Potential Biomarker For Breast Cancer Research Articles

Comprehensive analysis of the metabolomics and transcriptomics uncovers the dysregulated network and potential biomarkers of Triple Negative Breast Cancer

Triple-negative breast cancer (TNBC) is known for its aggressive nature, lack of effective diagnostic tools and treatments, and generally poor prognosis. The objective of this study was to investigate metabolic changes in TNBC using metabolomics approaches and explore the underlying mechanisms through integrated analysis with transcriptomics. In this study, serum untargeted metabolic profiles were first examined between 18 TNBC patients and 21 healthy control (HC) subjects using liquid chromatography-mass spectrometry (LC-MS), identifying a total of 22 significantly differential metabolites (DMs). Subsequently, receiver operating characteristic analysis revealed that 7-methylguanine could serve as a potential biomarker for TNBC in both the discovery and validation sets. Additionally, transcriptomic datasets were retrieved from the GEO database to identify differentially expressed genes (DEGs) between TNBC and normal tissues. An integrative analysis of the DMs and DEGs was conducted, uncovering potential molecular mechanisms underlying TNBC. Notably, three pathways—tyrosine metabolism, phenylalanine metabolism, and glycolysis/gluconeogenesis—were enriched, providing insight into the energy metabolism disorders in TNBC. Within these pathways, two DMs (4-hydroxyphenylacetaldehyde and oxaloacetic acid) and six DEGs (MAOA, ADH1B, ADH1C, AOC3, TAT, and PCK1) were identified as key components. In summary, this study highlights metabolic biomarkers that could potentially be used for the diagnosis and screening of TNBC. The comprehensive analysis of metabolomics and transcriptomics data offers a validated and in-depth understanding of TNBC metabolism.

Untargeted metabolomic profiling of small extracellular vesicles reveals potential new biomarkers for triple negative breast cancer.

Breast Cancer (BC) is one of the most diagnosed malignancies among women and the second leading cause of cancer related death in North America. Triple Negative BC (TNBC), one of the most severe subtypes of BC, is extremely aggressive and has a higher chance of occurrence in women under 50 years of age. Due to a lack of regular mammographic testing in women under 50, many individuals with TNBC are diagnosed late which can decrease their survival rate. Currently, liquid biopsy is being investigated as a potentially less-invasive alternative to traditional breast tissue biopsy, but this approach is not completely reliable. Blood contains extracellular vesicles (EVs), which carry biomolecular cargo and play a role in BC progression and metastasis. Examination of small EVs could potentially yield metabolite biomarkers for early BC diagnosis. We aim to study metabolites in small EVs to find biomarkers for BC diagnosis. In this work, an untargeted nano-LC MS/MS metabolomics approach was used to analyze metabolites from small EVs derived from metastatic MDA-MB-231 and compare it with a non-cancerous MCF10A cell line. Two metabolites, LysoPC 22:6/0:0 and N-acetyl-L-Phenylalanine, unique to sEVs of MDA-MB-231, were identified, validated, and proposed as potential BC biomarkers. Metabolites from sEVs may be used for BC diagnosis.

