Recent advances of transcriptomics and proteomics in triple‐negative breast cancer prognosis assessment

Abstract Triple‐negative breast cancer (TNBC), a heterogeneous tumour that lacks the expression of oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2), is often characterized by aggressiveness and tends to recur or metastasize. TNBC lacks therapeutic targets compared with other subtypes and is not sensitive to endocrine therapy or targeted therapy except chemotherapy. Therefore, identifying the prognostic characteristics and valid therapeutic targets of TNBC could facilitate early personalized treatment. Due to the rapid development of various technologies, researchers are increasingly focusing on integrating ‘big data’ and biological systems, which is referred to as ‘omics’, as a means of resolving it. Transcriptomics and proteomics analyses play an essential role in exploring prospective biomarkers and potential therapeutic targets for triple‐negative breast cancers, which provides a powerful engine for TNBC’s therapeutic discovery when combined with complementary information. Here, we review the recent progress of TNBC research in transcriptomics and proteomics to identify possible therapeutic goals and improve the survival of patients with triple‐negative breast cancer. Also, researchers may benefit from this article to catalyse further analysis and investigation to decipher the global picture of TNBC cancer.


| INTRODUC TI ON
Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death in women worldwide. 1 Different breast cancer subtypes have distinct biological, morphological, histological, and molecular features and display different therapy responses.
Triple-negative breast cancer (TNBC), a heterogeneous subtype characterized by the absence of oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2), represents 12% to 17% of all breast carcinomas. 2 It is often seen in younger and premenopausal women and more frequently in African-American women. 3 A study has shown that they preferentially metastasize to the brain and have a higher recurrence potential and often worse prognosis than other breast cancer subtypes. 4 Given the lack of specific molecular targets, TNBC treatment is mainly based on surgery and assisted with radiotherapy and chemotherapy. 5 Chemotherapy includes neoadjuvant chemotherapy and postoperative chemotherapy. The effect of long-term chemotherapy is significantly reduced due to therapy resistance, which easily leads to tumour recurrence and distant metastasis. 6,7 Less than 30% of women with metastatic breast cancer will survive five years after initial diagnosis despite systematic chemotherapy and virtually all metastatic TNBC patients eventually die of this disease. 8 Therefore, by analysing the essential characteristics of TNBC and using effective indicators to determine the clinical prognosis, it is possible to find a suitable alternative therapy by exploring the specific treatment targets.
Transcriptomic investigations have been used to investigate promised biomarkers and potential therapeutic targets for human tumours. 9 Microarray analysis helps to measure the gene expression levels via complementary probe hybridization, and a variety of breast cancer-related genes have been found. 10,11 Moreover, the broad utilization of RNA sequencing (RNA-seq) technologies has dramatically expanded our knowledge of breast cancer. 12 Utilizing RNA-seq, we can quantify genes that are expressed at extremely low levels. 13 Proteomics approaches have emerged as a powerful technique for performing protein profiling and discovering novel biomarkers associated with cancer. 14 Targeted proteomics offers new strategies for validating these candidate biomarkers' diagnostic, prognostic, or predictive performance, which can be precisely quantified in a large cohort of clinical samples. There are two types of targeted proteomics approaches: non-MS-based methods that use protein detection antibodies (Western blot, ELISA, immunohistochemistry and reversephase protein array) and MS-based methods (mass spectrometry imaging, targeted proteomics and next-generation proteomics). The rapid advancement of proteomics offers a unique opportunity to investigate the proteome of triple-negative breast cancer further.
Traditionally, four different subtypes of breast cancer have been identified based on the expression of three molecules: the oestrogen receptor (ER), the progesterone receptor (PR) and the human epidermal growth factor receptor 2 (HER2). Triple-negative breast cancer (TNBC), one of these subtypes, is characterized by the absence of all three receptors. Subtypes of breast cancer can be classified according to their molecular characteristics as (1) Basal-like (Triplenegative), (2) Luminal A, (3) Luminal B and (4) HER2-positive, and additionally Normal-like and Claudin-low. 3 Most basal-like tumours are triple-negative breast cancers, but not all.
Our current understanding of breast cancer biology is more comprehensive than ever. Various molecular subtypes of breast cancer have been elucidated using genomic profiling strategies and other major technological discoveries, opening new windows into breast cancer treatment and research. However, the basal-like (Triplenegative) subtype, the most distinct among all the intrinsic subtypes of breast cancer, has not been well characterized. 15 A genomic study has also revealed that the basal-like (Triple-negative) breast cancer subtype is not only distinct among other breast cancer subtypes but also other cancer types. 16 A further subclassification of this aggressive cancer subtype is urgently required to help develop more targeted treatments for TNBC patients with better clinical outcomes.
Proteomics and transcriptomics are already impacting TNBC research, and this article reviews the latest prognostic studies of TNBC through proteomics and transcriptomics to better understand TNBC and its potential therapies.

