research paper on cancer biotechnology

• Open access
• Published: 04 December 2020

RNA sequencing: new technologies and applications in cancer research

• Mingye Hong 1   na1 ,
• Shuang Tao 2   na1 ,
• Ling Zhang 3 ,
• Li-Ting Diao 2 ,
• Xuanmei Huang 1 ,
• Shaohui Huang 1 ,
• Shu-Juan Xie 2 ,
• Zhen-Dong Xiao 2 &
• Hua Zhang   ORCID: orcid.org/0000-0001-9731-2737 1  

Journal of Hematology & Oncology volume  13 , Article number:  166 ( 2020 ) Cite this article

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Over the past few decades, RNA sequencing has significantly progressed, becoming a paramount approach for transcriptome profiling. The revolution from bulk RNA sequencing to single-molecular, single-cell and spatial transcriptome approaches has enabled increasingly accurate, individual cell resolution incorporated with spatial information. Cancer, a major malignant and heterogeneous lethal disease, remains an enormous challenge in medical research and clinical treatment. As a vital tool, RNA sequencing has been utilized in many aspects of cancer research and therapy, including biomarker discovery and characterization of cancer heterogeneity and evolution, drug resistance, cancer immune microenvironment and immunotherapy, cancer neoantigens and so on. In this review, the latest studies on RNA sequencing technology and their applications in cancer are summarized, and future challenges and opportunities for RNA sequencing technology in cancer applications are discussed.

Cancer remains one of the major malignant diseases that endangers human life and health and comprises complex biological systems that require accurate and comprehensive analysis. Since the first appearance of high-throughput sequencing in 2005 [ 1 ], it has become possible to understand life activities at the molecular level and to conduct detailed research to elucidate the genome and transcriptome. As an essential part of high-throughput sequencing, RNA sequencing (RNA-seq), especially single-cell RNA sequencing (scRNA-seq), provides biological information on a single tumor cell, analyzes the determinants of intratumor expression heterogeneity and identifies the molecular basis of formation of many oncological diseases [ 2 , 3 ]. Thus, RNA sequencing offers invaluable insights for cancer research and treatment. With the advent of the era of precision medicine, RNA sequencing will be widely used for research on many different types of cancer. This review summarizes the history of the development of RNA sequencing and focuses on the latest studies of RNA sequencing technology in cancer applications, especially single-cell RNA sequencing and spatial transcriptome sequencing. In addition, we provide a general introduction to the current bioinformatics analysis tools used for RNA sequencing and discuss future challenges and opportunities for RNA sequencing technology in cancer applications.

The development of RNA sequencing technologies

It was not until 1953 when Watson and Crick proposed the double-helix structure did people truly realize at the molecular level that the essence of life is the result of gene interactions [ 4 ]. The continuous development of RNA sequencing has ushered transcriptome analysis into a new era, with higher efficiency and lower cost. The timeline of RNA sequencing technologies is shown in Fig.  1 .

The development timeline of RNA sequencing technologies

The first-generation sequencing technology is also called Sanger sequencing. The chain termination method was initiated by Sanger in 1977, followed by the chemical degradation method developed by Maxam and Gilbert [ 5 , 6 ]. The same year, Sanger determined the 5368 bp genome of phage φX174, which is the first DNA genome sequenced [ 7 ]. The DNA microarray has aided significant progress in many fields since it was first introduced. However, microarrays require prior knowledge of gene sequences and are unable to identify novel gene expression [ 8 ]. After the first high-throughput sequencing platform appeared in 2005 [ 1 ], multiple next-generation sequencing platforms followed (Table 1 , Figs.  2 , 3 ). The accuracy and reproducibility among different platforms depended on several factors, including the inherent features of the platform and the corresponding analysis pipelines [ 9 , 10 ]. Pyrosequencing that was no longer supported after 2016, developed by 454 Life Sciences, used a “sequencing by synthesis” method [ 1 , 11 , 12 , 13 ]. The ion torrent sequencing platform is also based on the “sequencing by synthesis” method, which outperforms pyrosequencing with respect to sensitivity. SOLiD (Sequencing by Oligonucleotide Ligation and Detection) exhibits high accuracy, as each base is sequenced twice, but the read length is short [ 11 , 12 , 13 ]. DNBS (DNA nanoball sequencing) enables large collection of DNA nanoballs for simultaneous sequencing. Illumina-based sequencing technology represents a “reversible terminator sequencing” method. High-throughput sequencing has the advantage of fast speed, low sequencing cost and high accuracy, otherwise known as next-generation sequencing (NGS). Compared to microarray, it can detect unknown gene expression sequences but is time intensive [ 14 ].

RNA extraction and template preparation before RNA-sequencing. RNA was extracted from tissues, and after fragmentation, fragmented DNA molecules were converted into cDNA by reverse transcription then amplified by emulsion PCR or bridge PCR to prepare sequencing library

Three kinds of sequencing methods. These methods contain sequencing by synthesis, sequencing by reversible terminator and sequencing by ligation. And their different mechanisms are shown in detail

In addition to NGS, there is third-generation sequencing, which allows for long-read sequencing of individual RNA molecules [ 15 ]. Single-molecule RNA sequencing enables the generation of full-length cDNA transcripts without clonal amplification or transcript assembly. Thus, third-generation sequencing is free from the shortcomings generated by PCR amplification and read mapping. It can greatly reduce the false positive rate of splice sites and capture the diversity of transcript isoforms [ 15 ]. Single-molecule sequencing platforms comprise Pacific Biosciences (PacBio) single-molecule real-time (SMRT) sequencing [ 16 ], Helicos single-molecule fluorescent sequencing [ 17 ] and Oxford Nanopore Technologies (ONT) nanopore sequencing [ 18 ]. Furthermore, RNA-seq recently evolved from bulk sequencing to single-cell sequencing. Single-cell RNA sequencing was first published in 2009 to profile the transcriptome at single-cell resolution [ 19 ]. Drop-Seq and InDrop were initially reported in 2015 by analyzing mouse retina cell and embryonic stem cell transcriptomes, identifying novel cell types. Sci-RNA-seq, single-cell combinatorial indexing RNA sequencing, was developed in 2017, and SPLiT-seq (split-pool ligation-based transcriptome sequencing) was first reported in 2018. Both approaches use a combinatorial indexing strategy in which attached RNAs are labeled with barcodes that indicate their cellular origin [ 20 , 21 ].

Though single-cell data enable single-cell transcriptomics, it may lose spatial information during single-cell isolation. To solve this problem, spatial transcriptomics has emerged. Spatial transcriptomics employs unique positional barcodes to visualize RNA distributions in RNA sequencing of tissue sections and was first published in 2016 [ 22 ]. Slide-seq, reported in 2019, uses DNA barcode beads with specific positional information [ 23 ]. Geo-seq was introduced in 2017 and integrated scRNA-seq with laser capture microdissection (LCM), which can isolate individual cells [ 24 ]. In situ sequencing refers to targeted sequencing of RNA fragments in morphologically preserved tissues or cells without RNA extraction, including in situ cDNA synthesis by padlock probes or stably cross-linked cDNA amplicons in fluorescent in situ RNA sequencing (FISSEQ) and in situ amplification by rolling-circle amplification (RCA) [ 25 , 26 ]. Furthermore, various new technologies based on RNA-seq have been developed for specific applications. For example, a type of targeted RNA sequencing, CaptureSeq, employs biotinylated oligonucleotide probes and results in the enrichment of certain transcripts to identify gene fusion [ 27 , 28 ].

