Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach

Mercatelli, Daniele; Formaggio, Francesco; Caprini, Marco; Holding, Andrew N; Giorgi, Federico M.

doi:10.1042/bsr20212218

Cited by 6 publications

(3 citation statements)

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“…Therefore, in this paper, we divide LGG datasets into these two cases for discussion. BRCA datasets are used to classify the PAM50 subtypes of breast cancer, including five categories: Normal-like, Basal-like, HER2enriched, Luminal A, and Luminal B [38][39]. SKCM datasets are used for the classification of melanoma, including the Keratin, Immune, and MITF-low categories [40][41].…”

Section: Benchmark Datasetsmentioning

confidence: 99%

MODILM: Towards Better Complex Diseases Classification Using a Novel Multi-omics Data Integration Learning Model

Zhong

Peng

Lin

et al. 2023

Preprint

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Background Complex disease classification is an important part of the complex disease diagnosis and personalized treatment process. It has been shown that the integration of multi-omics data can analyze and classify complex diseases more accurately, because multi-omics data are highly correlated with the onset and progression of various diseases and can provide comprehensive and complementary information about a disease. However, multi-omics data of complex diseases are usually characterized by high imbalance, scale variation, high data heterogeneity and high noise interference, which pose great challenges to multi-omics integration methods. Results We propose a novel multi-omics data integration learning model called MODILM, to obtain more important and complementary information for complex disease classification from multiple omics data. Specifically, MODILM first initially constructs a similarity network for each omics data using cosine similarity measure, then learns the sample-specific features and intra-association features of single-omics data from the similarity networks using Graph Attention Networks, then maps them uniformly to a new feature space to further strengthen and extract high-level omics-specific features of the omics data using Multilayer Perceptron networks. MODILM then uses a View Correlation Discovery Network to fuse the high-level omics-specific features extracted from each omics data and further learn cross-omics features in the label space, providing unique class-level distinctiveness to classify complex diseases. We conducted extensive experiments on six benchmark datasets having the miRNA expression data, mRNA and DNA methylation data to demonstrate the superiority of our MODILM. The experimental results show that MODILM outperforms state-of-the-art methods, effectively improving the accuracy of complex disease classification. Conclusions Our MODILM provides a more competitive way to extract and integrate important and complementary information from multiple omics data, providing a very promising tool for supporting decision making for clinical diagnosis.

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Section: Benchmark Datasetsmentioning

confidence: 99%

MODILM: Towards Better Complex Diseases Classification Using a Novel Multi-omics Data Integration Learning Model

Zhong

Peng

Lin

et al. 2023

Preprint

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“…Numerous studies have been developed to find potential biomarkers for breast cancer survival and progression. 2,3 BRCA1 and BRCA2 are the two most important breast cancer susceptibility genes. A substantial proportion of hereditary breast cancers can be attributed to BRCA mutation.…”

Section: Introductionmentioning

confidence: 99%

Survival Outcomes and Efficacy of Platinum in Early Breast Cancer Patients with Germline BRCA1 or BRCA2 Mutation: A Multicenter Retrospective Cohort Study

