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MLSP: A bioinformatics tool for predicting molecular subtypes and prognosis in patients with breast cancer 1 1 Introduction 1 2 Methods 2 2.1 Breast cancer datasets 2 2.2 Consensus clustering 2 2.3 Genomic instability and somatic copy-number alterations (SCNA) analysis 2 2.4 Gene set variation analysis (GSVA) and functional annotation 2 2.5 Single sample gene expression pathway analysis 3 2.6 PD1/CTLA4 response prediction and PAM50 subtyping. 3 2.7 Tumor mutation burden (TMB) analysis 3 2.8 Gene feature selection of each subtype of BRCA and drug analysis 3 2.9 Prognostic model building 3 2.10 Prognostic model validation 3 2.11 Nomogram construction and validation 4 2.12 Design of a web app 4 2.13 Pan-cancer analysis of overall survival 4 3 Results 4 3.1 Multiple-Pathway-Based stratification of breast cancer 4 3.2 Breast cancer subtypes based on Multiple-Pathway analysis exhibit distinct immune features 5 3.3 Subtypes based on Multiple-Pathway analysis show distinct tumor mutation burden 6 3.4 Drug screening in Multiple-Pathway-Based and PAM50-Based subtypes 8 3.5 Construction of a prognostic robust gene pair (RGP) score for breast cancer 9 3.6 Evaluation of the RGP score 9 3.7 Cons

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Language

UND

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nonfiction