Construction Of A Nomogram Discriminating Lung Metastasis Risk Of Breast Cancer Patients Through Bioinformatics Methods

Breast cancer is the most common malignant disease in women.With the improvement of diagnosis and treatment,the life threat of primary breast cancer has been effectively controlled.However,metastasis is the foremost cause of death.Breast tumor cells have a proclivity to metastasize to specific organs.The lung is one of the most common sites of breast cancer metastasis.We hope to use limited biomarkers to identify breast cancer patients at high risk of lung metastasis.After a comprehensive search of the GEO database,we obtained two data sets that met the research criteria: GSE5327 and GSE2603.Genes associated with lung metastasis in breast cancer were preliminarily identified in the training set GSE5327.Enrichment analysis of GO functions and KEGG pathways was conducted based on these genes.The best genes for building prognostic nomogram were selected using a robust likelihood-based survival modeling approach.The effectiveness of the prognostic nomogram was evaluated in the training set GSE5327 and the validation set GSE2603.A total of 319 genes were considered to be associated with lung metastasis in breast cancer.Six of them were chosen to build a gene prognostic nomogram.Both the internal validation and the external validation manifested the effectiveness of our 6-gene prognostic nomogram in predicting the lung metastasis risk of breast cancer patients.We constructed a new and convenient prediction model based on 6 genes that showed practical value in predicting the lung metastasis risk for clinical breast cancer patients.In addition,all 6 of these genes could be treated as potential metastasis biomarkers for antimetastatic therapy in breast cancer.The evolution of this nomogram will provide a good reference for the prediction of tumor metastasis to other specific organs.