Serum exosomal miR-200c is a potential diagnostic biomarker for breast cancer

Background Breast cancer (BC) is one of the most common malignancies in women. Exosomes are widely found in body fluids and carry microRNAs (miRNAs) that reflect the biological properties of the parental cells. Our study aimed to investigate the differential expression of miR-200c in BC serum exosomes and its diagnostic value. Methodology miRNA profiles in culture supernatant exosomes of normal mammary epithelial cells MCF-10A and BC cells (MCF-7, MDA-MB-231, MCF-7 Taxol) were examined by miRNA deep sequencing to screen for significantly differentially expressed miRNAs; Transmission electron microscopy (TEM), Nanoparticle tracking analysis (NTA), and Western blot were used to identify exosomes; qPCR was used to detect the expression level of miR-200c in cellular exosomes and serum exosomes; The efficacy of individual and combined tests of each indicator to diagnose BC was evaluated using receiver operating characteristic (ROC) curves. Results We identified typical exosome features by TEM, NTA and Western blot, indicating successful exosome extraction. Then our miRNA sequencing results and qRT-PCR experiments showed that miR-200c was significantly down-regulated in BC cell exosomes. In addition, we divided the clinical serum samples into two cohorts according to region, and in independent cohort I, the serum exosomal miR-200c levels of BC patients were significantly lower than those of healthy controls. In cohort II, serum exosomal miR-200c expression was significantly lower in the BC group than in the control and benign breast disease (BBD) groups, whereas miR-200c expression in the BBD group was not statistically different from that in the control group. ROC analyses in both independent cohorts confirmed that serum exosomal miR-200c could differentiate between patients with and without BC disease and could be used as an early diagnostic marker for BC disease. Conclusion Serum exosome miR-200c can be used as a potential biomarker for the diagnosis of BC, and combined with conventional serum diagnostic markers AFP, CA125 and CA153 can help to improve diagnostic efficiency.

Identifying new biomarkers and potential therapeutic targets for breast cancer through the integration of human plasma proteomics: a Mendelian randomization study and colocalization analysis.

The proteome is a crucial reservoir of targets for cancer treatment. While some targeted therapies have been developed, there are still significant challenges in early diagnosis and treatment, highlighting the need to identify new biomarkers and therapeutic targets for breast cancer. Therefore, we conducted a comprehensive proteome-wide Mendelian randomization (MR) study to identify novel biomarkers and potential therapeutic targets for breast cancer. Protein quantitative trait locus (pQTL) data were extracted from two published plasma proteome-wide association studies. Genetic variants associated with breast cancer were obtained from the Breast Cancer Association Consortium, which included 133,384 cases and 113,789 controls, and the Finnish cohort study, comprising 18,786 cases and 182,927 controls. We employed summary-based MR and colocalization methods to identify potential drug targets for breast cancer, which were subsequently validated using a two-sample MR approach. Finally, a protein-protein interaction (PPI) network was constructed to detect interactions between the identified proteins and existing cancer drug targets. Gene-predicted levels of ten proteins were associated with breast cancer risk. Decreased levels of CASP8, DDX58, CPNE1, ULK3, PARK7, and BTN2A1, as well as increased levels of TNFRSF9, TNXB, DNPH1, and TLR1, were linked to an elevated risk of breast cancer. Among these, CASP8 and DDX58 were supported by tier-one evidence, while CPNE1, ULK3, PARK7, and TNFRSF9 received tier-two evidence support. The remaining proteins, TNXB, BTN2A1, DNPH1, and TLR1, were supported by tier-three evidence. CASP8, DDX58, CPNE1, ULK3, PARK7, and TNFRSF9 have already been identified as targets in drug development and potential therapeutic targets for breast cancer treatment. Additionally, ULK3 showed promise as a prognostic biomarker for breast cancer. The present study identified several novel potential drug targets and biomarkers for breast cancer, providing new insights into its diagnosis and treatment. The integration of PPI and druggability evaluations enhances the prioritization of these therapeutic targets, paving the way for future drug development efforts.

Major considerations on the relationship of triple negative breast cancer with microRNAs and nutrological triggers: a systematic review