Computational analysis of RNA sequencing data

Computational analysis tools for RNA sequencing have dramatically increased during the past decade. The choice of a particular tool should be based on the purpose and accuracy of application [ 29 , 30 , 31 ]. A general RNA sequencing data analysis process involves the quality control of raw data, read alignment and transcript assembly, expression quantification and differential expression analysis (Fig.  4 ).

Bioinformatics tools commonly used in RNA-seq data analysis. These tools are primarily used in the four main processes of RNA-seq data analysis, including quality control, read alignment and transcript assembly, expression quantification and differential expression analysis

The first step of data analysis is to assess and clean the raw sequencing data, which is usually provided in the form of FASTQ files [ 32 ]. Quality control visually reflects the quality of the sequencing and purposefully discards low-quality reads, eliminates poor-quality bases and trims adaptor sequences [ 31 ]. Common tools include FASTQ [ 33 ], NGSQC [ 34 ], RNA-SeQC [ 35 ], Trimmomatic [ 36 ], PRINSEQ [ 37 ] and Soapnuke [ 38 ].

The next step is to map the clean reads to either a genome or a transcriptome. There are some mapping tools available, including Tophat2 [ 39 ], HISAT2 [ 40 ], STAR [ 41 ], BWA [ 42 ] and Bowtie [ 43 ]. After alignment, another type of software, such as Cufflinks [ 44 ], StringTie [ 45 ], Trinity [ 46 ], SOAPdenovoTrans [ 47 ] and Trans-AByS [ 48 ] can be used to assemble transcripts from short-reads. When the transcript model is established, its expression can be quantified at the gene, transcript and exon levels. Commonly used software for gene-level quantification includes FeatureCount [ 49 ] and HTSeq-count [ 50 ]. Transcript level quantitative software includes Cufflinks [ 44 ], eXpress [ 51 ] and RSEM [ 52 ]. DEXSeq is a software for exon level quantification [ 53 ]. In addition, there are some alignment-free quantification tools such as Kallisto [ 54 ], Sailfish [ 55 ] and Salmon [ 56 ], which have the advantage of marked computational resource saving. After normalizing, an expression matrix is generated, and statistical methods can be used to identify differentially expressed genes. DESeq2 [ 57 ] and edgeR [ 58 ] are commonly used to perform this task.

Applications of RNA-sequencing in cancer research

Genomic data, such as RNA-seq, have become widely available due to the popularity of high-throughput sequencing technology [ 59 ]. As an important part of next-generation sequencing, RNA sequencing has made great contributions in various fields, especially cancer research, including studies on differential gene expression analysis and cancer biomarkers, cancer heterogeneity and evolution, cancer drug resistance, the cancer microenvironment and immunotherapy, neoantigens, etc. (Fig.  5 ).

Applications of RNA-seq in differential expression analysis and cancer biomarkers, cancer heterogeneity and drug resistance, cancer immune microenvironment, immunotherapy and neoantigen. a Differential expression analysis by RNA sequencing can identify potential biomarkers, including fusion transcript, lncRNA, miRNA and circRNA. b The heterogeneity and drug resistance of cancer cells identified by RNA-seq. c Novel molecular signature, regulatory protein and unknown subtypes in cancer infiltrating immune cells and potential resistance effector in immunotherapy can be identified by RNA-seq; d Neoantigen profiling by RNA-seq and TCR modification targeted neoantigens

Differential gene expression analysis and cancer biomarkers

Differential gene expression analysis is one of the most common applications of RNA sequencing [ 60 ]. Samples from different backgrounds (different species, tissues and periods) can be used for RNA sequencing to identify differentially expressed genes, revealing their function and potential molecular mechanisms [ 61 ]. More importantly, differential gene expression analysis facilitates the discovery of potential cancer biomarkers [ 62 ]. Many studies have shown that gene fusions are closely related to oncogenesis and are appreciated as both ideal cancer biomarkers and therapeutic targets [ 63 ]. Gene fusions in clinical samples are primarily detected by RNA-CaptureSeq. Compared to whole transcriptome sequencing, RNA-CaptureSeq has significantly higher sequencing depth [ 27 , 64 , 65 ]. It has been reported that the NUP98-PHF23 fusion gene is likely to be a novel therapeutic target in acute myeloid leukemia (AML) [ 66 ]. Recently, a variety of recurrent gene fusions, including ESR1-CCDC170, SEC16A-NOTCH1, SEC22B-NOTCH2 and ESR1-YAP1, have been identified in breast cancer, indicating that recurrent gene fusion is one of the key drivers for cancer [ 67 ]. Several novel configurations of  BRAF, NTRK3 and RET gene fusions have been identified in colorectal cancer [ 68 ]. These fusions may promote the development of malignancy and provide new targets for personalized treatment [ 68 ]. In addition, some special genomic factors have been discovered as biomarkers by RNA sequencing, including miRNA, lncRNA and circRNA, which are widely present in various types of cancer [ 69 , 70 , 71 ]. A recent example is circRNA_0001178 and circRNA_0000826, which are biomarkers of colorectal cancer metastasis to the liver [ 72 ]. By applying both RNA sequencing and small RNA sequencing, a study on pancreatic cancer identified differential expression of simple repetitive sequences (SSRs) and demonstrated that the frequency of SSR motifs changed dramatically, which is expected to become a tumor biomarker [ 73 ]. In addition to nucleic acid biomarkers, RNA-seq combined with immunohistochemistry and western blot has also identified certain proteins as cancer biomarkers, such as nuclear COX2 (cyclooxygenase2) in combination with HER2 (human epidermal growth factor receptor type 2), which may serve as potential biomarkers for the diagnosis and prognosis of colorectal cancer [ 74 ]. Similar examples identified using RNA-seq profiling analysis include ISG15 (Interferon-stimulated gene 15) in nasopharyngeal carcinoma [ 75 ] and DMGDH (dimethylglycine dehydrogenase) in hepatocellular carcinoma [ 76 ]. Data-mining analysis of RNA sequencing data and other clinical data has identified that the isoforms of peroxiredoxins also can be expected the prognostic biomarkers for predicting overall survival and relapse-free survival in breast cancer [ 77 ]. Increasing differentially expressed genes are being identified by RNA sequencing, and new potential cancer biomarkers are being continuously discovered (Table 2 ). However, sufficient clinical practice is needed to confirm the diagnostic and predictive applications of these biomarkers in cancer.

RNA-seq could detect early mutations as well as high molecular risk mutations, thus can discover novel cancer biomarkers and potential therapeutic targets, monitoring of diseases and guiding targeted therapy during early treatment decisions. Tumor mutation burden (TMB) is considered as a potential biomarker for immune checkpoint therapy and prognosis [ 78 , 79 ]. RNA-seq can be used to explore the application value of TMB in diffuse glioma [ 78 ]. Through the RNA-seq, MET exon 14 mutation and isocitrate dehydrogenase 1 (IDH1) mutation were identified as new potential therapeutic targets in lung adenocarcinoma and chondrosarcoma patients, respectively [ 80 , 81 ]. Several studies have shown that RNA sequencing can effectively improve the detection rate on the basis of DNA sequencing, provide more comprehensive detection results and achieve a better curative effect for targeted therapy [ 82 ]. In addition, it has been proved that IDH mutation is a good prognostic marker for glioma by RNA-seq [ 83 ]. Targeted therapy is also considered to enhance or replace cytotoxic chemotherapy regimen in cancer including AML [ 84 , 85 , 86 ].

