Integrative, In Silico and Comparative Analysis of Breast Cancer Secretome Highlights Invasive-Ductal-Carcinoma-Grade Progression Biomarkers

Abstract

Globally, BC is the most frequently diagnosed cancer in women. The aim of this study was to identify novel secreted biomarkers that may indicate progression to high-grade BC malignancies and therefore predict metastatic potential. A total of 33 studies of breast cancer and 78 of other malignancies were screened via a systematic review for eligibility, yielding 26 datasets, 8 breast cancer secretome datasets, and 18 of other cancers that were included in the comparative secretome analysis. Sequential bioinformatic analysis using online resources enabled the identification of enriched GO_terms, overlapping clusters, and pathway reconstruction. This study identified putative predictors of IDC grade progression and their association with breast cancer patient mortality outcomes, namely, HSPG2, ACTG1, and LAMA5 as biomarkers of in silico pathway prediction, offering a putative approach by which the abovementioned proteins may mediate their effects, enabling disease progression. This study also identified ITGB1, FBN1, and THBS1 as putative pan-cancer detection biomarkers. The present study highlights novel, putative secretome biomarkers that may provide insight into the tumor biology and could inform clinical decision making in the context of IDC management in a non-invasive manner.

HE2Gene: image-to-RNA translation via multi-task learning for spatial transcriptomics data

“…These genes were selected by calculating the average gene expression of 250 target genes in both tumor and normal tissue spots across all patients and identifying the top five with the greatest differences in average expression. The selected genes—FASN, ACTG1, PTMA, GNAS, and HSP90AB1—have all been previously identified as known cancer biomarkers ( Tripathi et al 2011 , Lin et al 2020 , Jiang et al 2021 , Kastora et al 2022 , Zhang et al 2022 ). The positive predictive performance of HE2Gene for these genes indicates a higher correlation between biomarker genes and tissue morphology.…”

“…In this premise, we assume that adjacent patches with the same annotation in whole-slide images tend to have similar gene expressions. This presumption is based on the understanding that overexpression or underexpression of certain genes can precipitate disease states ( Kastora et al 2022 ). Moreover, it has been demonstrated that some genes serve as biomarkers for specific diseases ( Tripathi et al 2011 , Zhang et al 2022 ), further supporting the notion that spatial proximity in tissue samples correlates with gene expression patterns related to disease conditions.…”