Gene Expression Profiling in True Interval Breast Cancer Reveals Overactivation of the mTOR Signaling Pathway

Rojo, Federico; Domingo, Laia; Sala, María; Zazo, Sandra; Chamizo, Cristina; Menéndez, Sílvia; Arpí, Oriol; Corominas, Josep; Bragado, Rafael; Servitja, Sònia; Tusquets, Ignasi; Nonell, Lara; Macià, Francesc; Martínez, Juan; Rovira, Ana; Albanell, Joan; Castells, Xavier

doi:10.1158/1055-9965.epi-13-0761

Cited by 12 publications

(11 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The only similar work for gene expression differences between screen-detected and interval cancers by Rojo and colleagues was performed in 10 samples (33). The authors found the mTOR signaling pathway to be significantly upregulated in interval cancers and concluded that this pathway may mediate their aggressiveness.…”

Section: Discussionmentioning

confidence: 99%

Molecular Differences between Screen-Detected and Interval Breast Cancers Are Largely Explained by PAM50 Subtypes

Ivansson

Klevebring

et al. 2017

Clinical Cancer Research

View full text Add to dashboard Cite

Interval breast cancer is of clinical interest, as it exhibits an aggressive phenotype and evades detection by screening mammography. A comprehensive picture of somatic changes that drive tumors to become symptomatic in the screening interval can improve understanding of the biology underlying these aggressive tumors. Initiated in April 2013, Clinical Sequencing of Cancer in Sweden (Clinseq) is a scientific and clinical platform for the genomic profiling of cancer. The breast cancer pilot study consisted of women diagnosed with breast cancer between 2001 and 2012 in the Stockholm/Gotland regions. A subset of 307 breast tumors was successfully sequenced, of which 113 were screen-detected and 60 were interval cancers. We applied targeted deep sequencing of cancer-related genes; low-pass, whole-genome sequencing; and RNA sequencing technology to characterize somatic differences in the genomic and transcriptomic architecture by interval cancer status. Mammographic density and PAM50 molecular subtypes were considered. In the univariate analyses, , and were significantly more frequently mutated in interval cancers than in screen-detected cancers. Acquired somatic copy number aberrations with a frequency difference of at least 15% between the two groups included gains in 17q23-q25.3 and losses in 16q24.2. Gene expression analysis identified 447 significantly differentially expressed genes, of which 120 were replicated in an independent microarray dataset. After adjusting for PAM50, most differences were no longer significant. Molecular differences by interval cancer status were observed, but they were largely explained by PAM50 subtypes. This work offers new insights into the biological differences between the two tumor groups. .

show abstract

Section: Discussionmentioning

confidence: 99%

Molecular Differences between Screen-Detected and Interval Breast Cancers Are Largely Explained by PAM50 Subtypes

Ivansson

Klevebring

et al. 2017

Clinical Cancer Research

View full text Add to dashboard Cite

show abstract

“…Previous studies have reported differences in molecular characteristics between ICs and screening-detected breast cancers. Rojo et al, (2014) showed differential expression profiles both at gene and protein levels, especially mTOR signaling, which is upregulated in true interval cancers, suggesting that this pathway may mediate aggressiveness. first reported that these two types of breast cancer may have unique underlying biology based on a 77 single-nucleotide polymorphism risk score However, a recent, well-designed study indicated molecular differences between less aggressive screening-detected breast cancer and more aggressive ICs.…”

Section: Discussionmentioning

confidence: 99%

Five-Year Overall Survival of Interval Breast Cancers is Better than Non- Interval Cancers from Korean Breast Cancer Registry