Introduction: Triple-negative breast cancer (TNBC) accounts for 15% of all cases. Its incidence is usually higher in young women (under 40 years of age). The classification as triple-negative occurs because, in this case, there are no estrogen, progesterone, and HER2 receptors, which are responsible for controlling tumor growth. Higher levels of physical activity and better diet quality are associated with lower mortality from breast cancer in observational studies. Furthermore, physical activity and optimal nutrition can improve the relative dose intensity of chemotherapy, as they can activate miRNAs that regulate several biological processes. Objective: This was to conduct a systematic review to establish the main considerations of the relationship between triple-negative breast cancer and microRNAs as biomarkers and regulators of gene expression in cancer cells, as well as to show some nutritional triggers of microRNA activation desirable for breast cancer control. Methods: The PRISMA Platform systematic review rules were followed. The search was conducted from April to June 2024 in the Web of Science, Scopus, PubMed, Science Direct, Scielo, and Google Scholar databases. The quality of the studies was based on the GRADE and AMSTAR-2 instrument and the risk of bias was analyzed appropriately. According to the Cochrane instrument. Results and Conclusion: 112 articles were found, and 40 were evaluated in full and included in this article, 22 of which were included in the systematic review. Considering the Cochrane tool for risk of bias, the global assessment resulted in 15 studies with a high risk of bias and 25 studies that did not reach GRADE and AMSTAR-2. Most studies showed homogeneity in their results, with X2=81.5%>50%. It was concluded that an increasing number of studies have shown that non-coding RNA (ncRNA), including microRNA (miRNA) and long non-coding RNA (lncRNA), play a significant role in tumorigenesis. Although a diet and exercise intervention did not affect relative dose intensity, the intervention was associated with a higher pCR in patients with hormone receptor-positive/human epidermal growth factor receptor 2-negative and triple-negative breast cancer undergoing neoadjuvant chemotherapy. High expression levels of miR-27a/b, miR-210, and miR-454 were associated with shorter overall survival, whereas high expression levels of miR-454 and miR-374a/b were associated with disease-free survival. The miRNAs associated with triple-negative breast cancer may provide new avenues for early diagnosis and treatment of breast cancer. Furthermore, specific miRNAs may serve as potential prognostic biomarkers in triple-negative breast cancer.

LINC01614 activated by SP1 promoted malignant behavior of triple-negative breast cancer cells via the WNT/b-Catenin signaling pathway.

Triple-negative breast cancer (TNBC) represents the most aggressive subtype of breast cancer (BC), characterized by a dismal prognosis. Dysregulated long non-coding RNA LINC01614 might be a potential biomarker for BC as previously reported. Nevertheless, its functions and mechanism in TNBC cells are unclear. The study aimed to study the effects of LINC01614 on TNBC cell migration, invasion, and epithelial-mesenchymal transition (EMT) process as well as the related mechanism. Reverse transcription quantitative polymerase chain reaction was performed to detect the expression of LINC01614 and SP1 in TNBC cells and tissues. The cellular localization of LINC01614 was determined by subcellular fraction assays. Cell counting kit-8 and Transwell invasion assays were conducted for measurement of TNBC cell viability and invasive ability. Cell migration was performed using wound healing assays and Transwell migration assays. Chromatin immunoprecipitation assays and luciferase reporter assays were used to explore the interaction between SP1 and LINC01614. Western blotting was used to assess protein levels of factors involved in EMT process and Wnt/ß-catenin signaling in TNBC cells. LINC01614 expression was elevated in TNBC tissues and cells. LINC01614 knockdown inhibited cell viability as well as migratory and invasive abilities of TNBC cells. LINC01614 knockdown also obstructed EMT process, as shown by E-cadherin upregulation and vimentin downregulation in TNBC cells. SP1 directly bound to the promoter of LINC01614 and activated LINC01614 expression. SP1 overexpression reversed the suppressive effect of LINC01614 knockdown on TNBC cell migration, invasion, and EMT process. Protein levels of Wnt and ß-catenin were diminished by LINC01614 knockdown, and the trend was partially rescued by SP1 overexpression. SP1-induced LINC01614 promoted malignant behavior of TNBC cells by activating the Wnt/ß-catenin signaling pathway.

LncRNA HAGLROS promotes breast cancer evolution through miR-135b-3p/COL10A1 axis and exosome-mediated macrophage M2 polarization

Long non-coding RNAs (lncRNAs) play an important role in breast cancer progression, but the function of lncRNAs in regulating tumor-associated macrophages (TAMs) remains unclear. As carriers of lncRNAs, exosomes play an important role as mediators in the communication between cancer cells and the tumor microenvironment. In this study, we found that lncRNA HAGLROS was highly expressed in breast cancer tissues and plasma exosomes, and its high expression was related to the poor prognosis of breast cancer patients. Functionally, breast cancer cell-derived exosomal lncRNA HAGLROS promotes breast cancer cell proliferation, migration, epithelial-mesenchymal transition (EMT) process and angiogenesis by inducing TAM/M2 polarization. Mechanistically, lncRNA HAGLROS competitively binds to miR-135-3p to prevent the degradation of its target gene COL10A1. Collectively, these results indicated that the lncRNA HAGLROS/miR-135b-3p/COL10A1 axis promoted breast cancer progression, and revealed the interactive communication mechanism between breast cancer cells and TAMs, suggesting that lncRNA HAGLROS may be a potential biomarker and therapeutic target for breast cancer.