| Sequencing analysis of whole transcriptome
With the deep understanding of genes and the application of highthroughput technology continues to mature, the prognosis can be determined by constructing a gene expression scoring system or typing according to the characteristics of TNBC gene expression profile. 17 At the same time, prognostic indicators can be obtained by sequencing and analysing the whole transcriptome of each subtype of the tumour. Lehmann et al. classify TNBC into seven subtypes based on a comprehensive transcriptomic analysis of 21 data sets for breast cancer. 17 These include two basal-like subtypes (BL1 and BL2), an immunomodulatory subtype (IM), a mesenchymal subtype (M), a mesenchymal stem-like subtype (MSL), a luminal androgen receptor subtype (LAR) and an unstable unclassified set (UNS). Specific genes linked to cell proliferation and DNA damage response are strongly expressed in the BL1 subtype and this subtype preferentially responds to cisplatin and poly (ADP-ribose) polymerase (PARP) inhibitors. The BL2 subtype is enriched with genes associated with growth factor pathways, suggesting that growth factor inhibitors may be effective for the BL2 subtype. The IM subtype has abundant genes involved in immune-mediated reactions, and programmed cell death 1/programmed death-ligand 1 (PD1/PDL1) inhibitors are expected to be a hopeful therapeutic option for this subtype. Both subtypes of M and MSL explicitly express genes relevant to cell motility, cellular differentiation, and growth factor pathways, while the MSL subtype expresses lower proliferation genes than those present in the M subtype. For these two subtypes, the mammalian target of rapamycin (mTOR) inhibitors and targeted epithelial-to-mesenchymal transition (EMT) agents are candidate drugs. The LAR subtype is named for the AR enrichment, and anti-androgen treatments (eg bicalutamide, an AR antagonist) are undergoing clinical trials. 18 Liu et al. analysed the transcriptome data and found four molecular types, which were named FUSCC typing, including immunomodulatory (IM), luminal androgen receptor (LAR), mesenchymal-like (MES), Basal-like and immunosuppressive (BLIS), 19 the first two types are basically consistent with IM and LAR in Lehmann's study. 17 Further analysis of this study found that MES with FUSCC typing indicating these genes might be important prognostic markers in TNBC. 21 Chen et al. validated HORMAD1 mRNA levels were significantly upregulated in both breast cancer cell lines and clinical samples using qRT-PCR, and survival analysis suggested that its high expression was associated with worse RFS. 22 The conclusion was also confirmed by immunohistochemical detection and clinicopathological information analysis. Li et al. analysed the CCR7 gene amplification and mRNA expression levels and found that the prognosis of patients with positive CCR7 expression was significantly better than those with negative expression in TNBC patients. 23 Although these studies lack uniform standards, they can be used as a clinical prognostication of tumour therapy to some extent. It is worth noting that ERRLR01 and MALAT1 are also regulated by the E2 hormone signalling pathway and participate in TNBC migration and invasion. 30,31 One study identified potential core lncRNAs in TNBC by co-expression networks and found that the patients with low expression of potential core lncRNA-RMST (rhabdomyosarcoma 2) had worse overall survival. 32 34 Other studies also showed that LncRNA AWPPH and lncRNA POU3F3 might promote cancer cells' proliferation in triple-negative breast cancer, while LncRNA NEF overexpression inhibited the migration and invasion of TNBC cells. [35][36][37] MicroRNA (miRNA), a class of non-coding small RNA that posttranscriptionally regulates gene expression, plays a vital role in cell proliferation, differentiation and apoptosis by targeting multiple downstream genes. 38 The previous study with relatively small numbers of patients has specifically evaluated tumour miRNA markers in TNBC and showed that miRNAs play a crucial role in cellular growth and proliferation, cellular movement and migration and Extra Cellular F I G U R E 1 Model of E2-induced breast cancer cell migration via upregulation of HOTAIR expression. Reprinted from. 29 Copyright © 2015 J Transl Med Matrix degradation. 39 Table 1 lists the recent studies of MicroRNAs in triple-negative breast cancer.