ScRNA-seq also has some new discoveries in diagnosis. For example, scRNA-seq data can be used to infer copy number variations (CNV) and to distinguish malignant from non-malignant cells. The infer CNV algorithm, which was used in the study of glioblastoma, uses averaging relative expression levels over large genomic regions to infer chromosome copy number variation [ 87 ]. Similar examples include head and neck cancer [ 88 ] and human oligodendroglioma [ 89 ]. It is reported that RNA sequence of tumor-educated blood platelets (TEPs) can also become a blood-based cancer diagnosis method [ 90 ]. It should be noted that the lack of detailed functional implications of the identified RNAs in platelets in the field of platelet RNA research is also an urgent problem to be solved [ 91 ].

Cancer heterogeneity and evolution

Heterogeneity has always existed during the transformation of normal cells to cancer cells. The continuous accumulation of heterogeneity may reflect the evolution of cancer [ 109 ]. Early RNA sequencing detected all RNA transcripts in a given tissue or cell group, ignoring differences in individual cells. Transcriptome profiling of single-cell RNA sequencing solves this problem by providing single-cell resolution of the transcriptome [ 3 ]. In melanoma, single-cell RNA-seq was used to analyze 4645 tumor cells from 19 patients, including cancer cells, immune cells, mesenchymal cells and endothelial cells. Transcriptomic data from different single cells revealed that heterogeneity of cells within the same cancer is associated with cell cycle, spatial background and drug resistance [ 110 ]. A recent single-cell RNA-seq study of 49 samples of metastatic lung cancer revealed changes in plasticity induced by non-small cell lung cancer treatment, providing new directions for clinical treatment [ 111 ]. Single-cell RNA sequencing also integrates a variety of information in a single cancer cell, deciphering the secrets of cancer heterogeneity and evolution [ 112 ]. Compared with scRNA-seq, another emerging technology spatial transcriptome sequencing incorporates information on the spatial location of cells. In prostate cancer, using spatial transcriptomics technology, the transcriptome of nearly 6750 tissue regions was analyzed, revealing the whole-tissue gene expression heterogeneity of the entire multifocal prostate cancer and accurately describing the range of cancer foci [ 113 ]. In a study of breast cancer tissues, the results of spatial transcriptome sequencing revealed that gene expression among different regions was surprisingly highly heterogeneous [ 22 ]. In recent years, single-nucleus RNA sequencing (snRNA-seq) has also received extensive attention due to its solving the problem that single-cell RNA sequencing cannot be applied to frozen specimens and cannot obtain all cell types in a given tissue [ 114 , 115 ]. The emerging technology of RNA sequencing will contribute to research on cancer heterogeneity and evolution.

Cancer drug resistance

Drug resistance is a main reason leading to cancer treatment failure. However, the molecular mechanisms underlying drug resistance are still poorly understood [ 116 ]. RNA sequencing became a vital tool for revealing the mechanisms of cancer drug resistance. In breast cancer, single-cell RNA sequencing identified a tumor-infiltrating immunosuppressive immature myeloid cell that leads to drug resistance [ 117 ]. Another study identified a new COX7B gene related to platinum resistance and a surrogate marker CD63 in cancer cells by single-cell RNA-seq [ 118 ]. RNA sequencing has also demonstrated that cancer cells that wake up from a dormant state produce large amounts of BORIS (brother of the regulator of imprinted sites), which can regulate the expression of survival genes in drug-resistant neuroblastoma cells [ 119 ]. Identifying special molecules that mediate these processes could help us understand the occurrence of drug resistance. Single-cell transcriptomics can be used to study different modes of chemoresistance in tumor cells and has shown that pre-existing drug-resistant cells can be selected through higher phenotypic intratumoral heterogeneity, while phenotypic homogeneous cells use other mechanisms to trans-differentiate under drug-selection [ 120 ]. In one study of pancreatic ductal adenocarcinoma, human pancreatic cancer (PANC-1) cells and gemcitabine-resistant PANC-1 cell lines were compared by RNA sequencing, and two circRNAs were identified as both novel biomarkers and potential therapeutic targets for gemcitabine resistant patients [ 121 ]. RNA-seq has also conducted in-depth research on the drug resistance of hematological malignancies. Through RNA-seq, it has been found that non-coding RNAs and fusion genes play an important role in mediating the drug resistance of hematological malignancies [ 122 ]. A good example is to compare the circRNA expression profile of the drug-resistant acute myeloid leukemia cell with its parent cell, and determine the circRNAs involved in drug resistance [ 123 ]. Similarly, the novel MEF2D-BCL9 fusion transcript identified by RNA-seq was found to increase HDAC9 (histone deacetylase 9) expression and to enhance the resistance to dexamethasone in acute lymphocytic leukemia (ALL) [ 124 ]. Leukemia stem cells (LSCs), a rare cell population assumed to be responsible for relapse, is crucial to improve the prognosis of patients [ 125 , 126 ]. RNA-seq analysis showed that LSCs have a unique lncRNA signature with functional relevance and therapeutic potential, providing an explanation for chemotherapy resistance and disease recurrence [ 127 ].

The cancer microenvironment and immunotherapy

The immune system plays a critical role in the cancer microenvironment, affecting several stages of cancer development, including tumorigenesis, progression and metastasis, through tumor-infiltrating lymphocytes (TILs) [ 128 ]. TILs and their interactions with malignant cells and stromal cells make up the cancer immune microenvironment. Due to the heterogeneity of cancer, it is difficult to define the exact pro- or anti-cancer function of certain immune cells. Cancer heterogeneity also causes the varied clinical efficacy observed in patients treated with immunotherapies due to different responses of different subclones [ 129 ]. Transcriptomic profiling by RNA-seq, in particular scRNA-seq, provides comprehensive information on cellular activity and interactions among cells in the tumor microenvironment (TME). ScRNA-seq enables genomic and molecular profiling of high quantity and quality individual immune cells and assessment of cellular heterogeneity to depict the immune system spectrum in the cancer microenvironment [ 130 , 131 , 132 ]. ScRNA-seq data demonstrated that compared to normal tissues, cancer tissues exhibited significantly higher heterogeneity in the immune microenvironment, and a continuity in T cell activation resulting from polyclonal T cells and heterogeneous antigen-presenting cells has been identified [ 133 ].

ScRNA-seq of tumor-infiltrating T cells in metastatic melanoma identified transcription factor NFATC1 (nuclear factor of activated T cells 1) as a potential molecular signature of T cell exhaustion programs and revealed the depletion of low-exhaustion T cells in expanded clones of T cells [ 110 ]. Combining scRNA-seq with assembled T cell receptor (TCR) sequences, 11 T cell subsets, such as CD8  +  T cells and CD8  +  FOXP3  +  regulatory-like cells, and their genomic signatures, were identified in hepatocellular carcinoma (HCC), providing valuable insights for understanding the immune landscape of infiltrating T cells in HCC [ 134 ]. Cancer infiltrating T cells also play an anti-tumor role through impairment of an autophagy protein, LC3 (microtubule-associated protein 1A/1B-light chain 3, often short for LC3)-associated phagocytosis (LAP), demonstrating the role of autophagy in oncogenesis and suppression revealed by scRNA-seq [ 135 ]. In addition to solid tumors, scRNA-seq of acute myeloid leukemia patients detected diverse immunomodulatory genes that suppress T cell function [ 136 ]. By CSOmap, a computational tool for scRNA-seq, the CCL4-CCR8 directed interaction between Tregs and Texs, as well as reduced proliferation of Texs, was characterized [ 137 ]. Notably, findings also revealed that tumor-infiltrating T cells exhibited more interactions among themselves than with T cells from peripheral blood and different interactions between tumors and T cells, indicating a varied response to immunotherapy and a potential trend for immune escape [ 137 ].