The Overexpressed MicroRNAs miRs-182, 155, 493, 454, and U6 snRNA and Underexpressed let-7c, miR-328, and miR-451a as Potential Biomarkers in Invasive Breast Cancer and Their Clinicopathological Significance.

Breast cancer comprises the leading cause of cancer-related death in women. MicroRNAs (miRNAs) have emerged as important factors with concern to carcinogenesis and have potential for use as biomarkers. This study provides a comprehensive evaluation of the microRNA expression in invasive breast carcinoma of no special type tissues compared with benign tissues via large-scale screening and the candidate-specific validation of 15 miRNAs and U6 snRNA applying qPCR and the examination of clinicopathological data. Of the six downregulated miRNAs, let-7c was identified as the most promising miRNA biomarker and its lower expression was linked with Ki-67 positivity, luminal B versus luminal A samples, multifocality, lymph node metastasis, and inferior PFS. Of the 9 upregulated sncRNAs, the data on U6 snRNA, miR-493 and miR-454 highlighted their potential oncogenic functions. An elevated U6 snRNA expression was associated with the tumor grade, Ki-67 positivity, luminal B versus A samples, lymph node metastasis, and worsened PFS (and OS) outcomes. An elevated miR-454 expression was detected in higher grades, Ki-67 positive and luminal B versus A samples. Higher miR-493 levels were noted for the tumor stage (and grade) and worse patient outcomes (PFS, OS). The data also suggested that miR-451a and miR-328 may have tumor suppressor roles, and miR-182 and miR-200c pro-oncogenic functions, while the remaining sncRNAs did not evince any significant associations. We showed particular microRNAs and U6 snRNA as differentially expressed between tumors and benign tissues and associated with clinicopathological parameters, thus potentially corresponding with important roles in breast carcinogenesis. Their importance should be further investigated and evaluated in follow-up studies to reveal their potential in clinical practice.

Computational Analyses Reveal Deregulated Clock Genes Associated with Breast Cancer Development in Night Shift Workers.

Breast cancer (BC) is the leading cause of cancer death among women worldwide. Women employed in shift jobs face heightened BC risk due to prolonged exposure to night shift work (NSW), classified as potentially carcinogenic by the International Agency for Research on Cancer (IARC). This risk is linked to disruptions in circadian rhythms governed by clock genes at the cellular level. However, the molecular mechanisms are unclear. This study aimed to assess clock genes as potential BC biomarkers among women exposed to long-term NSW. Clock gene expression was analysed in paired BC and normal breast tissues within Nurses' Health Studies I and II GEO datasets. Validation was performed on additional gene expression datasets from healthy night shift workers and women with varying BC susceptibility, as well as single-cell sequencing datasets. Post-transcriptional regulators of clock genes were identified through miRNA analyses. Significant alterations in clock gene expression in BC compared to normal tissues were found. BHLHE40, CIART, CLOCK, PDPK1, and TIMELESS were over-expressed, while HLF, NFIL3, NPAS3, PER1, PER3, SIM1, and TEF were under-expressed. The downregulation of PER1 and TEF and upregulation of CLOCK correlated with increased BC risk in healthy women. Also, twenty-six miRNAs, including miR-10a, miR-21, miR-107, and miR-34, were identified as potential post-transcriptional regulators influenced by NSW. In conclusion, a panel of clock genes and circadian miRNAs are suggested as BC susceptibility biomarkers among night shift workers, supporting implications for risk stratification and early detection strategies.

