Hypoxia-induced factor-1 alpha upregulates vascular endothelial growth factor C to promote lymphangiogenesis and angiogenesis in breast cancer patients

Hypoxia-induced factor-1 alpha （HIF-1α） affects many effector molecules and regulates tumor lymphangio- genesis and angiogenesis during hypoxia. The aim of this study was to investigate the role of HIF-1α in the regu- lation of vascular endothelial growth factor C （VEGF-C） expression and its effect on lymphangiogenesis and an- giogenesis in breast cancer. Lymphatic vessel density （LVD）, microvessel density （MVD） and the expressions of HIF-1α and VEGF-C proteins were evaluated by immunohistochemistry in 75 breast cancer samples. There was a significant correlation between HIF-1α and VEGF-C （P = 0.014, r = 0.273, Spearman＇s coefficient of correlation）. HIF-1α and VEGF-C overexpression was significantly correlated with higher LVD （P = 0.003 and P = 0.017, re- spectively）, regional lymph nodal involvement （P = 0.002 and P = 0.004, respectively） and advanced tumor, node, metastasis （TNM） classification （P = 0.001 and P = 0.01, respectively）. Higher MVD was observed in the group expressing higher levels of HIF-1α and VEGF-C （P = 0.033 and P = 0.037, respectively）. Univariate analysis showed shorter survival time in patients expressing higher levels of HIF-1α and VEGF-C. HIF-1α was also found to be an independent prognostic factor of overall survival in multivariate analysis. The results suggest that HIF-1α may affect VEGF-C expression, thus acting as a crucial regulator of lymphangiogenesis and angiogenesis in breast cancer. This study highlights promising potential of HIF- 1α as a therapeutic target against tumor lymph node me- tastasis.

An inflammatory-related genes signature based model for prognosis prediction in breast cancer

Background:Breast cancer has become the most common malignant tumor in the world.It is vital to discover novel prognostic biomarkers despite the fact that the majority of breast cancer patients have a good prognosis because of the high heterogeneity of breast cancer,which causes the disparity in prognosis.Recently,inflammatory-related genes have been proven to play an important role in the development and progression of breast cancer,so we set out to investigate the predictive usefulness of inflammatory-related genes in breast malignancies.Methods:We assessed the connection between Inflammatory-Related Genes(IRGs)and breast cancer by studying the TCGA database.Following differential and univariate Cox regression analysis,prognosis-related differentially expressed inflammatory genes were estimated.The prognostic model was constructed through the Least Absolute Shrinkage and Selector Operation(LASSO)regression based on the IRGs.The accuracy of the prognostic model was then evaluated using the Kaplan-Meier and Receiver Operating Characteristic(ROC)curves.The nomogram model was established to predict the survival rate of breast cancer patients clinically.Based on the prognostic expression,we also looked at immune cell infiltration and the function of immune-related pathways.The CellMiner database was used to research drug sensitivity.Results:In this study,7 IRGs were selected to construct a prognostic risk model.Further research revealed a negative relationship between the risk score and the prognosis of breast cancer patients.The ROC curve proved the accuracy of the prognostic model,and the nomogram accurately predicted survival rate.The scores of tumorinfiltrating immune cells and immune-related pathways were utilized to calculate the differences between the low-and high-risk groups,and then explored the relationship between drug susceptibility and the genes that were included in the model.Conclusion:These findings contributed to a better understanding of the function of inflammatory-related genes in breast cancer,and the prognostic risk model provides a potentially promising prognostic strategy for breast cancer.

Value of negatively correlated miR-205-5p/HMGB3 and miR-96-5p/FOXO1 on the diagnosis of breast cancer and benign breast diseases

Background:MicroRNA(miRNA)and mRNA levels in matching specimens were used to identify miRNA–mRNA interactions.We aimed to integrate transcriptome,immunophenotype,methylation,mutation,and survival data analyses to examine the profiles of miRNAs and target mRNAs and their associations with breast cancer(BC)diagnosis.Methods:Based on the Gene Expression Omnibus(GEO)database and The Cancer Genome Atlas(TCGA),differentially expressed miRNAs and targeted mRNAs were screened from experimentally verified miRNA-target interaction databases using Pearson's correlation analysis.We used real-time quantitative reverse transcription polymerase chain reaction to verify BC and benign disease samples,and logistic regression analysis was used to establish a diagnostic model based on miRNAs and target mRNAs.Receiver operating characteristic curve analysis was performed to test the ability to recognize the miRNA-mRNA pairs.Next,we investigated the complex interactions between miRNA-mRNA regulatory pairs and phenotypic hallmarks.Results:We identified 27 and 359 dysregulated miRNAs and mRNAs,respectively,based on the GEO and TCGA databases.Using Pearson's correlation analysis,10 negative miRNA-mRNA regulatory pairs were identified after screening both databases,and the related miRNA and target mRNA levels were assessed in 40 BC tissues and 40 benign breast disease tissues.Two key regulatory pairs(miR-205-5p/High mobility group box 3(HMGB3)and miR-96-5p/Forkhead Box O1(FOXO1))were selected to establish the diagnostic model.They also had utility in survival and clinical analyses.Conclusions:A diagnostic model including two miRNAs and their respective target mRNAs was established to distinguish between BC and benign breast diseases.These markers play essential roles in BC pathogenesis.