Discovering Breast Cancer Biomarkers Candidates through mRNA Expression Analysis Based on The Cancer Genome Atlas Database

Kim, Dong Hyeok; Lee, Kyung Eun

doi:10.3390/jpm12101753

Cited by 6 publications

(6 citation statements)

References 55 publications

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“…Overexpression of PSMB4 promotes the proliferation and survival of breast cancer cells, lleading to an unfavorable prognosis [ 47 ]. The study revealed a significant upregulation in AP1M2, PSMD10, and RPL2 expression in breast cancer tissue compared to normal tissues [ 48 – 50 ]. RPS27A is overexpressed in breast cancer, enhanceing EBV-encoded LMP1-mediated proliferation and invasion through stabilization of LMP1 [ 51 ].…”

Section: Resultsmentioning

confidence: 99%

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome

Chai,

Lin,

Lin

et al. 2024

BMC Bioinformatics

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Background Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability. In this study, we proposed a novel multitask deep neural network called UISNet to predict the outcome of breast cancer. The UISNet is able to interpret the importance of features for the prediction model via an uncertainty-based integrated gradients algorithm. UISNet improved the prediction by introducing prior biological pathway knowledge and utilizing patient heterogeneity information. Results The model was tested in seven public datasets of breast cancer, and showed better performance (average C-index = 0.691) than the state-of-the-art methods (average C-index = 0.650, ranged from 0.619 to 0.677). Importantly, the UISNet identified 20 genes as associated with breast cancer, among which 11 have been proven to be associated with breast cancer by previous studies, and others are novel findings of this study. Conclusions Our proposed method is accurate and robust in predicting breast cancer outcomes, and it is an effective way to identify breast cancer-associated genes. The method codes are available at: https://github.com/chh171/UISNet.

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Section: Resultsmentioning

confidence: 99%

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome

Chai,

Lin,

Lin

et al. 2024

BMC Bioinformatics

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“…Of the 97 known protein-coding genes at these loci, many of the genes located at, or spanning the peak QTL are known to be associated directly with cancer development, or with cancer associated pathways. For example, Kcnd2, Tspan12, Ing3 and Cped1 located on Chromosome six are known to promote proliferation of breast cancer cells ( Yang et al, 2023 ), be a critical factor for cancer associated fibroblast mediated invasion ( Otomo et al, 2014 ), used as a potential biomarker for CRC/breast cancer ( Kim and Lee, 2022 ; Li et al, 2023 ) or confer oncogenic effects in prostate cancer ( Zhu et al, 2023 ) respectively. Likewise on chromosome 12, expression of Matn3, Osr1, and Nt5c1b have been associated with colon adenocarcinoma, gastric and breast cancer development ( Zhao Z. et al, 2023a ; Chi et al, 2023 ; Gadwal et al, 2023 ); potential use as a biomarker for breast cancer ( Li et al, 2020 ) or promotion of EMT and metastasis in breast cancer ( Wang et al, 2020 ; Li et al, 2021 ) and identified as a cancer testis-antigen in canine malignancies ( Nemec et al, 2019 ) respectively.…”

Section: Discussionmentioning

confidence: 99%

The MexTAg collaborative cross: host genetics affects asbestos related disease latency, but has little influence once tumours develop

Fisher,

Patrick,

Hoang

et al. 2024

Front. Toxicol.