In the cancer immune microenvironment, neutrophils, in addition to T cells, are also key components of cancer progression and cancer drug resistance [ 138 , 139 , 140 , 141 ]. Through scRNA-seq of murine sarcomas and certain human cancers, neutrophils with CSF3R (colony stimulating factor 3 receptor) expression were found to be a part of type 1 antitumor immunity associated with unconventional CD4 − CD8 − αβ T cells (UTCαβ) in anti-cancer immunity, indicating better prognosis [ 142 ]. ScRNA-seq of metastatic breast cancer and CD45 cells from primary cancer identified neutrophils as pro- and anti-tumorigenic or metastatic, in which pro-tumorigenic and metastatic neutrophils are induced by IL11 expressing cancer subclones, resulting in polyclonal metastasis [ 143 ]. This observation also provides new insight into anti-cancer immunotherapy by targeting neutrophils [ 143 ]. With scRNA-seq of CD4 and CD8 T cells, several crucial pathways with anti-cancer function were revealed [ 144 ].

The balance between immune reaction and immune tolerance is the basis of immune homeostasis, which is also involved in anti-cancer immunity and oncogenesis. scRNA-seq of monocytes and dendritic cells (DCs) separated from a single lymph node melanoma metastasis revealed a conserved homeostatic module regulated by suppressor-of-cytokine-2 (SOCS2) protein and IFNγ [ 145 ]. SOCS2 serves an essential regulatory role in anti-tumor immunity and T cell priming through DCs. This highly conserved homeostatic program establishes a connection between autoimmune prevention and immune surveillance in cancer [ 145 ].

Immunotherapies, especially immune checkpoint blockade (ICB), has opened a new chapter for anti-cancer therapy with remarkable responses from targeting programmed death 1 (PD1), programmed death-ligand 1 (PD-L1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA4) [ 146 , 147 , 148 ]. However, only a few patients benefit from ICB, and severe side effects were observed [ 149 , 150 ]. Obviously, various unknown determinants are correlated with the outcome of immunotherapies in addition to well-known factors such as PD1/PD-L1/CTLA-4 expression and mismatch repair deficiency [ 151 , 152 , 153 , 154 , 155 ]. Therefore, it is paramount to identify potential effectors for ICB efficacy. By analyzing RNA-seq data from melanoma patients who underwent anti-PD1 and anti-CTLA4 treatment, a potential ICB resistance effector SERPINB9 (a member of the serine protease inhibitor (serpin) family) and the connection between cytotoxic T lymphocytes (CTL) infiltration level and ICB response were characterized [ 156 ]. A sub-population cells with immunotherapy persistence have been identified by scRNA-seq and were found to have stem cell-like states with the expression of stem cell antigen-1 (Sca-1) and Snai1 [ 157 ].

Another immunotherapy, myeloid-targeted immunotherapy, is based on the complexity of tumor-infiltrating myeloid cells, including DCs and tumor-associated macrophages (TAMs) revealed by scRNA-seq [ 158 ]. Through scRNA-seq of immune cells from colorectal cancer patients, C1QC  +  and SPP1  +  TAMs, two subsets of TAMs, were identified, and the mechanism of myeloid-targeted immunotherapy, such as anti-CSF1R (colony stimulating factor 1 receptor) and CD40 agonist, was revealed [ 159 ]. Intracellular staining and sequencing (INs-seq), a novel technology integrating scRNA-seq and intracellular protein activity measurements, revealed novel Arg1  +  Trem2  +  regulatory myeloid (Mreg) cells and demonstrated that depletion of Trem2 led to deduction of exhausted CD8 T cells with increased NK and cytotoxic T cells and cancer suppression by reducing accumulation of intratumoral Mreg cells [ 160 ].

Cancer neoantigens

Neoantigens, human leukocyte antigen (HLA)-bound peptides derived from cancer-specific somatic mutations or gene fusions during tumor growth, are another crucial regulator of the clinical response to immunotherapy [ 161 ]. Higher intratumor neoantigen heterogeneity and clonal neoantigen burden increases sensitivity to ICB and contributes to better clinical outcome in patients with melanoma and advanced non-small cell lung cancer [ 162 ]. This kind of antigen is an optimal target for anti-cancer immunotherapy, enhancing neoantigen-specific T cell activity, and a vaccine targeting personal neoantigens for melanoma patients has been developed [ 163 , 164 ]. Given all these promising features of personalized medicine targeting neoantigens in tumors, massive parallel profiling of tumor neoantigen burden is necessary for improving clinical efficacy and a deeper understanding of the neoantigen landscape. An RNA-seq-based transcriptomic approach is an efficient tool for neoantigen profiling in many studies. It was revealed that homology of neoantigen and somatic-mutation induced pathogens are important in response prediction in anti-CTLA4 treated melanoma [ 165 ]. In addition to melanoma, other studies found reduced neoantigen load in triple-negative and HER2 breast cancers [ 166 ], diverse neoantigen abundance in non-small-cell lung cancer patients with different treatment strategies [ 167 ], a decreased ratio of neoantigen expression to predicted neoantigens in recurrent glioma due to immune selection pressure [ 168 ], a negative correlation between neoantigen abundance and clinical outcome in selected solid tumors [ 169 ] and different neoantigen landscapes in immune filtration and T cell dysfunction based on histology in salivary gland carcinoma (SGC) patients [ 170 ].

A neoantigen prediction program, Neopepsee, based on RNA-seq data and somatic mutation, can be utilized to detect potential neoantigens for personal vaccine development with reduced false-positive rate compared to binding affinity prediction [ 171 ]. ScanNeo is another prediction computational pipeline based on RNA-seq that aims to identify insertion and deletion derived neoantigens, which was validated in prostate cancer [ 172 ], and ASNEO, which identifies personal-specific alternative splicing derived neoantigens [ 173 ]. Several neoantigens have been identified to be related to cancer prognosis and might be potential targets of immunotherapies, such as the TP53 neoantigen for HCC patients [ 174 ]. For the anti-neoantigen immunotherapy in cancer, a new strategy involving neoantigen-specific TCRs modification has been proposed, and scRNA-seq has been applied to isolate neoantigen-specific TCRs for further clinical application [ 175 ].

Conclusions and perspectives

High-throughput RNA-seq technology has been a major tool to explore the transcriptome. The rapid development of RNA-seq technology not only saves time and cost but also sheds light on many new research fields. However, there are still limitations of RNA-seq technology that need to be improved.

