Predicting survival and prognosis of postoperative breast cancer brain metastasis: a population-based retrospective analysis.

BACKGROUND: Breast cancer is one of the most common cancer in women and a proportion of patients experiences brain metastases with poor prognosis. The study aimed to construct a novel predictive clinical model to evaluate the overall survival (OS) of patients with postoperative brain metastasis of breast cancer (BCBM) and validate its effectiveness. METHODS: From 2010 to 2020, a total of 310 female patients with BCBM were diagnosed in The Affiliated Cancer Hospital of Xinjiang Medical University, and they were randomly assigned to the training cohort and the validation cohort. Data of another 173 BCBM patients were collected from the Surveillance, Epidemiology, and End Results Program (SEER) database as an external validation cohort. In the training cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used to determine the fundamental clinical predictive indicators and the nomogram was constructed to predict OS. The model capability was assessed using receiver operating characteristic, C-index, and calibration curves. Kaplan-Meier survival analysis was performed to evaluate clinical effectiveness of the risk stratification system in the model. The accuracy and prediction capability of the model were verified using the validation and SEER cohorts. RESULTS: LASSO Cox regression analysis revealed that lymph node metastasis, molecular subtype, tumor size, chemotherapy, radiotherapy, and lung metastasis were statistically significantly correlated with BCBM. The C-indexes of the survival nomogram in the training, validation, and SEER cohorts were 0.714, 0.710, and 0.670, respectively, which showed good prediction capability. The calibration curves demonstrated that the nomogram had great forecast precision, and a dynamic diagram was drawn to increase the maneuverability of the results. The Risk Stratification System showed that the OS of low-risk patients was considerably better than that of high-risk patients ( P < 0.001). CONCLUSION: The nomogram prediction model constructed in this study has a good predictive value, which can effectively evaluate the survival rate of patients with postoperative BCBM.

HSPA8 Is a New Biomarker of Triple Negative Breast Cancer Related to Prognosis and Immune Infiltration.

Objective: Triple negative breast cancer (TNBC) is a kind of cancer that endangers the lives of women all over the world in the 21st century. Heat shock protein member 8 (HSPA8) is the chaperone gene of the heat shock protein family. It is involved in many cellular functions. For example, it promotes the circulation between ATP and ADP, participates in protein folding, and can change the vitality of the cell and inhibit its growth. However, the abnormal expression of HSPA8 gene in TNBC and its diagnostic and prognostic significance still need to be further studied. Methods: First, we used related databases (such as TCGA, GEO, GTEx, ONCOMINE, TIMER2.0, UALCAN, HPA, STRING, CCLE, and Kaplan-Meier plotter databases) to analyze the relationship between HSPA8 and TNBC by bioinformatics. Then, the analysis using only a small part of the experimental work is used to explain our findings. For example, HSPA8 protein expression was evaluated by immunohistochemical method in TNBC tissues. Western blotting experiments were carried out to verify the results. Then, the clinicopathological characteristics of patients with TNBC were analyzed by R software and Cox regression analysis. On the basis, a nomogram is constructed to estimate the 1-, 3-, and 5-year overall survival (OS). The prognostic nomogram performance was calibrated and evaluated by the calibration curve and receiver operating characteristic (ROC) curve. Results: In the study, we analyzed the three GEO databases (including GSE86945, GSE106977, and GSE102088) and found that HSPA8 is one of the central genes of TNBC. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) researches indicated that HSPA8 was mainly involved in partner-mediated autophagy, mRNA catabolism, neutrophil activation, immune response, protein targeting, RNA splicing, RNA catabolism, and other biological processes. Next, we used bioinformatics technology to find that the expression level of HSPA8 in breast cancer (BC) and TNBC samples was significantly higher than that in normal breast tissues, which was determined by analyzing hospital patient samples and related experiments. In addition, the expression level of HSPA8 in BC and TNBC samples was significantly correlated with clinical indexes such as TNM stage. The Cox analysis revealed that the expression of HSPA8 in TNBC had significant clinical prognostic value. The results of nomogram and ROC test show that HSPA8 has significant predictive ability in TNBC. The results of immune infiltration of HSPA8 through the TIMER2.0 database showed that there was a significant correlation between HSPA8 and immune cell subsets. Conclusions: Our results show that the expression of HSPA8 in TNBC has important clinical diagnostic significance and clarify the potential molecular mechanism that promotes the evolution of TNBC. The high expression of HSPA8 may be related with the poor clinical outcome of TNBC. This helps to provide us with a new direction of TNBC targeted therapy.