Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends

Jurca, Gabriela; Addam, Omar; Aksaç, Alper; Gao, Shang; Özyer, Tansel; Demetrick, Douglas J.; Alhajj, Reda

doi:10.1186/s13104-016-2023-5

Cited by 27 publications

(19 citation statements)

References 35 publications

(63 reference statements)

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“…S2) while the non-perturbed edges did not contain proteins that could predict the overall patient survival. To understand the roles these proteins play in KICH tumorigenesis, we performed text mining in PubMed using the protein identifiers plus the term cancer for each protein involved in significant edgetic perturbations 32 . The results for each individual cancer type are summarized in the Text S1, with the corresponding images available in Figs.…”

Section: Resultsmentioning

confidence: 99%

Edgetic perturbation signatures represent known and novel cancer biomarkers

Kataka

Zaucha

Frishman

et al. 2020

Sci Rep

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isoform switching is a recently characterized hallmark of cancer, and often translates to the loss or gain of domains mediating protein interactions and thus, the re-wiring of the interactome. Recent computational tools leverage domain-domain interaction data to resolve the condition-specific interaction networks from RnA-Seq data accounting for the domain content of the primary transcripts expressed. Here, we used The Cancer Genome Atlas RNA-Seq datasets to generate 642 patient-specific pairs of interactomes corresponding to both the tumor and the healthy tissues across 13 cancer types. The comparison of these interactomes provided a list of patient-specific edgetic perturbations of the interactomes associated with the cancerous state. We found that among the identified perturbations, select sets are robustly shared between patients at the multi-cancer, cancer-specific and cancer subtype specific levels. Interestingly, the majority of the alterations do not directly involve significantly mutated genes, nevertheless, they strongly correlate with patient survival. The findings (available at edgeexplorer: "http://webclu.bio.wzw.tum.de/edgeexplorer") are a new source of potential biomarkers for classifying cancer types and the proteins we identified are potential anti-cancer therapy targets.Cancer involves the accumulation of somatic mutations 1 and epigenetic modifications 2 , which drive the cells into the malignant state. Recurrent mutations implicated in tumorigenesis affect highly connected proteins within the protein interaction network 3,4 and are enriched at the interaction interfaces 5,6 and phosphorylation sites 7 signifying their role in rewiring protein interactions 8 . For this reason, cancer has been described as the disease of the interactome 9 . Indeed, the network of protein-protein interactions (PPI) has repeatedly allowed for the extraction of molecular features predictive of various phenotypic traits relevant to cancer -the so-called disease biomarkers 10 . For example, Cui et al. have identified putative interaction-disrupting mutations occurring at the interfaces of protein complexes and demonstrated that their presence is prognostic of poor survival 11 . In another study, Li et al. 12 developed the "OncoPPI" network of protein-protein interactions (PPIN) relevant to lung cancer, identifying biomarkers that can inform therapeutic decisions according to the drug sensitivity in certain conditions 12 . Nevertheless, the physical disruption of interaction sites by somatic mutations is only one mode of perturbing the interactome. Another relevant cellular process is regulating the expression (and thereby the local molecular concentration) of the interacting proteins 13 ; this has been utilized in mining the network of protein-protein interactions to identify modules of differentially expressed genes serving as robust biomarkers indicative of breast cancer metastasis 14 or stratifying patients from several breast cancer subtypes 15 . Furthermore, the phenomenon of "isoform switching", i.e. altering t...

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Section: Resultsmentioning

confidence: 99%

Edgetic perturbation signatures represent known and novel cancer biomarkers

Kataka

Zaucha

Frishman

et al. 2020

Sci Rep

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“…EEF1D is involved in breast cancer (Jurca et al, 2016). In fact, was detected an EEF1D gene copy number gain in BT483, EFM19, HCC1143, HCC1395, HCC1569, HCC1806, HCC1937, HCC2157, HCC2218, HDQP1, MDAMB436 and UACC893 breast cancer cell lines and in about 10% of breast invasive carcinoma donor samples (http://www.oasis-genomics.org/).…”

Section: Breast Cancermentioning

confidence: 99%

EEF1D (eukaryotic translation elongation factor 1 delta)

Cristiano¹

2020

Atlas of Genetics and Cytogenetics in Oncology and Haematology

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“…A recent study has begun to use structured formats of breast imaging examinations and data mining techniques to improve the detection and screening of breast cancer [27]. Others have integrated text mining, data mining and network analysis to identify genetic biomarkers of breast cancer [28]. These different research topics regarding the use of distinct data sets have prompted scholars to investigate breast cancer whereby people are almost likely to develop diseases.…”

Section: Data Analytics For Identifying Risks Of Breast Cancermentioning

confidence: 99%