Lee

Kim

Cho

et al. 2019

Asian Pac J Cancer Prev

View full text Add to dashboard Cite

Objective: Interval breast cancer (IC) is a limitation of breast cancer screening. We investigated data from a large scaled breast cancer dataset of patients with breast cancer who underwent breast cancer screening in order to recapitulate the overall survival (OS) of patients with ICs compared to those with non-ICs. Methods: A total of 27,141 patients in the Korean breast cancer registry with breast cancer who had ever participated in biannual national breast cancer screening programs between 2009 and 2013 were enrolled. We compared the social, pregnancy-associated, and pathologic characteristics between the IC and non-IC groups and identified the significant prognostic factors for OS. Results: The proportion of ICs was 1.3% (370/27,141) in this study population. ICs were correlated with age 45-55 years at diagnosis, higher levels of education, early menopause (<50 years), hormone replacement therapy, specific provinces (Kangwon, Kyungnam, Jeju, and Dae-jeon), and family history of breast cancer. Low-to-intermediate nuclear grade, early stage (stage 0-I), and low Ki-67 level were also correlated with IC proportion. Non-ICs were associated with an increased risk of five-year mortality (hazard ratio [HR] 7.4; 95% confidence interval [CI]:1.85-29.66; p = 0.005) compared to ICs. Lymph node metastasis, residence (Kyung-nam province), low education status, high histologic grade, and asymptomatic cancers increased the HR of five-year OS. Conclusion: ICs occurred unequally in specific province and relatively high-educated women in Korea. They were also diagnosed with early-stage breast cancer with a favorable recurrence risk, and their outcome was better than those of patients with other breast cancers in breast cancer screening.

show abstract

“…Microarray-based gene expression pro ling is widely used in biomedical researchespecially in chronic non-communicable diseases including neurological diseases [8],cardiovascular disease [9], diabetes [10] and cancer [11].In previous studies, most of microarray analysis methods focus on the comparison between normal and diseased conditions [12]. Identi cation of differential gene expression is a widely used analytical strategy for screening of potential biomarkers in diseased state.Several studies microarray-based gene expression have been carried out, however, single microarray dataset have high false positive rates.Therefore, integration of multiple datasets would signi cantly increase the reliability and sensitivity of ndings and reduce false positives.Gene co-expression network analysis, the recent systems biology approach is widely used for analysis of correlation patterns among microarray analysis [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Hub Genes of Stroke Identified by Weighted Gene Co-expression Network Analysis

Zhai

et al. 2020

Preprint

View full text Add to dashboard Cite

BACKGROUD: Microarray-based gene expression profiling is widely used in biomedical research. Weighted gene co-expression network analysis (WGCNA) links microarray data directly to clinical traits and identifies rules for predicting pathological stage and prognosis of disease.WGCNA is useful in understandingmany biological processes. Stroke is a common disease worldwide, however, molecular mechanisms of its pathogenesis are largely unknown. The aim of this study was to construct gene co-expression networks for identification of key modules and hub genes associated with stroke pathogenesis.METHODS: Gene microarray expression profiles of stroke samples were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by the limma package in R software. WGCNA was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify key modules and hub genes. Subsequently, functional enrichment analyses were performed. Further, receiver operating characteristic (ROC) curve analysis was carried out to validate expression of hub genes and literature validation was performed as well.RESULTS: A total of 11,747 most variant genes were used for co-expression network construction. Pink and yellow modules were significantly correlated to stroke pathogenesis. Functional enrichment analysis showed that the pink module was mainly involved in regulation of neuron regeneration, and repair of DNA damage.On the other hand, yellow module was mainly enriched in ion transport system dysfunction which was correlated with neuron death. A total of eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) were identified and validated at transcriptional levels and through existing literature.CONCLUSION: The eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) identified in the study are potentialbiomarkers and therapeutic targets for effective diagnosis and treatment of stroke.

show abstract

Gene Expression Profiling in True Interval Breast Cancer Reveals Overactivation of the mTOR Signaling Pathway

Cited by 12 publications

References 41 publications

Molecular Differences between Screen-Detected and Interval Breast Cancers Are Largely Explained by PAM50 Subtypes

Molecular Differences between Screen-Detected and Interval Breast Cancers Are Largely Explained by PAM50 Subtypes

Five-Year Overall Survival of Interval Breast Cancers is Better than Non- Interval Cancers from Korean Breast Cancer Registry

Hub Genes of Stroke Identified by Weighted Gene Co-expression Network Analysis

Contact Info

Product

Resources

About