Long non‐coding RNAs (lncRNAs), which competitively bind miRNAs to regulate target mRNA expression in the competing endogenous RNAs (ceRNAs) network, have attracted increasing attention in breast cancer research. We aim to find more effective therapeutic targets and prognostic markers for breast cancer. LncRNA, mRNA and miRNA expression profiles of breast cancer were downloaded from TCGA database. We screened the top 5000 lncRNAs, top 5000 mRNAs and all miRNAs to perform weighted gene co‐expression network analysis. The correlation between modules and clinical information of breast cancer was identified by Pearson's correlation coefficient. Based on the most relevant modules, we constructed a ceRNA network of breast cancer. Additionally, the standard Kaplan‐Meier univariate curve analysis was adopted to identify the prognosis of lncRNAs. Ultimately, a total of 23 and 5 modules were generated in the lncRNAs/mRNAs and miRNAs co‐expression network, respectively. According to the Green module of lncRNAs/mRNAs and Blue module of miRNAs, our constructed ceRNA network consisted of 52 lncRNAs, 17miRNAs and 79 mRNAs. Through survival analysis, 5 lncRNAs (AL117190.1, COL4A2‐AS1, LINC00184, MEG3 and MIR22HG) were identified as crucial prognostic factors for patients with breast cancer. Taken together, we have identified five novel lncRNAs related to prognosis of breast cancer. Our study has contributed to the deeper understanding of the molecular mechanism of breast cancer and provided novel insights into the use of breast cancer drugs and prognosis.

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Advances in cancer biology have allowed early diagnosis and more comprehensive treatment of breast cancer (BC). However, it remains the most common cause of cancer death in women worldwide because of its strong invasiveness and metastasis. In‐depth study of the molecular pathogenesis of BC and of relevant prognostic markers would improve the quality of life and prognosis of patients. In this study, bioinformatics analysis of SNP‐related data from BC patients provided in the TCGA database revealed that six mutant genes ( NCOR1, GATA3, CDH1, ATM, AKT1, and PTEN ) were significantly associated with the corresponding expression levels of the proteins. The proteins were involved in multiple pathways related to the development of cancer, including the PI3K‐Akt signaling pathway, pertinent microRNAs, and the MAPK signaling pathway. In addition, overall survival and recurrence‐free survival analysis revealed the close associations of the expression of GATA3 , NCOR1 , CDH1, and ATM with survival of BC patients. Therefore, detecting these gene mutations and exploring their corresponding expression could be valuable in predicting the prognosis of patients. The results of the high‐throughput data mining provide important fundamental bioinformatics information and a relevant theoretical basis for further exploring the molecular pathogenesis of BC and assessing the prognosis of patients.

Background With the development in research, the importance of microRNAs (miRNAs) in the occurrence and metastasis, and prognosis of lung squamous cell carcinoma (LUSC) have received extensive attention. This study aims to identify new biomarkers and pivotal genes associated with LUSC prognosis through bioinformatics. Materials and methods We downloaded miRNA and messenger RNA samples related to LUSC from The Cancer Genome Atlas (TCGA) database. Following initial data screening and preprocessing, we utilized the R platform and a range of analysis tools (miRDB, TargetScanHuman7.2, DAVID, and Cytoscape_v3.5.1) to analyze the TCGA data and identify highly specific and sensitive biomarkers. Results We finally identified 12 miRNAs and six central genes closely related to the overall survival of patients with LUSC. We found that high expression of 7 miRNAs (miR‐301b, miR‐383, miR‐512, miR‐515, miR‐525, miR‐577, and miR‐5682) and low expression of five miRNAs (miR‐448, miR‐486, miR‐4732,miR‐4732, miR‐516, and miR‐1911) can significantly improve the overall survival rate of patients with LUSC. The six central genes DLGAP5, CENPA, CHEK1, LRRK2, CALB1, and TOP2A are directly or indirectly involved in the formation and metastasis of LUSC and could, therefore, be considered as target genes for drug therapy. Conclusion With the development in research, the importance of miRNAs in the occurrence and metastasis and prognosis of LUSC have received extensive attention. This study aims to identify new biomarkers and pivotal genes associated with LUSC prognosis through bioinformatics.

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This meta-analysis suggested that more pro-inflammatory diets (higher DII scores) are associated with increased breast cancer incidence. However, the research is not about significant associations but about moderate effect sizes.

Background: Breast cancer is one of the deadliest malignant tumors worldwide. Due to its complex molecular and cellular heterogeneity, the efficacy of existing breast cancer risk prediction models is unsatisfactory. In this study, we developed a new lncRNA model to predict the prognosis of patients with BRCA.Methods: BRCA-related differentially-expressed long non-coding RNA were screened from the Cancer Genome Atlas database. A novel lncRNA model was developed by univariate and multivariate analyses to predict the prognosis of patients with BRCA. The efficacy of the model was verified by TCGA-based breast cancer samples. Identified lncRNA-related mRNA based on the co-expression method.Results: We constructed a 7-lncRNA breast cancer prediction model including LINC00377, LINC00536, LINC01224, LINC00668, LINC01234, LINC02037, and LINC01456. The breast cancer samples were divided into high-risk and low-risk groups based on the model, which verified the specificity and sensitivity of the model. The Area Under Curve (AUC) of the 3- and 5-year Receiver Operating Characteristic curve were 0.711 and 0.734, respectively, indicating that the model has good performance.Conclusion: We constructed a 7-lncRNA model to predict the prognosis of patients with BRCA, and suggest that these lncRNAs may play a specific role in the carcinogenesis of BRCA.

In recent years, the emergence of immunotherapy has provided a new perspective for the treatment and management of triple-negative breast cancer (TNBC). However, the relationship between tumor mutation burden (TMB) and immune infiltration and the prognosis of TNBC remains unclear. In this study, to explore the immunogenicity of TNBC, we divided patients with TNBC into high and low TMB groups based on the somatic mutation data of TNBC in The Cancer Genome Atlas (TCGA), and screened out genes with mutation rate ≥10. Then, Kaplan-Meier survival analysis revealed that the 5-year survival rate of the high TMB group was much higher than that of the low TMB group and the two groups also showed differences in immune cell infiltration. Further exploration found that the FAT3 gene, which displays significant difference and a higher mutation rate between the two groups, is not only significantly related to the prognosis of TNBC patients but also exhibits difference in immune cell infiltration between the wild group and the mutant group of the FAT3 gene. The results of gene set enrichment analysis and drug sensitivity analysis further support the importance of the FAT3 gene in TNBC. This study reveals the characteristics of TMB and immune cell infiltration in triple-negative breast cancer and their relationship with prognosis, to provide new biomarkers and potential treatment options for the future treatment of TNBC. The FAT3 gene, as a risk predictor gene of TNBC, is considered a potential biological target and may provide new insight for the treatment of TNBC.