ArticleBMC medical genomics2019
Novel secretome-to-transcriptome integrated or secreto-transcriptomic approach to reveal liquid biopsy biomarkers for predicting individualized prognosis of breast cancer patients.
Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 14 citations in OpenAlex.
- Biliverdin reductase B as a new target in breast cancer.Breast cancer research : BCR · 2025Article
- Pan-cancer secreted proteome and skeletal muscle regulation: insight from a proteogenomic data-driven knowledge base.Functional & integrative genomics · 2025Review
- Metabolic Functions of Biliverdin IXβ Reductase in Redox-Regulated Hematopoietic Cell Fate.Antioxidants (Basel, Switzerland) · 2023Review
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- Nano-omics: nanotechnology-based multidimensional harvesting of the blood-circulating cancerome.Nature reviews. Clinical oncology · 2022Review
- Global mapping of cancers: The Cancer Genome Atlas and beyond.Molecular oncology · 2021Review
- Liquid biopsies and cancer omics.Cell death discovery · 2020Review
- Personalized Approach to Determination of Histidine-Rich Glycoprotein and E-Cadherin in Supernatants of Immunocompetent Blood Cells and Breast Biopsy Specimens in Breast Malignant and Non-Malignant Disease.Doklady. Biochemistry and biophysics · 2020Article
- Components of the human-specific, p53-mediated "kill switch" tumor suppression mechanism are usurped by human tumors, creating the possibility of therapeutic exploitation.Cancer drug resistance (Alhambra, Calif.) · 2019Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundPresently, a 50-gene expression model (PAM50) serves as a breast cancer (BC) subtype classifier that is insufficient to distinguish, within each single PAM50-classified subtype, patient subpopulations having different prognosis. There is a pressing need for inexpensive and minimally invasive biomarker tests to easily and accurately predict individuals' clinical outcomes and response to treatments. Although quantitative proteomic approaches have been developed to identify/profile proteins secreted (secretome) from various cancer cell lines in vitro, missing are the clinicopathological relevance and the associated prognostic value of these secretomic identifications.
methodsTo discover biomarkers to predict individualized prognosis we introduce a new multi-omics (secreto-transcriptomics) method that identifies, in their oncogenically secreted states, candidate markers of BC subtypes whose genes bear patient-specific mRNA expression alterations of prognostic significance. First, we used label-free quantitative (LFQ) proteomics to identify the proteins showing BC-subtypic secretion from a series of BC cell lines representing major BC-subtypes. To determine and externally validate the prognostic value of these secreted proteins, we developed a secreto-transcriptomic approach that discovered a PAM50-subtypic Secretion-Correlated mRNA Expression Pattern (SeCEP) wherein the PAM50-subtypic secretion of select proteins statistically correlated with cis-mRNA expression of their encoding genes in patients of the corresponding PAM50-subtypes. Kaplan-Meier analysis of SeCEP genes was used to identify new liquid biopsy biomarkers for predicting individualized prognosis.
resultsThe mRNA expression-to-secretion correlation (SeCEP) pinpointed multiple genes that are fully translated into the oncogenically active secretome in a PAM50-subtypic manner. Further, multiple SeCEP genes in distinct combinations or panels of multiple SeCEP genes were identified as 'systems prognostic markers' that showed mRNA co-overexpression patterns in the distinct subpopulations of PAM50-subtypic patients with poor prognosis or high-risk of relapse. Thus, our secreto-transcriptomic approach statistically linked BC subtypic secretome genes with patient-specific information about their mRNA expression alterations and significantly improved the sensitivity and specificity in patient stratification in the context of clinical outcomes or prognosis.
conclusionsBy combining LFQ secretome screening with proteo-transcriptomic retrospective analysis of patient data our integrated multi-omics approach bypasses costly, tedious, genome-wide fishing and predictive modeling that are commonly required to distinguish a few prognostically altered genes from thousands of other non-BC related genes in a genome.
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