Integration of Automatic Text Mining and Genomic and Proteomic Analysis to Unravel Prostate Cancer Biomarkers

Lima, Tânia; Ferreira, Rita; Freitas, M.J.; Henrique, Rui; Vitorino, Rui; Fardilha, Margarida

doi:10.1021/acs.jproteome.1c00763

Cited by 8 publications

(6 citation statements)

References 59 publications

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“…Based on text mining, we retrieved medical record numbers with “metastasis” and “lymph node” involvement. Similar approaches were used to identify the hub genes for cancer metastasis 53 . Dissemination of cancer cells from the primary site via the blood is called metastasis 54 , 55 .…”

Section: Discussionmentioning

confidence: 99%

Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms

Botlagunta

Bala

et al. 2023

Sci Rep

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Metastatic Breast Cancer (MBC) is one of the primary causes of cancer-related deaths in women. Despite several limitations, histopathological information about the malignancy is used for the classification of cancer. The objective of our study is to develop a non-invasive breast cancer classification system for the diagnosis of cancer metastases. The anaconda—Jupyter notebook is used to develop various python programming modules for text mining, data processing, and Machine Learning (ML) methods. Utilizing classification model cross-validation criteria, including accuracy, AUC, and ROC, the prediction performance of the ML models is assessed. Welch Unpaired t-test was used to ascertain the statistical significance of the datasets. Text mining framework from the Electronic Medical Records (EMR) made it easier to separate the blood profile data and identify MBC patients. Monocytes revealed a noticeable mean difference between MBC patients as compared to healthy individuals. The accuracy of ML models was dramatically improved by removing outliers from the blood profile data. A Decision Tree (DT) classifier displayed an accuracy of 83% with an AUC of 0.87. Next, we deployed DT classifiers using Flask to create a web application for robust diagnosis of MBC patients. Taken together, we conclude that ML models based on blood profile data may assist physicians in selecting intensive-care MBC patients to enhance the overall survival outcome.

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Section: Discussionmentioning

confidence: 99%

Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms

Botlagunta

Bala

et al. 2023

Sci Rep

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“…These results will be complemented with DisGENET data, a repository of disease associations, and a recent bioinformatics analysis integrating all differentially expressed lipids identified in tumor tissue and serum samples from patients to improve VOSviewer's limited term selection. Later, the results can be integrated with gene expression data from the Gene Expression Omnibus database to correlate gene and protein levels (257). Additional lipid tools and resources for bioinformatic analysis are listed in Table 3.…”

Section: Overall View On Lipid Analysismentioning

confidence: 99%

Ceramides and Ceramide Synthases in Cancer: Focus on Apoptosis and Autophagy

Alizadeh¹,

Rosa²,

Weng³

et al. 2023

Preprint

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Different studies corroborate a role for ceramide synthases and their downstream products, ceramides, in modulation of apoptosis and autophagy in the context of cancer. These mechanisms of regulation, however, appear to be context dependent in terms of ceramides’ fatty acid chain length, subcellular localization, and the presence or absence of their downstream targets. Our current understanding of the role of ceramide synthases and ceramides in regulation of apoptosis and autophagy could be harnessed to pioneer the development of new treatments to activate or inhibit a single type of ceramide synthase, thereby regulating the apoptosis induction or cross talk of apoptosis and autophagy in cancer cells. Moreover, the apoptotic function of ceramide suggests that ceramide analogues can pave the way for the development of novel cancer treatments. Therefore, in the current review paper we discuss the impact of ceramide synthases and ceramides in regulation of apoptosis and autophagy in context of different types of cancers. We also briefly introduce the latest methods to analyze the lipids in biological samples. Finally, we discuss the drug development strategies focusing on the ceramide synthases and ceramides as future therapeutic approaches in cancer therapy.

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“…A recent study investigated the possibility of exploring novel biomarkers through metabolic profiling of urine [ 168 , 169 ]. Indeed, metabolomics has been applied to different types of samples, including prostate tissue [ 170 ], cell lines [ 171 ], and serum [ 172 ], with the ultimate goal of finding novel diagnostic biomarkers.…”

Section: Identification Of Novel Biomarkers Including Lifestyle-assoc...mentioning

confidence: 99%

The Impact of Lifestyle on Prostate Cancer: A Road to the Discovery of New Biomarkers

Leitão

Matos

Roque

et al. 2022

JCM

Self Cite

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Prostate cancer (PCa) is one of the most common cancers among men, and its incidence has been rising through the years. Several risk factors have been associated with this disease and unhealthy lifestyles and inflammation were appointed as major contributors for PCa development, progression, and severity. Despite the advantages associated with the currently used diagnostic tools [prostate-specific antigen(PSA) serum levels and digital rectal examination (DRE)], the development of effective approaches for PCa diagnosis is still necessary. Finding lifestyle-associated proteins that may predict the development of PCa seems to be a promising strategy to improve PCa diagnosis. In this context, several biomarkers have been identified, including circulating biomarkers (CRP, insulin, C-peptide, TNFα-R2, adiponectin, IL-6, total PSA, free PSA, and p2PSA), urine biomarkers (PCA3, guanidine, phenylacetylglycine, and glycine), proteins expressed in exosomes (afamin, vitamin D-binding protein, and filamin A), and miRNAs expressed in prostate tissue (miRNA-21, miRNA-101, and miRNA-182). In conclusion, exploring the impact of lifestyle and inflammation on PCa development and progression may open doors to the identification of new biomarkers. The discovery of new PCa diagnostic biomarkers should contribute to reduce overdiagnosis and overtreatment.

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Integration of Automatic Text Mining and Genomic and Proteomic Analysis to Unravel Prostate Cancer Biomarkers

Cited by 8 publications

References 59 publications

Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms

Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms

Ceramides and Ceramide Synthases in Cancer: Focus on Apoptosis and Autophagy

The Impact of Lifestyle on Prostate Cancer: A Road to the Discovery of New Biomarkers

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