For short-read length RNA-seq technologies, bias and imperfections are primarily generated in sequencing library preparation and short read assembly. It is difficult for these methods to correctly identify multiple isoforms from a certain gene. To overcome the disadvantage of short read length, improved read coverage and sequencing depth is required. Long-read length RNA-seq technologies avoid shortcomings in template amplification, reduce the false positive rate in splice junction detection and enable the identification of unannotated longer transcripts, overcoming the common limitations of short-read sequencing [ 176 , 177 ]. However, this method suffers from the drawback of reduced throughput, higher cost and higher sequencing error rate, especially insertion-deletion errors. To reduce random errors, PacBio circular consensus-sequencing (CCS) was developed to increase sequencing depth by rereading molecules several times. However, it also reduces the identification rate of unique isoforms. In addition, the sensitivity of long-read sequencing for identification of differentially expressed genes is lower compared to short-read sequencing [ 178 , 179 , 180 ]. Thus, hybridization of long-read and short-read sequencing has been reported to yield a more comprehensive and accurate analysis [ 181 ].

Improvements in the throughput of RNA sequencing technology have resulted in billions of sequencing reads, bringing great challenges to the computational process, such as data storage, transmission, quality control and data analysis, including read mapping, transcript assembly and read normalization. Therefore, it is important for bioinformatics to keep pace with the continuous developments of RNA-seq technologies. Notably, bias could be produced due to differences in read data handling, necessitating the improvement of current bioinformatics pipelines.

RNA-seq measures gene expression by the read counts, which always containing missing values, thus results in information loss of specific gene and negative impact on downstream analysis. To overcome this problem, missing data need to be imputed and analyzed by several methods, such as optimal clustering with missing values [ 182 ]. For scRNA-seq, the proportion of genes with zero or low expression varies across cells due to biological or technical bias. For example, batch effects can come from cells captured and sequenced in different conditions [ 183 ]. Imputation methods, such as SAVER, MAGIC and kNN-smoothing, are recommended for scRNA-seq [ 184 ]. Another method named batch effects correction with unknown subtypes for scRNA-seq data (BUSseq) utilizes Bayesian hierarchical model and can also be used to correct batch effects and missing data [ 185 ].

Combination of data from multi-omics sequencing can undoubtedly expand the application of RNA-seq. For example, Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) was developed by utilizing hyperactive Tn5 transposase to identify open chromatin region and transcriptional factor (TF) binding sites [ 186 ]. The integration of ATAC-seq and RNA-seq enables the reveal of TF-targeted genes and their transcripts [ 187 , 188 ]. Chromatin conformation capture analysis (3C) technology and its several derivatives including circular chromosome conformation capture (4C), carbon copy chromosome conformation capture (5C), ChIP-Loop, Hi-C and capture Hi-C were developed and improved to detect chromatin structure as well as unknown interacting regions [ 189 , 190 , 191 ]. It has been reported that combined analysis of RNA-seq and chromatin structure can detect structure variation-related differentially expressed genes [ 192 , 193 , 194 ].

Epitranscriptomics is a crucial part of gene expression, and methylation of adenosine at the N6 position (m6A) is the most abundant [ 195 ]. Traditional RNA-seq needs reverse transcription before sequencing and thus easily loses the information of transcriptome complexity. This shortcoming can be overcome by directly sequencing native RNA molecules using methods such as nanopore sequencing. Transcript modifications could be inferred from the current signal as the modified RNA molecules passing nanopore cause a characteristic temporary current blockade, which enables the detection of diverse modifications such as m6A or 5-methylcytosine (m5C) [ 196 , 197 , 198 ].

ScRNA-seq is a powerful technology to facilitate further exploration in cancer research and also has been employed in the detection of cancer stem cell subpopulation, metabolic switch in cancer-draining lymph nodes and therapy-induced adaption of cancer cells [ 111 , 199 , 200 ]. Combined with cell sorting or ligand-receptor interaction, scRNA-seq was utilized in cellular interaction, cell spatial organization as well as molecular crosstalk characterization [ 137 , 201 , 202 ]. Coupling of parallel CRISPR (clustered regularly interspaced short palindromic repeats)-pooled screen, scRNA-seq enables the simultaneous analysis of genomic perturbation and transcriptional activity to detect heterogeneous cell type as well as crucial factors of complexity regulatory mechanism [ 203 , 204 , 205 ]. ScNT-seq, single-cell metabolically labeled new RNA tagging sequencing, brings RNA-seq into time resolution by identifying RNAs transcribed at different stage [ 206 ]. Utilizing SNP-based demultiplexing of scRNA-seq data, MIX-Seq was developed to study cancer cell reaction to pharmacologic treatment [ 207 ]. Another technology, snRNA-seq, is invaluable for detecting cellular heterogeneity of cancer and has been employed to identification of a sub-population of adipocytes regulating cancer genesis [ 208 ].

Taken together, RNA-seq has been applied in an impressively wide range of cancer research. All applications in cancer research rely on the boost of advanced RNA-seq technologies, especially the combination of scRNA-seq and spatial transcriptomics as well as data from multi-omics, which will bring RNA-seq technologies into single-cell resolution and tissue-level transcriptomics, providing new insight into cancer diagnosis, treatment and prevention.

Availability of data and materials

Not applicable.

Abbreviations

• RNA sequencing

Single-cell RNA sequencing

Sequencing by Oligonucleotide Ligation and Detection

DNA nanoball sequencing

Next-generation sequencing

Pacific Biosciences single-molecule real-time

Oxford Nanopore Technologies

Single-cell combinatorial indexing RNA sequencing

Split-pool ligation-based transcriptome sequencing

Laser capture microdissection

Fluorescent in situ RNA sequencing

Rolling-circle amplification

Acute myeloid leukemia

Simple repetitive sequences

Cyclooxygenase2

Human epidermal growth factor receptor type 2

Interferon-stimulated gene 15

Dimethylglycine dehydrogenase

Tumor mutation burden

Isocitrate dehydrogenase

Copy number variations

Tumor-educated blood platelets

Single-nucleus RNA-sequencing

Brother of the regulator of imprinted sites

Acute lymphocytic leukemia

Leukemia stem cells

Tumor-infiltrating lymphocytes

Tumor microenvironment

Nuclear factor of activated T cells 1

T cell receptor

Hepatocellular carcinoma

Microtubule-associated protein 1A/1B-light chain 3-associated phagocytosis

Colony stimulating factor 3 receptor

Unconventional CD4 − CD8 −  αβ T cells

Dendritic cells

Suppressor-of-cytokine-2 protein

Immune checkpoint blockade

Targeting programmed death 1

Programmed death-ligand 1

Cytotoxic T-lymphocyte-associated protein 4

Cytotoxic T lymphocytes

Stem cell antigen-1

Tumor-associated macrophages

Colony stimulating factor 1 receptor

Intracellular staining and sequencing

Regulatory myeloid cells

Human leukocyte antigen

Salivary gland carcinoma

Circular consensus-sequencing

Assay for Transposase-Accessible Chromatin using sequencing

Transcriptional factor

Chromatin conformation capture analysis

Circular chromosome conformation capture

Carbon copy chromosome conformation capture

N6 position

5-Methylcytosine

Clustered regularly interspaced short palindromic repeats

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (81300398 and 81974436); Key-Area Research and Development Program of Guangdong Province (2020B1111030005); The Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2019ZT08Y485); Key Program of Marine Economy Development (Six Marine Industries) Special Foundation of Department of Natural Resources of Guangdong Province (GDNRC [2020]070); The Fundamental Research Funds for the Central Universities (19ykzd06) and the Opening Fund of Guangdong Key Laboratory of Marine Materia Medica (LMM2020-4).

