plasma samples from BC patients. Potential correlations between plasma lincRNA‐ROR levels and clinicopathological characteristics were analyzed. A receiver operating characteristic curve was calculated to evaluate the diagnostic values for BC. Pearson correlation analysis of lincRNA‐ROR in plasma samples and the corresponding tissues of the same patients was performed to explore tumor monitoring values.Results: LincRNA‐ROR expression was significantly increased in BC cell lines, tissues, and plasma (all P < 0.01). Plasma lincRNA‐ROR levels were associated with estrogen receptors (P = 0.042) and lymph node metastasis (P = 0.046). The area under the receiver operating characteristic curve of plasma lincRNA‐ROR was 0.844 (sensitivity 80.0%, specificity 56.7%), which was higher than carcinoembryonic and carbohydrate antigen 15‐3 values. Moreover, plasma lincRNA‐ROR levels were decreased in postoperative compared to preoperative samples (P < 0.0001). Plasma lincRNA‐ROR levels moderately correlated with the corresponding tissue level in the same patients (r² = 0.638, P < 0.0001).Conclusion: Plasma lincRNA‐ROR may be a potential biomarker for BC diagnosis and a dynamic monitor.

Alternative polyadenylation (APA) is a post-translational modification that occurs during mRNA maturation in humans. Studies suggested that abnormal APA events are associated with the genesis and progression of malignant tumors. Here, we aimed to comprehensively evaluate the prognostic value of APA events involved in breast cancer (BC). Both APA events and clinical information for BC patients were downloaded from The Cancer Genome Atlas (TCGA) database to identify prognosis-related APA events in BC. A total of 462 APA events and 374 APA events were shown to be significantly related to overall survival (OS) and relapse-free survival (RFS), respectively, of BC patients. The TCGA set was randomly divided into a training and a test set. Key prognosis-related APA events were selected by LASSO regression to build prediction signatures for OS and RFS by multivariate Cox regression analysis in the training, test, and whole set. BC patients were stratified into high-risk and low-risk groups based on median risk scores. Kaplan–Meier survival analysis demonstrated that low-risk groups had better OS and RFS than high-risk groups in all three sets. The time-dependent receiver operating characteristic (ROC) curves showed that our signatures had a good predictive ability for survival and recurrence for BC patients in all three sets. The independent prognostic indicators-based nomogram model had excellent performance and considerable net benefit for predicting the OS and RFS in BC. A PPI network was constructed between key prognosis and core regulators associated with APA, consisting of 48 nodes and 244 edges. Functional enrichment analysis also revealed their association with RNA processing and RNA synthesis. Collectively, our data indicate that prognostic signatures based on APA events may be powerful prognostic predictors for OS and RFS in BC.