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SAGES: Structural Analysis of Gene and Protein Expression Signatures Reveal Dynamic Expression of Disordered Proteins in Breast Cancer and Tissue

ABSTRACT: Structural features of proteins capture underlying information about protein evolution and function, which enhances the analysis of proteomic and transcriptomic data. Here we develop Structural Analysis of Gene and protein Expression Signatures (SAGES), a method that describes expression data using features calculated from sequence-based prediction methods and 3D structural models. We used SAGES, along with machine learning, to characterize tissues from healthy individuals and those with breast cancer. We identified prominent expression of intrinsically disordered regions in breast cancer proteins as well as relationships between drug perturbation signatures and breast cancer disease signatures. Our results suggest that SAGES is generally applicable to describe diverse biological phenomena including disease states and drug effects.

ORGANISM(S): Homo sapiens

PROVIDER: GSE227679 | GEO | 2024/03/31

REPOSITORIES: GEO

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Project description:Breast cancer accounts for roughly 30% of all cancers in women worldwide, has a 15% death rate, and incidence rates are increasing at a rate of about 0.5% per year. Breast cancer comprises a heterogeneous group of tumor subtypes, whether defined by the histopathology of the primary tumor, the expression pattern of hormone receptors (estrogen and/or progesterone receptors; ER/PR) and epidermal growth factor receptor 2 (HER2), genetic alterations of transcriptomic traits. These patient-to-patient differences (as known as �쁦ntertumoral heterogeneity��, largely affect patient prognosis and treatment options. Alongside intertumoral heterogeneity, many studies reported that breast cancers heterogeneous consisting of many different cells or subclones of which different gene expression profiles within a patient�셲 primary tumor and individual metastases. These differences within the tumor are referred to as intratumor heterogeneity, which is caused by a combination of extrinsic factors from the tumor microenvironment and intrinsic parameters including genetic, epigenetic and transcriptomic traits, ability of proliferation, migration and invasion, cell plasticity, and the extent of stemness. These heterogeneities endow tumors with multiple capabilities and biological characteristics, making them more prone to metastasis, recurrence, and drug resistance. To overcome these facing challenges, understanding the proteome mechanisms behind transcriptome profiling from the aspect of treatment can help to improve resistance to cancer therapy. Recent proteomics technologies based on mass spectrometry enable an unbiased investigation of drug-induced changes in protein abundance and post-translational modifications. Several studies on resistance to chemotherapy have recently published data on mass spectrometry-based chemotherapeutic proteome profiling, which has the potential to discover molecular subtypes and related pathway features that may have been missed in prior transcriptome analyses. Nevertheless, few proteomics studies to date explore three types of drug-specific resistance of breast cancer signatures. In this study, we employed tandem mass tag (TMT) based proteomics technology to process the acquired mass spectrometry data to test the hypothesis that the chemotherapy in breast cancer cells may have distinct protein profiles that may result in their drug properties and new clinical implications. By unraveling the protein signatures across tamoxifen, doxorubicin, and paclitaxel and their relationship between drug-resistant cell lines and normal breast cancer cells, our study advances the understanding of drug-specific resistance and provides potential diagnostic and prognostic markers, as well as testable targets of therapy specific to breast cancer resistant cells.

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SAGES: Structural Analysis of Gene and Protein Expression Signatures Reveal Dynamic Expression of Disordered Proteins in Breast Cancer and Tissue

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