Individualized Prediction of Survival by a 10-Long Non-coding RNA-Based Prognostic Model for Patients With Breast Cancer

CCIVR2 facilitates comprehensive identification of both overlapping and non-overlapping antisense transcripts within specified regions

Pairs of sense and antisense transcriptions that are adjacent at their 5' and 3' regions are called divergent and convergent transcription, respectively. However, the structural properties of divergent/convergent transcription in different species or RNA biotypes are poorly characterized. Here, we developed CCIVR2, a program that facilitates identification of both overlapping and non-overlapping antisense transcripts produced from divergent/convergent transcription whose transcription start sites (TSS) or transcript end sites (TES) are located within a specified region. We used CCIVR2 to analyze antisense transcripts starting around the sense TSS (from divergent transcription) or ending around the sense TES (from convergent transcription) in 11 different species and found species- and RNA biotype-specific features of divergent/convergent transcription. Furthermore, we confirmed that CCIVR2 enables the identification of multiple sense/antisense transcript pairs from divergent transcription, including those with known functions in processes such as embryonic stem cell differentiation and TGFβ stimulation. CCIVR2 is therefore a valuable bioinformatics tool that facilitates the characterization of divergent/convergent transcription in different species and aids the identification of functional sense/antisense transcript pairs from divergent transcription in specified biological processes.

LncRNA CASC9 activated by STAT3 promotes the invasion of breast cancer and the formation of lymphatic vessels by enhancing H3K27ac-activated SOX4

Numerous long noncoding RNAs (lncRNAs) are abnormally expressed in breast cancer (BC), but the underlying mechanisms remain large unknown. Here, we aimed to investigate the functions and mechanisms of lncRNA cancer susceptibility candidate 9 (CASC9) in BC. Western blotting and quantitative real-time PCR (qRT-PCR) were performed to assess gene and protein expression, respectively. The proliferative and metastatic abilities of BC cells were tested by cell counting kit-8 and transwell assays, respectively. The formation of lymphatic vessels was detected by tube formation assay. Chromatin immunoprecipitation (ChIP) and dual luciferase reporter assays were performed to verify molecular interactions. CASC9 was found to be highly expressed in BC tissues and cell lines, and ectopic overexpression was positively associated with tumor volume, TNM stage, and lymph node metastasis. In addition, CASC9 silencing significantly inhibited the proliferation and invasion of BC cells, as well as BC-associated invasion and formation of lymphatic vessels of human dermal lymphatic endothelial cells. Mechanical studies demonstrated that CASC9 could be transcriptionally activated by STAT3 and elevate SOX4 expression by enhancing the acetylation of its promoter region. Our results illustrated that STAT3-activated CASC9 served as a tumor-promoting gene involved in promoting BC invasion and BC-associated formation of lymphatic vessels by upregulating SOX4 through altering H3K27ac level. This finding elucidated a new underlying network of CASC9 in the metastasis of BC.

Integrated Multi-Omics Analysis Model to Identify Biomarkers Associated With Prognosis of Breast Cancer

Background

With the rapid development and wide application of high-throughput sequencing technology, biomedical research has entered the era of large-scale omics data. We aim to identify genes associated with breast cancer prognosis by integrating multi-omics data.

Method

Gene-gene interactions were taken into account, and we applied two differential network methods JDINAC and LGCDG to identify differential genes. The patients were divided into case and control groups according to their survival time. The TCGA and METABRIC database were used as the training and validation set respectively.

Result

In the TCGA dataset, C11orf1, OLA1, RPL31, SPDL1 and IL33 were identified to be associated with prognosis of breast cancer. In the METABRIC database, ZNF273, ZBTB37, TRIM52, TSGA10, ZNF727, TRAF2, TSPAN17, USP28 and ZNF519 were identified as hub genes. In addition, RPL31, TMEM163 and ZNF273 were screened out in both datasets. GO enrichment analysis shows that most of these hub genes were involved in zinc ion binding.

Conclusion

In this study, a total of 15 hub genes associated with long-term survival of breast cancer were identified, which can promote understanding of the molecular mechanism of breast cancer and provide new insight into clinical research and treatment.

Development and validation of a nomogram to predict survival for advanced male breast cancer

Male breast cancer is a rare disease. Many experiences of male breast cancer were derived of female breast cancer. However, there are huge differences between two groups. We conducted this study to find a reliable prognostic model for advanced male breast cancer. The cohort was selected from the Surveillance, Epidemiology, and End Results database. The enrolled patients were randomly divided into training and validation group. The univariate and multivariate analyses were used for prognostic assessment and a nomogram was built. Calibration curves and concordance index were compiled to determine predictive and discriminatory capacity. The time-dependent receiver operating curves and the decision curve analysis was used to verify the model's ability. Two hundred and eighty individuals were enrolled. The cumulative rates of 1-, 3- and 5-year overall survival (OS) rates were 98.6%, 72% and 57.9%. The C-indexes for OS were 0.835 (95%CI, 0.777-0.893) in the training group and 0.765 (95%CI, 0.668-0.862) in the validation group. The calibration curves confirmed the consistency of the nomogram both in the training and validation group. The time-dependent receiver operating curves and decision curve analysis demonstrated that the nomogram had better prediction capacity than TNM stage system for advanced male breast cancer. The nomogram we built was a reliable and solid predictive model.