Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis

Introduced the ITGB1-DT as a novel biomarker associated with five potential drugs using bioinformatics analysis of breast cancer proteomics data and RT-PCR

BACKGROUND

Breast cancer (BC) has been identified as a significant contributor to the rising number of female cancer deaths. As, it has become clear that breast cancer development depends on the interplay of several biological factors against a single molecule. This research aimed to use proteomics to gain a regulatory and metabolic understanding of BC pathophysiology.

METHOD

For the study, a breast cancer proteomics dataset was downloaded from ProteomeXchange and then analyzed by employing MaxQuant and Perseus. Functional enrichment analysis through Metascape and Cytoscape software showed DEPs related biomedical phenomena with potential abruption. The expression of selected lncRNA in terms of the highest connectivity parameters was then quantitatively assessed through RT-PCR in 30 tumor tissues of breast cancer patients, as compared to the adjacent healthy ones.

RESULT

The results indicated that among the 3048 identified proteins, 1149 were differentially expressed, which could be mainly enriched in several key terms. Furthermore, the obtained findings revealed that ITGB1-DT was significantly overexpressed in tumor tissues. Moreover, we found five potential compounds that could be attributed to ITGB1-DT targets (ATN-161, Firategrast, SB-683698, dabigatran-etexilate, and tranexamic-acid).

CONCLUSION

These analyses proposed that ITGB1-DT could be employed as a differentiated factor to identify breast tumor tissues in healthy samples. Besides this, Firategrast could be introduced as a potential remedial agent for breast cancer patients. Overall, from the analysis of a proteomics dataset, an integrative map was generated, and a novel biomarker that may have been implicated in the early detection of BC was introduced.

Interplay between LncRNAs and microRNAs in Breast Cancer

(1) Although long noncoding RNAs (lncRNAs) are known to be precursors of microRNAs (miRNAs), they frequently act as competing endogoneous RNAs (ceRNAs), yet still their interplay with miRNA is not well known. However, their interaction with miRNAs may result in the modulation of miRNA action. (2) To determine the contribution of these RNA molecules in tumor resistance to chemotherapeutic drugs, it is essential to consider not only the oncogenic and tumor suppressive function of miRNAs but also the impact of lncRNAs on miRNAs. Therefore, we performed an extensive search in different databases including PubMed. (3) The present study concerns the interplay between lncRNAs and miRNAs in the regulatory post-transcriptional network and their impact on drugs used in the treatment of breast cancer. (4) Consideration of this interplay may improve the search for new drugs to circumvent chemoresistance.

Topology-enhanced molecular graph representation for anti-breast cancer drug selection

Background

Breast cancer is currently one of the cancers with a higher mortality rate in the world. The biological research on anti-breast cancer drugs focuses on the activity of estrogen receptors alpha (ER\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α), the pharmacokinetic properties and the safety of the compounds, which, however, is an expensive and time-consuming process. Developments of deep learning bring potential to efficiently facilitate the candidate drug selection against breast cancer.

Methods

In this paper, we propose an Anti-Breast Cancer Drug selection method utilizing Gated Graph Neural Networks (ABCD-GGNN) to topologically enhance the molecular representation of candidate drugs. By constructing atom-level graphs through atomic descriptors for each distinct compound, ABCD-GGNN can topologically learn both the implicit structure and substructure characteristics of a candidate drug and then integrate the representation with explicit discrete molecular descriptors to generate a molecule-level representation. As a result, the representation of ABCD-GGNN can inductively predict the ER\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α, the pharmacokinetic properties and the safety of each candidate drug. Finally, we design a ranking operator whose inputs are the predicted properties so as to statistically select the appropriate drugs against breast cancer.

Results

Extensive experiments conducted on our collected anti-breast cancer candidate drug dataset demonstrate that our proposed method outperform all the other representative methods in the tasks of predicting ER\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α, and the pharmacokinetic properties and safety of the compounds. Extended result analysis demonstrates the efficiency and biological rationality of the operator we design to calculate the candidate drug ranking from the predicted properties.

Conclusion

In this paper, we propose the ABCD-GGNN representation method to efficiently integrate the topological structure and substructure features of the molecules with the discrete molecular descriptors. With a ranking operator applied, the predicted properties efficiently facilitate the candidate drug selection against breast cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04913-6.

An Eleven-microRNA Signature Related to Tumor-Associated Macrophages Predicts Prognosis of Breast Cancer

The dysregulation of microRNAs (miRNAs) has been known to play important roles in tumor development and progression. However, the understanding of the involvement of miRNAs in regulating tumor-associated macrophages (TAMs) and how these TAM-related miRNAs (TRMs) modulate cancer progression is still in its infancy. This study aims to explore the prognostic value of TRMs in breast cancer via the construction of a novel TRM signature. Potential TRMs were identified from the literature, and their prognostic value was evaluated using 1063 cases in The Cancer Genome Atlas Breast Cancer database. The TRM signature was further validated in the external Gene Expression Omnibus GSE22220 dataset. Gene sets enrichment analyses were performed to gain insight into the biological functions of this TRM signature. An eleven-TRM signature consisting of mir-21, mir-24-2, mir-125a, mir-221, mir-22, mir-501, mir-365b, mir-660, mir-146a, let-7b and mir-31 was constructed. This signature significantly differentiated the high-risk group from the low-risk in terms of overall survival (OS)/ distant-relapse free survival (DRFS) (p value < 0.001). The prognostic value of the signature was further enhanced by incorporating other independent prognostic factors in a nomogram-based prediction model, yielding the highest AUC of 0.79 (95% CI: 0.72−0.86) at 5-year OS. Enrichment analyses confirmed that the differentially expressed genes were mainly involved in immune-related pathways such as adaptive immune response, humoral immune response and Th1 and Th2 cell differentiation. This eleven-TRM signature has great potential as a prognostic factor for breast cancer patients besides unravelling the dysregulated immune pathways in high-risk breast cancer.