| Non-coding RNA sequencing analysis
Considering that the overall miRNA score can better define the tumour prognosis than the single miRNA index, some studies have tried to establish a feature score system to evaluate the tumour prognosis. They systematically evaluated 57 metastasis-related miRNAs in tumour tissue in 456 TNBC patients and proved that the expression levels of miR-374b-5p, miR-27b-3p, miR-126-3p and miR-218-5p in tumour tissues predict TNBC outcomes. 40 Avery-Kiejda et al. have identified 27 miRNAs related to the metastatic capabilities of TNBC cells. 41 The expression of some miRNAs in TNBC is upregulated and may serve to promote the growth and/or invasion of TNBC cells.
Therefore, this type of miRNAs is referred to as oncomiRs, including miR-146a/146b, 42 miR-181a/181b, 43 miR-155, 44 miR-21, 45 miR-720 46 and miR-455. 47  TNBC patients and demonstrated that miR-128 was able to inhibit the proliferation of TNBC cells. 48 Jang et al. proved that miR-9 overexpression was significantly associated with poor disease-free survival and distant metastasis-free survival (DMFS) in TNBC, while the high level of miR-155 expression showed significant association with better DMFS. 49  without the 5′-cap structure and the 3′-poly-A tail. 66 Some studies have revealed that circRNAs are associated with TNBC and can be the potential prognosis marker for TNBC. He et al. showed that circG-FRA1 functions as a competing endogenous RNA (ceRNA) to regulate GFRA1 expression through sponging miR-34a to promote proliferation and inhibit apoptosis in TNBC, which correlated with reduced survival of patients. 67 Yang et al. demonstrated that circAGFG1 could sponge miR195-5p to modulate CCNE1 expression, leading to tumorigenesis and development of TNBC ( Figure 3). 68 Wang et al. revealed that circ-UBAP2 (hsa_circ_0001846) was markedly upregulated in TNBC, and its expression was associated with unfavourable prognosis. They noticed that circ-UBAP2 was able to sponge miRNA-661 to increase the expression of the oncogene MTA1, serving as a promising therapeutic target for TNBC patients. 69 Tang 71 Its expression is closely related to lymph node metastasis and advanced clinical stage and function as an independent risk factor for OS in breast cancer patients. The study further confirmed that ESRP, which promotes circANKS1B synthesis, is regulated by USF1, suggesting that cir-cRNA and mRNA are not linearly regulated.

| Single-cell RNA sequencing analysis
Sunny Wu et al. used a single-cell RNA sequencing strategy to sequence nearly 24,300 single cells from five patients with triplenegative breast cancer. 72 By differing gene expression patterns, they identified two cancer-associated fibroblasts (CAF) as well as two subpopulations of perivascular-like (PVL) cells. After delving into these stromal clusters, they began to sort out certain microenvironmental changes that might affect tumour growth or treatment response. In particular, the researchers noted that an inflammatory cancer-associated fibroblast (iCAF) releases the chemokine CXCL12, a signalling molecule that inhibits the anti-tumour activity of T cells.
They believe that this promises to point the way to enhanced immunotherapy for triple-negative breast cancer.
Mihriban et al. also sequenced >1500 cells from six female patients with primary triple-negative breast cancer using single-cell RNA sequencing in order to investigate the underlying biology of triple-negative breast cancer. 73 Through computational analysis of F I G U R E 2 lncRNAs that by complementarity of bases succeed in matching or sequestering sequences of small non-coding RNAs, such as miRNAs, are controlling bioavailability of miRNAs, vs. lncRNAs themselves, with the functional biological repercussions at cellular or physiological level. RNA-induced silencing complex RISC. Reprinted from. 34

| PROTEOMIC ANALYS IS OF TNBC
The difference between RNA and protein expression levels prevents functional biological characteristics from fully reflecting gene expression characteristics. Therefore, functional proteomics analysis is used as supplementary information, and the integration of genomic and transcriptome data is conducive to the discovery of new targets. 76 Intrinsically, proteins are more complex, dynamic, and reflect biological function more closely than genes. The requirements for protein analysis were also exemplified by shortcomings in the bioinformatics capacity to predict gene products' presence and function. The  Table 2.