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Institute of Laboratory Medicine, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, School of Medical Technology, Guangdong Medical University, Dongguan, 523808, China

Mingye Hong, Xuanmei Huang, Shaohui Huang & Hua Zhang

Biotherapy Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China

Shuang Tao, Li-Ting Diao, Shu-Juan Xie & Zhen-Dong Xiao

Health Science Center, The University of Texas, Houston, 77030, USA

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Hong, M., Tao, S., Zhang, L. et al. RNA sequencing: new technologies and applications in cancer research. J Hematol Oncol 13 , 166 (2020). https://doi.org/10.1186/s13045-020-01005-x

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The research trends and future prospects of nanomaterials in breast cancer

• Yue Li 1 , 3 ,
• Xiaoqing Li 1 , 2 ,
• Aoqun Li 1 , 2 ,
• Jingyan Zhu 1 , 2 ,
• Zhenhua Lin 1 , 2 &
• Yang Yang 1 , 2  

Cancer Nanotechnology volume  15 , Article number:  24 ( 2024 ) Cite this article

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Breast cancer is the most common cause of cancer-related deaths among women globally and the most deadly illness for them. New advances in nanotechnology have led to the development of strategies intended to target breast cancer cells more precisely while causing the least amount of damage to healthy cells. We retrieved articles about nanomaterials for the diagnosis and treatment of breast cancer from the Web of Science Core Collection (WoSCC) database between 2008 and 2023. Our research aims to assess publications on the use of nanomaterials for breast cancer treatment and diagnosis to predict future research directions.

A total of 457 papers on nanomaterials in breast cancer were discovered from various nations, with China being the primary source and the United States having the highest H index. The number of papers in this discipline is increasing on an annual basis. The Egyptian Knowledge Bank is an important research center in this sector. The International Journal of Nanomedicine has the most papers, and Kesharwani P is the most frequently referenced author. The most quoted article was written by Miele, Evelina of India in 2009. Topics such as drug delivery may be emerging areas of research.

Our findings predict that the use of nanomaterials in medication delivery will become a significant research area in the future, and provide valuable references for scholars investigating the role of nanotechnology in breast cancer.

Introduction

Breast cancer is the most common malignant tumor among women, with the highest incidence rate (Thomas et al. 2024 ; Siegel et al. 2023 ). According to the GLOBACON database, there were 22,968,840 new cases and 666,103 deaths of breast cancer between 2022 and 2024 (8th February). Early diagnosis of breast cancer is challenging due to the limited sensitivity of existing methods in detecting small lesions (Ha et al. 2018 ). Various therapeutic modalities have been used to treat breast cancer, including the combination of radiotherapy and chemotherapy to inhibit tumor progression and recurrence. However, the low therapeutic efficacy of these drugs is due to their poor target and affinity (Liu et al. 2023 ; Zhang et al. 2022 ). Therefore, new and effective methods for diagnosing and treating breast cancer are needed.

Nanoparticles can interact with a variety of organelles and biomolecules and range in size from 1 to 100 nm (Nikalje 2015 ). These properties make them suitable for various applications, from chemical reactions to biomedicine (Mujahid et al. 2022 ). Quantum dots (QDs) and gold nanoparticles are used at the molecular level for cancer diagnosis (Kher and Kumar 2022 ). Molecular diagnostic techniques based on nanoparticles can be used for biomarker discovery and rapid tumor diagnosis (Yan et al. 2019 ). The rapid growth of tumors results in the epidermal cell gap of intra-tumor blood vessels being larger than that of normal blood vessels, and the lack of a lymphatic system within the tumor makes it easy for nanoparticles to “leak” into the tumor from the gap in the tumor blood vessels and accumulate in the tumor. Some investigations have shown that nanomaterials can be targeted against the endothelial cells of tumor blood vessels, releasing anti-angiogenic medicines, successfully suppressing tumor blood vessel growth and reducing the oxygen supply. (Chakraborty et al. 2019 ). The high surface-to-volume ratio of magnetic nanoparticles allows them to assemble with biomolecules or residues, which can enhance the specificity of chemical drug complexes in targeted therapy (Moloudi et al. 2023 ; Londhe et al. 2023 ). In addition, nanotechnology-enhanced photodynamic therapy and immunotherapy are emerging as exciting cancer therapeutic methods with significant potential for improving patient outcomes (Jia et al. 2023 ). The addition of nanomaterials enhances the responsive release, depth of tissue penetration and precise targeting of phototherapy, enabling precise treatment of specific cancer tissues and cells through photodynamic therapy, photothermal therapy and combination therapy (Mosleh-Shirazi et al. 2022 ). Some studies have indicated that immune checkpoint blockade therapy does not become noticeably more effective when drugs are delivered using nanoparticles. On the other hand, when paired with chemotherapy and other treatments, nanoparticles can enhance anti-cancer immune responses as well as improve medication transport and usage efficiency. However, no bibliometric analysis has been published on the use of nanomaterials in the diagnosis and treatment of breast cancer.

Hence, this study aims to analyze the use of nanoparticles in breast cancer therapy through quantitative methods. Scientometric analyses aid academics in comprehending the effectiveness of nanomaterials in treating breast cancer, familiarising themselves with published research results, and understanding collaborations and links between countries, institutions, and authors. Additionally, we objectively reveal the current status and future directions of nanotechnology in breast cancer; review current research hotspots; and anticipate research trends and future prospects in this field.

Browse and search

Web of Science is a crucial database for accessing global academic information (Pei et al. 2022 ). It can rapidly identify high-impact papers, reveal research directions that domestic and foreign authorities focus on, and expose the trend of subject development (Wang and Maniruzzaman 2022 ). A search was conducted in the WOSCC database for literature related to the use of nanomaterials in breast cancer diagnosis and treatment from 1 January 2008 to 31 December 2023. The search formula used was as follows: TS = (“nanostructured materials” OR “nanomaterials” OR “nanotechnology”) AND TS = (“breast” OR “breast cancer”) AND TS = (“treatment” OR “diagnosis” OR “cure” OR “therapy”).

Screening procedures

The inclusion criteria are as follows: (1) the full text of the available papers focused on the diagnosis and treatment of nanomaterials in breast cancer; (2) the papers were written in English; (3) only articles and reviews were permitted; (4) the papers were sourced from the Social Science Citation Index (SSCI) and the Science Citation Index Expanded (SCI-E) databases; and (5) the timeframe was from 2008 to 2023. The study’s inclusion criteria were as follows: The following criteria were excluded: (1) This paper will focus on nanomaterials that are not related to breast cancer diagnosis and treatment. (2) This study will consider all types of publications, including reports, theses, and conference abstracts.

Data analytics

The publications and citations were exported as plain text for bibliometric analysis and visualization. VOSviewer (version 1.6.19) and CiteSpace (version 6.2) were used to create visualizations. Line graphs were generated using GraphPad Prism (version 9.5.1) to display the number of publications, citations, and h-index per year. To analyze the most prolific/collaborative countries, institutions, authors, co-cited journals, and co-occurring keywords, VOSviewer was used.

