Using publicly available datasets to identify population-based transcriptomic landscape contributing to the aggressiveness of breast cancer in young women

Abstract

Introduction: Although the risk of breast cancer increases with advancing age, some regions have larger number of young breast cancer patients (≤45 years-old), such as the Middle East, Eastern Asia, and North Africa, with more aggressive and poorly differentiated tumors. We aimed to conduct an in-silico analysis in an attempt to understand the aggressive nature of early-onset breast cancer, and to identify potential drivers of early-onset breast cancer using gene expression profiling datasets in a population-dependent manner. Methods: Functional genomics experiments data were acquired from cBioPortal database for cancer genomics, followed by the stratification of patients based on the age at representation of breast cancer and race. Differential gene expression analysis and gene amplification status analysis were carried out, followed by hub gene, transcription factor, and signalling pathway identification. Results: PAM50 subtype analysis revealed that young patients (≤45 years-old) had four-fold more basal tumors and worst progression-free survival (median of 101 months), compared with the 45-65 years group (median of 168 months). Fourteen genes were amplified in more than 14% of patients with an early-onset breast cancer. Interestingly, FREM2, LINC00332, and LINC00366 were exclusively amplified in younger patients. Gene expression data from three different populations (Asian, White, and African) revealed a unique transcriptomic profile of young patients, which was also reflected on the PAM50 subtype analysis. Our data indicates a higher tendency of young African patients to develop basal tumors, while young Asian patients are more prone to developing Luminal A tumors. Most genes that were found to be upregulated in younger patients are involved in important signaling pathways that promote cancer progression and metastasis, such as MAPK pathway, Reelin pathway and the PI3K/Akt pathway. Conclusion: This study provides strong evidence that the molecular profile of tumors derived from young breast cancer patients of different populations is unique and may explain the aggressiveness of these tumors, stressing the need to conduct population- based multi-omic analyses to identify the potential drivers for tumorigenesis and molecular profiles of young breast cancer patients.

Keywords: aggressive; amplification; breast cancer; early-onset; genomics; in-silico; metastasis; transcriptomics.

FIGURE 1

The percentage of breast cancer cases by age groups in the UAE, United States, and United Kingdom in 2017. Data obtained from the annual UAE report-national cancer registry 2017, NIH SEER incidence database, and Office for National Statistics (United Kingdom) (Cancer Incidence, 2017).

FIGURE 2

Flowchart diagram of the inclusion and exclusion criteria used in the study, in addition to the methods used to achieve the aims of the study. DEG: differentially expressed genes, PAM50: prediction analysis of microarray 50. Created with BioRender.com.

FIGURE 3

Clinical plot of PAM50 breast cancer subtypes.

FIGURE 4

Overall survival analysis of women with breast cancer of different age groups.

FIGURE 5

Disease free survival analysis of women with breast cancer of different age groups.

FIGURE 6

Amplified genes in women with breast cancer of different age groups.

FIGURE 7

Clinical plot of PAM50 breast cancer subtypes in two different age groups, All populations.

FIGURE 8

Progression-free survival plots of young African patients with Luminal and Basal tumors compared to young White patients with Luminal and Basal tumors.

FIGURE 9

Differentially expressed genes in young vs. older patients of the white population (p < 0.005, student’s t-test). Error bars represent Mean ± SEM.

FIGURE 10

Differentially expressed genes in older vs. young patients of the white population (p < 0.005, student’s t-test). Error bars represent Mean ± SEM.

FIGURE 11

Differentially expressed genes in older and young patients of the Asian population (p < 0.005, student’s t-test). Error bars represent Mean ± SEM.

FIGURE 12

Differentially expressed genes in older and young patients of the Asian population (p < 0.05, student’s t-test). Error bars represent Mean ± SEM.

FIGURE 13

Common biological processes related to DEGs overexpressed in young women of the white populations.