| Quantitative mass spectrometry-based proteomic strategies in TNBC
Mass spectrometry-based technologies offer a unique opportunity to profile cancer proteomes accurately and rapidly in terms of mass precision, sequencing speed, resolution, power and cost-efficiency. 86 Powerful mass spectrometers like Q-TOF, TOF / TOF, Q-OT and Q-Exactive have high resolution, sensitivity and sub-ppm mass accuracy, making them suitable for shotgun proteomics approaches to quantify hundreds to thousands of proteins in a biological sample. 87,88 The dynamic changes in cellular proteome abundance have a significant influence on different life processes. For example, the occurrence and development of many diseases are often accompanied by abnormal expression of specific proteins. Quantitative proteomics is the accurate quantification and identification of all proteins expressed in a genome or all proteins in a complex mixed system. The current quantitative proteomics technology is primarily divided into labelling (Label) and non-labelling (Label Free) quantitative strategies, in which the labelling strategy is divided into in vivo labelling (such as SILAC 89 ), and in vitro labelling (such as iTRAQ 90 and TMT mark 91 ).

| Stable labelling approaches
The first global in-depth proteomic analysis of TNBC molecular characteristics identified 12,000 distinct proteins whose expression patterns could discriminate between TNBC subtypes. This study also elucidated the specific TNBC pathway for metastasizing, adherence and angiogenesis. 92 Different expression signatures for three proteins desmoplakin (DP), thrombospondin-1 (TPS1) and tryptophanyl-tRNA synthetase (TrpRS) were discovered for relapse and non-relapse TBNC tumours using an iTRAQ labelling-based proteomic approach. 93

| CON CLUS I ON AND PER S PEC TIVE
The development of high-profile sequencing technologies and computational analysis tools, including transcriptomic and proteomic technologies, has enhanced our understanding of TNBC.
Transcriptomic analyses have provided a considerable amount of information on the gene expression patterns in breast cancer.
For clinical applications, transcriptomic can be employed to classify TNBC into unique molecular subtypes and to propose reliable therapeutic targets, and large-scale approaches such as proteomics can be used to decipher the global picture of TNBC cancer biology.
At present, the transcriptomic and proteomic prognostic studies on TNBC can be basically divided into two categories: one is to establish a scoring model to identify prognosis; the other is to study the prognostic relevance through a single indicator involved in signalling pathways. In the transcriptome study, considering the interaction among mRNA, lncRNA, miRNA and circRNA from multiple perspectives, researchers attempted to establish a ceRNA regulatory network to evaluate the prognostic characteristics of tumours. 20,33,103 However, due to big data modelling, analysis tool selection, tumour heterogeneity and other reasons, the final feature scoring models are different. Adopting uniform standards and developing specific analytical data sets and tools can help balance cluttered data to a certain extent, particularly for cross-omics research.
In proteomic studies, the integration and analysis of gene data with protein-related information based on immunohistochemistry, lc-ms / MS and other technologies are one of the most important means of proteomics research, which helps discover potential targets, signalling pathways within or between tumours, and overall tissue biological characteristics.
Multi-omic data's emergence has become a routine in cancer studies. However, the challenge is increasingly difficult to assimilate the rapidly growing number of 'big data'. Intelligent utilization and management of these data require massive computational resources and accurate statistical methodologies to unearth the hidden links among different sub-components.
The multiple layers of cancer biology are detailed in multi-omic data, but our perception of the nature of cancer seems confusing with the endless complexity. Enormous efforts must be made to acquire a considerable quantity of multi-omic data indicating the diverse biological signatures of the development of TNBC.
The treatment of TNBC remains to be challenging since its poor patient outcomes and few therapeutic targets. A better understanding of TNBC carcinogenesis is a prerequisite for more sophisticated TNBC subtyping and the development of personalized treatment options.
This paper reviews the recent advances in TNBC research through transcriptomics and proteomics and understands that the power of proteomics and transcriptomics can be used for decoding the complexity of TNBC, which will develop more effective clinical interactions. It is high time we take advantage of these abundant resources to unveil TNBC, and we hope that this intractable cancer will be precisely targeted soon.

ACK N OWLED G EM ENTS
Not applicable.

CO N FLI C T O F I NTE R E S T S
The authors declare that they have no competing interests.