CiteSpace can be used to construct keyword timeline charts and keyword bursts. The visualization chart displays dots representing countries, institutions, authors, or journals, which are clustered into different groups based on their collaboration (Liu et al. 2022 ). The size of the dots corresponds to the number of publications. The thickness of the lines connecting the nodes represents the link strength (LS) and reflects the strength of collaboration between them (Xia et al. 2022 ). The Total Link Strength (TLS) metric measures the level of collaboration between nodes (Jin et al. 2023 ). We improved the keyword analysis by excluding irrelevant keywords and merging those with similar meanings to provide a better perspective. The graph generated by CiteSpace shows significant and reasonable clustering, with a modularity value greater than 0.3 and an average silhouette value greater than 0.7.

Selection and characterization of literature

A search for keywords related to breast cancer and nanomaterials in the Web of Science database retrieved a total of 463 papers. In the first stage of selection, 6 articles were excluded due to type restriction. Next, we screened 457 publications published during the decade from 2008 to 2023 to do the study (Fig.  1 ). The results show an overall increasing trend in the number of annual publications (Fig.  2 A), indicating an increased interest in the field of breast cancer and its treatment and nanomaterials. The number of publications peaked in 2022 with 72 publications, which accounted for 16.74% of the total publications (Fig.  2 B). The year with the highest number of citations was 2020, with a total of 1269 citations (Fig.  2 C). The number of publications has steadily increased from 2008 to 2024. The annual H-index increased from 2 in 2008 to 20 in 2020 (Fig.  2 D).

Flowchart of the literature screening proces

Publication trends in breast cancer and nanomaterials research. A . The number of the publications; B .The number of cumulative publications; 15-year publication rate with an exponential trend line, 2008 to 2023. C . The total citations of the publications; D . The H‑index values of the publications

Country/region and institutional analysis of publications

VOSviewer analysis shows that a total of 59 countries/regions have cooperated in this area (Fig.  3 A, B ). India had the most robust international cooperation network (TLS = 84) and cooperated most closely with Saudi Arabia (TLS = 65). The country with the highest centrality is Iran (0.53), followed by USA (0.34) and India (0.32). The USA (5.9) has the highest burst strength next to Spain (2.85) and South Korea (2.42). Next, we analyzed the top 10 productive countries/regions in terms of number of publications, total citations, and H-index. China published the most papers (109, 22.61%), followed by India (85, 18.53%) and USA (79, 40.14%). In addition, the USA has the highest number of citations (3171) and the highest H-index (30).

Diagram of the Nanomaterials and Breast Cancer Collaboration Network. A-B . The coauthorship network map of countries; C-D . The coauthorship network map of authors; E . The coauthorship network map of institutions; F . The cocitation network map of journals

The institutional collaboration network diagram is shown in Fig.  3 E, which contains 99 institutions. Jamia Hamdard has the strongest total connectivity strength (TLS = 51). Statistically, the institution with the highest centrality is the Egyptian Knowledge Bank (EKB) (0.14) followed by Islamiv Azda University (IAU) (0.07). Table 1 shows the top 10 most productive institutions. The highest number of published papers was from EKB (20), next to IAU (16). EKB had the highest total number of citations (393) and the highest h-index (10).

Analyzing the authors of publications

The network of inter-authors and collaborative relationships is shown in Fig.  3 C, D . In this study, we examined the top ten contributors in terms of posting volume. Kesharwani P (TLS = 60) works most closely with other authors. The highest number of posts is Kesharwani P (11, 25.09%), followed by Chorilli M (7, 5.71%). Kesharwani P has not only the highest number of citations (276), but also has the highest H-index (9).

Analysis of the publication journal

For this study, we selected the top ten most productive journals from the communication network diagram (Fig.  3 F, Table  2 ). The most partnerships with other journals are the Biomaterials (TLS = 108,995). The top three journals with the most published research in the field are the International Journal of Nanomedicine , the Journal of Controlled Release , and the Journal of Drug Delivery Science and Technology . The most cited is the International Journal of Nanomedicine , which has the highest H-index. Furthermore, the Impact Factor (IF) of journal is a crucial parameter for assessing its worth and that of its published works. Journal of Controlled Release has the highest IF (10.8) and JCR category (Q1).

Analysis of the most cited articles

The nanomaterials analyzed in the most cited publications of each year during the study period are shown in Fig.  4 (Karathanasis et al. 2008 ; Eghtedari et al. 2009 ; Chen et al. 2010 ; Deng et al. 2011 ; Dreaden et al. 2012 ; You et al. 2013 ; Lee et al. 2014 ; Gurunathan et al. 2015 ; Kumar et al. 2017 ; Mu et al. 2017 ; Zhang et al. 2018 ; Gao et al. 2019 ; Wang et al. 2020 ; Dubey et al. 2021 ; Fatima et al. 2022 ; Ashrafizadeh et al. 2023 ). Gold nanoparticles were commonly used in these studies. Table 3 shows the top 10 most cited articles as of 18 November 2023, along with the information associated with them, which we will examine in the next section. Four of the ten studies are from the USA, two are from India, and the remaining four are from Singapore, China, South Korea, and Italy. The study titled “Albumin-bound formulation of paclitaxel (Abraxane ® ABI-007) in the treatment of breast cancer” by Miele E, published in the International Journal of Nanomedicine in Italy in 2009, which was cited 652 times, making it the most cited publication in the field. (Miele et al. 2009 ).

The timeline of nanomaterials and breast cancer research

For the study of keywords and research hotspots

In a paper, keywords can help us get the topic and theme of the research more accurately and quickly (Tang et al. 2023 ). The division of all keywords into six clusters in Fig.  5 A. As shown in Fig.  5 B, terms in purple indicate that their average publication year is in 2018 and earlier, while terms in bright yellow indicate that their average publication year is after 2021. “quantum dots”, “resistance” and “therapeutics” were the main focus in the early days. Keywords such as “immunotherapy”, “nanomaterials” and “anticancer activity” will not start to attract widespread attention until after 2021. The largest clusters are in red and include keywords like “diagnosis” and “metastasis” in breast cancer. The second cluster is in purple and contains keywords such as “nanotechnology”, “nanoparticles” and “cells”. The third cluster is in yellow and includes keywords such as “drug delivery”, “in vitro” and “co-delivery”. The fourth cluster, in blue, contains keywords like “doxorubicin”, “apoptosis” and “gold nanoparticles”. The fifth cluster in pink contains the keywords “delivery” and “immunotherapy”.

Keyword analysis of research hotspots. A-B . The network A and overlay B of keyword co-occurring; C . The timeline map of keyword co-occurring; D . The Top 25 keywords with the strongest citation bursts

In addition, we have used CiteSpace to analyze the similarities and differences in the keywords over time. Until 2014, the main areas of research were breast cancer, metastasis, antitumor therapy, anti-tumour agents and chemotherapy. Breast cancer, photodynamic therapy, resistance and gold nanoparticles continue to be topics of high interest in 2023. The strength of the keyword bursts is another important indicator of the frontiers and hotspots of the study over time. The top ten keywords with the highest outbreak values are iron oxide nanoparticles (5.47), nanotechnology (3.62), quantum dots (3.5), metastatic breast cancer (3.44), transmission electron microscopy (3.2), reactive oxygen species (3.11), scanning electron microscopy (3,04), metastasis (3.04), chemotherapy (2.85), gold nanoparticles (2.84).

In recent years, there has been a surge in the bibliometric analysis of articles across various fields (Tang et al. 2023 ). Tools such as CiteSpace and the VOSviewer enable raw data visualization, offering comprehensive and intuitive data representation (Zhou et al. 2020 ). Our findings demonstrate an overall increased trend in the number of annual publications in the discipline. 2020 was the most significant year in the field, with the most citations and the highest H-index.