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Objectives: This study combines two innovative mouse models in a major gene discovery project to assess the influence of host genetics on asbestos related disease (ARD). Conventional genetics studies provided evidence that some susceptibility to mesothelioma is genetic. However, the identification of host modifier genes, the roles they may play, and whether they contribute to disease susceptibility remain unknown. Here we report a study designed to rapidly identify genes associated with mesothelioma susceptibility by combining the Collaborative Cross (CC) resource with the well-characterised MexTAg mesothelioma mouse model.Methods: The CC is a powerful mouse resource that harnesses over 90% of common genetic variation in the mouse species, allowing rapid identification of genes mediating complex traits. MexTAg mice rapidly, uniformly, and predictably develop mesothelioma, but only after asbestos exposure. To assess the influence of host genetics on ARD, we crossed 72 genetically distinct CC mouse strains with MexTAg mice and exposed the resulting CC-MexTAg (CCMT) progeny to asbestos and monitored them for traits including overall survival, the time to ARD onset (latency), the time between ARD onset and euthanasia (disease progression) and ascites volume. We identified phenotype-specific modifier genes associated with these traits and we validated the role of human orthologues in asbestos-induced carcinogenesis using human mesothelioma datasets.Results: We generated 72 genetically distinct CCMT strains and exposed their progeny (2,562 in total) to asbestos. Reflecting the genetic diversity of the CC, there was considerable variation in overall survival and disease latency. Surprisingly, however, there was no variation in disease progression, demonstrating that host genetic factors do have a significant influence during disease latency but have a limited role once disease is established. Quantitative trait loci (QTL) affecting ARD survival/latency were identified on chromosomes 6, 12 and X. Of the 97-protein coding candidate modifier genes that spanned these QTL, eight genes (CPED1, ORS1, NDUFA1, HS1BP3, IL13RA1, LSM8, TES and TSPAN12) were found to significantly affect outcome in both CCMT and human mesothelioma datasets.Conclusion: Host genetic factors affect susceptibility to development of asbestos associated disease. However, following mesothelioma establishment, genetic variation in molecular or immunological mechanisms did not affect disease progression. Identification of multiple candidate modifier genes and their human homologues with known associations in other advanced stage or metastatic cancers highlights the complexity of ARD and may provide a pathway to identify novel therapeutic targets.

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“…Recently, metformin treatment reduced oncogenic miR-21-5p in BC cells, further releasing CAB39L expression and evoking activation of AMPK, which was closely associated with reduced migratory and invasive capacity in BC cell lines [ 28 ]. Moreover, HBA2 was significantly downregulated in BC tissues and displayed excellent diagnostic performance [ 29 ]. DLGAP5 is a microtubule-associated protein and mitotic phosphorylated substrate of Aurora-A.…”

Section: Discussionmentioning

confidence: 99%

A novel signature based on cancer-associated fibroblast genes to predict prognosis, immune feature, and therapeutic response in breast cancer

Wang

et al. 2023

Aging

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Breast cancer (BC) ranks first in the incidence of tumors in women and remains the most prevalent malignancy in women worldwide. Cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TME) profoundly influence the progression, recurrence, and therapeutic resistance in BC. Here, we intended to establish a risk signature based on screened CAF-associated genes in BC (BCCGs) for patient stratification. Initially, BCCGs were screened by a combination of several CAF gene sets. The identified BCGGs were found to differ significantly in the overall survival (OS) of BC patients. Accordingly, we constructed a prognostic prediction signature of 5 BCCGs, which were independent prognostic factors associated with BC based on univariate and multivariate Cox regression. The risk model divided patients into low-and high-risk groups, accompanied by different OS, clinical features, and immune infiltration characteristics. Receiver operating characteristic (ROC) curves and a nomogram further validated the predictive performance of the prognostic model. Notably, 21 anticancer agents targeting these BCCGs possessed better sensitivity in BC patients. Meanwhile, the elevated expression of the majority of immune checkpoint genes suggested that the high-risk group may benefit more from immune checkpoint inhibitors (ICIs) therapy. Taken together, our well-established model is a robust instrument to precisely and comprehensively predict the prognosis, immune features, and drug sensitivity in BC patients, for combating BC.

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Discovering Breast Cancer Biomarkers Candidates through mRNA Expression Analysis Based on The Cancer Genome Atlas Database

Cited by 6 publications

References 55 publications

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome

The MexTAg collaborative cross: host genetics affects asbestos related disease latency, but has little influence once tumours develop

A novel signature based on cancer-associated fibroblast genes to predict prognosis, immune feature, and therapeutic response in breast cancer

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