A novel sandwich electrochemical immunosensor utilizing customized template and phosphotungstate catalytic amplification for CD44 detection

A sandwich-type electrochemical immunosensor was proposed for the ultra-sensitive detection of CD44, a potential biomarker for breast cancer. In this design, a customized template-based ionic liquid (1-butyl-2,3-dimethylimidazolium tetrafluoroborate) carbon paste electrode (CILE) served as the sensing platform, and thionine/Au nanoparticles/covalent-organic frameworks (THI/Au/COF) were used as the signal label. Moreover, an enzyme-free signal amplification strategy was introduced by involving H2O2 and phosphotungstate (PW12) with peroxidase-like activity. Under optimized conditions, the linear range is as wide as six orders of magnitude, and the detection limit is as low as 0.71 pg mL−1 (estimated based on S/N = 3). Average recoveries range from 98.16 %-100.1 %, with a relative standard deviation (RSD) of 1.42–8.27 % in mouse serum, and from 98.44 %-99.06 %, with an RSD of 1.14–4.84 % (n = 3) in artificial saliva. Furthermore, the immunosensor exhibits excellent specificity toward CD44, good stability, and low cost, indicating great potential for application in clinical trials.

Association of XRCC2 with breast cancer, a multi-omics analysis at genomic, transcriptomic, and epigenomic level.

One of the main causes of cancer-related mortality for women worldwide is breast cancer (BC). The XRCC2 gene, essential for DNA repair, has been implicated in cancer susceptibility. This study aims to evaluate the association between XRCC2 and BC risk. The study was conducted at Zheen International Hospital in Erbil, Iraq, between 2021 and 2024 with a total of 88 samples, including 44 paired normal and cancer tissue samples. Mutation analysis was performed using Next-Generation Sequencing, coupled with in silico tools for variant impact prediction. Expression levels were assessed through RT-PCR, and methylation status was determined using methylation-sensitive restriction enzyme digestion PCR. The study identified seven inherited germline variants in the XRCC2 gene, with five of these mutations being Uncertain Significance, one being Likely Pathogenic, and one being Likely benign. RNA purity was found high with mean A260/280 ratios of 1.986 ± 0.097 in normal (N) and 1.963 ± 0.092 in tumor (T) samples. Tumor samples exhibited a higher RNA concentration (78.56 ± 40.87 ng/µL) than normal samples (71.44 ± 40.79 ng/µL). XRCC2 gene expression was significantly upregulated in tumor tissue, with marked increases in patients aged 40-55 and >56 years and in higher cancer grades (II and III) and invasive ductal carcinoma (p-values ranging from <0.0001 to 0.0392). DNA methylation rates in tumor tissues were low (7%), suggesting limited regulation by methylation. The study suggests that XRCC2 can be classified as an oncogene and that its structural investigation by targeted NGS and expression evaluation can be used as a potential biomarker in BC.

Systematic characterization of the expression, prognosis and immune characteristics of PLOD family genes in breast cancer.

The expression patterns and prognostic value of Procollagen-lysine, 2-oxoglutarate 5-dioxygenase (PLOD) family genes in breast cancer remain to be elucidated. The expression levels, prognostic value, and biological function of PLODs were determined using Oncomine, cBioPortal, GEPIA, Timer, UALCAN, PrognoScan, GeneMANIA, Metascape, and breast cancer tissue microarrays. The expressions of PLOD1 and PLOD3 were upregulated in breast cancer tissues, indicating worse clinical stages. High expression levels of PLOD family genes were associated with worse disease-free survival and distant metastasis-free survival, while high expression levels of PLOD1 and PLOD3 were related to worse overall survival in all breast cancer patients. The levels of PLOD family genes were all significantly higher in the age ≤51 y group, HR-negative patients, and triple negative breast cancer (TNBC) patients. They are associated with tumor-infiltrating immune cells (TIICs), including CD4+ T cells, CD8+ T cells, B cells, macrophages, neutrophils, and dendritic cells. According to co-expression gene analysis and functional enrichment, they are associated with protein hydroxylation, collagen biosynthesis and modifying enzymes, collagen metabolism, RNA splicing, extracellular matrix organization, VEGFA-VEGFR2 signaling pathway, and skeletal system development. Immunohistochemistry showed that the expressions of all PLOD family genes were significantly elevated in breast cancer tissues. PLOD1 expression was positively correlated with ER, TNBC status, and tumor grade. PLOD2 expression was positively connected with Ki-67 status. PLOD3 expression was positively related with age and tumor grade. PLOD family genes are novel potential prognostic biomarkers for breast cancer, and targeting PLOD inhibitors might be an effective strategy for breast cancer therapy.