China leads in the number of publications per nation with 109 publications, accounting for 25.35% of the total. It also has the highest number of international partnerships, publications, and citations, as well as the highest h-index. China has a large population base and a large number of researchers and institutions in related fields, resulting in higher literature production. In particular, the efficacy of nanomedicines in breast cancer has been a hot topic of research in recent years. The International Journal of Nanomedicine , Journal of Controlled Release , and Journal of Drug Delivery Science and Technology are widely regarded as the most influential publications in this field. International Journal of Nanomedicine has the highest number of published papers, the highest h-index, the highest number of citations and the highest average number of citations of the papers, while Biomaterials has the highest JCR category and IF.

Subsequently, we found that one of the most cited papers used nanocarriers to improve the solubility of paclitaxel in water to increase the solubility and stability of the drug, thereby enhancing its efficacy against breast cancer. Furthermore, we have mapped the mechanism of nanomedicine therapy for breast cancer based on this article (Fig.  6 ). The study points to the development of so-called “third generation” nanocarriers and is based on a multi-stage strategy that addressing all the possible therapies in a more specific manner. The article states that human serum albumin stabilizes the drug particle at an average size of 130 nm (Stinchcombe 2007 ). This prevents any risk of capillary obstruction and eliminates the need for any particular infusion systems or steroid/antihistamine premedication before the infusion (Okuyama et al. 2021 ). Preclinical studies have shown that Abraxane® ABI-007 has higher tumor cell penetration and anti-tumor activity compared to equivalent doses of standard paclitaxel (Miele et al. 2009 ). The most highly cited article published in 2023 focuses on the therapeutic effects of Superparamagnetic Iron-Oxide Nanoparticles (SPIONs) on breast cancer (Dongsar et al. 2023 ). Controlled drug release from SPIONs can also aid in overcoming non-specific targeting and reducing the need for large therapeutic doses. This is because drug-loaded SPIONs can specifically target certain cells, tissues, or organs, minimizing damage to healthy tissues and improving therapeutic efficacy. (Fakayode et al. 2018 ). Hoekman et al. demonstrated that liposomes have a stable and robust closed membrane that can carry hydrophilic small molecule drugs, as well as protein, RNA, and DNA drugs within aqueous chambers. This membrane protects the drugs from plasma scavenging and degrading enzymes, prolonging their retention time in the circulation of the body (Xinchen et al. 2023 ). Overall, by increasing treatment efficacy and lowering harmful side effects, nanocarriers can be a useful drug delivery method.

Mechanistic map of nanochemical combination therapy in breast cancer (represented by the combination of paclitaxel and nanomaterials)

Moreover, keyword analysis has reviewed the development of nanomaterials in breast cancer. Before 2018, the main focus of research was still on the mechanisms of treatment resistance in people with breast cancer. Loading curcumin into solid lipid nanoparticles promotes the Bax/Bcl-2 ratio but reduces the expression of the cell cycle proteins CyclinD1 and cyclin-dependent kinase 4 (CDK4), which in turn improves the therapeutic outcome of breast cancer (Mitra and Dash 2018 ). It shows that research on nanomaterials and breast cancer was at a more macroscopic and superficial stage. In the medium term (2018–2021), the research focus gradually expands to the level of magnetic nanoparticles and gold nanoparticles, which have important applications in the diagnosis and treatment of breast cancer. Magnetic nanoparticles can be used in magnetic resonance imaging to help doctors diagnose tumors more accurately (Boosz et al. 2021 ). On the other hand, gold nanoparticles as the drug carriers can be used to deliver drugs precisely to the tumor site, improving therapeutic effects and reducing side effects (Javan Nikkhah and Thompson 2021 ). From here, the significance of nanomaterials in detecting and treating breast cancer will become clearer. Nanomaterials will play an important role in the immunotherapy of breast cancer between 2021 and 2024. They can act as carriers for immunomodulators or anticancer drugs, which contribute to increasing the concentration of the drug locally and reducing damage to healthy tissue (Vincenzo et al. 2023 ). Furthermore, nanomaterials can be designed the specific immunogenic properties that activate the immune system to identify and attack breast cancer cells. Tailored Nano-immunotherapy is expected to enhance therapeutic efficacy and decrease adverse effects in patients with breast cancer.

While there is no denying the importance that nanomaterials play in the detection and management of breast cancer, we think that there are still certain limitations to them. The selectivity and specificity of nanomaterials might have negative effects on cells other than cancer. The usefulness of nanomaterials in therapy and diagnosis may be impacted by their unknown biodistribution and metabolic pathways in vivo (Park et al. 2017 ). At the same time, not all nanomaterials can be effectively translated into clinical applications, and the process of translating nanomaterials into clinical applications involves extensive clinical trials that demand time and money (Shirwaiker et al. 2013 ). Additionally, there is still some literature that was not included, such as grey literature and relevant literature that was not included in the WoSCC database. Some recently published articles may receive more citations in the future, which may have led to the exclusion of some high-quality studies from this analysis. And the number of citations does not fully reflect the importance of an article, as some important articles may have few citations and self-citations may introduce bias (Brown et al. 2021 ).

The findings indicate that the number of publications on this subject increased significantly starting in 2015, with China and the USA exhibiting the highest productivity levels. In the treatment of breast cancer, nanomedicines have demonstrated improved drug uptake and retention, resulting in more effective targeting of the tumor tissue and reduced systemic toxicity. The treatment of metastatic breast cancer may benefit greatly from the use of multifunctional nanotherapeutic medications. Nanotechnology holds the significant potential in improving drug transport, releasing, and targeting, which could lead to higher treatment efficacy and fewer side effects. Targeting nanomaterial-based drugs could have a beneficial effect on breast cancer patients and could be a milestone in the diagnosis and treatment of breast cancer.

Availability of data and materials

All data generated or analyzed during this study are included in this article and additional data are available from the corresponding author upon reasonable request.

Abbreviations

Web of science core collection

Quantum dots

Social science citation index

Science citation index expanded

Link strength

The total link strength

Egyptian knowledge bank

Islamiv Azda University

Albumin-bound formulation of paclitaxel

Superparamagnetic iron-oxide nanoparticles

Cyclin-dependent kinase 4

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Acknowledgements

We thank Professor Zhenhua Lin [Key Laboratory of Pathobiology (Yanbian University), State Ethnic Affairs Commission] for revising this manuscript.

This research was supported by the National Natural Science Foundation of China (No.82160552), the National Natural Science Foundation of Jilin Province (YDZJ202201ZYTS245).

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LY and LXQ performed the literature search and collected the data. LY performed the statistical analysis and wrote the manuscript, and prepared Figs.  1 , 2 , 3 . LAQ and ZJY prepared Figs.  4 , 5 . LZH revised the manuscript. LY and YY designed the study and conceived and revised the manuscript. All authors contributed to the article and approved the final manuscript.

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Li, Y., Li, X., Li, A. et al. The research trends and future prospects of nanomaterials in breast cancer. Cancer Nano 15 , 24 (2024). https://doi.org/10.1186/s12645-024-00261-7

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Biotech is a dynamic industry that is driving scientific advancements and innovation in healthcare.

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Latest science news, discoveries and analysis

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