Chen,

Qian,

Xiao

et al. 2023

BCTT

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The study aimed to compare the survival outcomes and efficacy of platinum in early breast cancer patients with BRCA1 and BRCA2 mutations. Methods: Patients diagnosed with stage I-III breast cancer and carrying germline pathogenic/likely pathogenic BRCA mutations in three medical institutions in China from April 2016 to January 2021 were retrospectively analyzed. Data on clinical and pathological characteristics, treatment information, pathogenic variants of BRCA, and survival outcomes were collected for all eligible patients. Outcomes: One hundred and sixty-nine patients with BRCA mutations were enrolled, including BRCA1 mutation (53.3%, n = 90) and BRCA2 mutation (46.7%, n = 79). The median age was 39 years, and most patients (68.1%, n = 115) were stage I-II. Patients with BRCA1 mutations were characterized by histological grade III (55.6%) and higher Ki-67 index (Ki-67 ≥ 30%, 78.9%) compared with patients with BRCA2 mutations (27.8%, 58.2%). BRCA1 mutation patients accounted for a significantly higher proportion of triple negative breast cancer than BRCA2 mutation patients (71.1% vs 19.0%, P < 0.0001). A total of 142 (84.0%) patients received neo/ adjuvant chemotherapy, including anthracycline and/or taxane-based regimens (55.6%) or platinum-based regimens (27.2%). Median follow-up was 33.2 months. Three-year DFS (disease-free survival) and DRFS (distant recurrence-free survival) had no significant differences between patients with BRCA1 and BRCA2 mutations (82.0% vs 85.4%, P = 0.35; 94.3% vs 94.6%, P = 0.39). The 3-year DFS rate in BRCA1 mutation cohort of patients received platinum regimen was significantly higher than patients received nonplatinum regimen (96.0% vs 75.2%, P = 0.01). No differences between DFS and DRFS were observed in patients with BRCA2 mutation received platinum regimen and non-platinum regimen. Conclusion: Similar survival outcomes were observed in early breast cancer patients with BRCA1 and BRCA2 mutation, though they had different biological characteristics. Patients with BRCA1 mutations are more benefit from platinum-regimen. The value of platinum-regimen for early breast cancer patients with BRCA1 and BRCA2 needs to be verified further.Plain Language Summary: BRCA pathogenic mutation has been the principal genetic cause of breast cancer. Since the majority of studies have focused on the clinical manifestation and survival of patients with BRCA mutations and the wild type, few studies gave a concern about the survival outcome of breast cancer patients with BRCA1 and BRCA2 mutation. We aimed to compare the survival outcomes and efficacy of platinum between early breast cancer patients with BRCA1 and BRCA2. Our results showed that patients with BRCA1 and BRCA2 mutations had different biological characteristics, but similar survival outcomes in the early stages. Patients with BRCA1 mutations are more sensitive to platinum therapy in neoadjuvant settings.

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“…Briefly, we downloaded transcriptional data and clinical information from the TCGA database, and then constructed and verified the gene signature related to apoptosis. Finally, we analyzed the GSEA enrichment and explored the potential regulatory mechanism, which provides a new perspective for breast cancer research ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

A Signature of Three Apoptosis-Related Genes Predicts Overall Survival in Breast Cancer

et al. 2022

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BackgroundThe commonest malignancy in women is known as breast cancer (BC). Numerous studies demonstrated that apoptosis appears to be critical to the management and clinical outcome of BC patients. The purpose of this study is to explore the potential connection between apoptosis and BC and establish the apoptosis-associated gene signature in BC.MethodsThe data of BC patient transcripts and related clinical information comes from the Cancer Genome Atlas Database (TCGA), and the genes related to apoptosis come from the Molecular Characterization Database (MSigDB). We identified the abnormally expressed apoptosis-related genes in BC samples. The optimal apoptosis-related genes screened by Cox regression analysis were designed to construct a prognostic model for predicting BC patients. Using the Nom Chart to Predict 1-Year, 3-Year, and 5-Year overall survival for BC patients. The gene signature-related functional pathways were explored by gene set enrichment analysis (GSEA).ResultsThree genes [alpha subunit of the interleukin 3 receptor (IL3RA), apoptosis-inducing factor mitochondrial-associated 1 (AIFM1), and phosphatidylinositol-3 kinase catalytic alpha (PIK3CA)] correlated with apoptosis were shown to be strongly linked to the overall survival of BC. Survival analysis shows that the risk score is directly proportional to the poor prognosis of BC patients. Risk assessment based on three genetic characteristics (age, pathological stage N, and pathological stage M) can independently predict the prognosis of patients with BC. The Nom chart is most suitable for assessing the long-term survival rate of BC patients. The results of GSEA demonstrated that numerous cell cycle-related pathways were abundant in the high-risk group.ConclusionWe constructed an apoptosis-associated gene signature in BC, which had a potential clinical application prospect for BC patients.

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Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach

Cited by 6 publications

References 50 publications

MODILM: Towards Better Complex Diseases Classification Using a Novel Multi-omics Data Integration Learning Model

MODILM: Towards Better Complex Diseases Classification Using a Novel Multi-omics Data Integration Learning Model

Survival Outcomes and Efficacy of Platinum in Early Breast Cancer Patients with Germline BRCA1 or BRCA2 Mutation: A Multicenter Retrospective Cohort Study

A Signature of Three Apoptosis-Related Genes Predicts Overall Survival in Breast Cancer

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