Identification of Tumor Budding-Associated Genes in Breast Cancer through Transcriptomic Profiling and Network Diffusion Analysis.

Breast cancer has the highest diagnosis rate among all cancers. Tumor budding (TB) is recognized as a recent prognostic marker. Identifying genes specific to high-TB samples is crucial for hindering tumor progression and metastasis. In this study, we utilized an RNA sequencing technique, called TempO-Seq, to profile transcriptomic data from breast cancer samples, aiming to identify biomarkers for high-TB cases. Through differential expression analysis and mutual information, we identified seven genes (NOL4, STAR, C8G, NEIL1, SLC46A3, FRMD6, and SCARF2) that are potential biomarkers in breast cancer. To gain more relevant proteins, further investigation based on a protein-protein interaction network and the network diffusion technique revealed enrichment in the Hippo signaling and Wnt signaling pathways, promoting tumor initiation, invasion, and metastasis in several cancer types. In conclusion, these novel genes, recognized as overexpressed in high-TB samples, along with their associated pathways, offer promising therapeutic targets, thus advancing treatment and diagnosis for breast cancer.

KCNK1 promotes proliferation and metastasis of breast cancer cells by activating lactate dehydrogenase A (LDHA) and up-regulating H3K18 lactylation.

Breast cancer is the most prevalent malignancy and the most significant contributor to mortality in female oncology patients. Potassium Two Pore Domain Channel Subfamily K Member 1 (KCNK1) is differentially expressed in a variety of tumors, but the mechanism of its function in breast cancer is unknown. In this study, we found for the first time that KCNK1 was significantly up-regulated in human breast cancer and was correlated with poor prognosis in breast cancer patients. KCNK1 promoted breast cancer proliferation, invasion, and metastasis in vitro and vivo. Further studies unexpectedly revealed that KCNK1 increased the glycolysis and lactate production in breast cancer cells by binding to and activating lactate dehydrogenase A (LDHA), which promoted histones lysine lactylation to induce the expression of a series of downstream genes and LDHA itself. Notably, increased expression of LDHA served as a vicious positive feedback to reduce tumor cell stiffness and adhesion, which eventually resulted in the proliferation, invasion, and metastasis of breast cancer. In conclusion, our results suggest that KCNK1 may serve as a potential breast cancer biomarker, and deeper insight into the cancer-promoting mechanism of KCNK1 may uncover a novel therapeutic target for breast cancer treatment.

NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset

Background and ObjectiveBreast cancer is a multifaceted condition characterized by diverse features and a substantial mortality rate, underscoring the imperative for timely detection and intervention. The utilization of multi-omics data has gained significant traction in recent years to identify biomarkers and classify subtypes in breast cancer. This kind of research idea from part to whole will also be an inevitable trend in future life science research. Deep learning can integrate and analyze multi-omics data to predict cancer subtypes, which can further drive targeted therapies. However, there are few articles leveraging the nature of deep learning for feature selection. Therefore, this paper proposes a Neural Network and Binary grey Wolf Optimization based BReast CAncer bioMarker (NNBGWO-BRCAMarker) discovery framework using multi-omics data to obtain a series of biomarkers for precise classification of breast cancer subtypes. MethodsNNBGWO-BRCAMarker consists of two phases: in the first phase, relevant genes are selected using the weights obtained from a trained feedforward neural network; in the second phase, the binary grey wolf optimization algorithm is leveraged to further screen the selected genes, resulting in a set of potential breast cancer biomarkers. ResultsThe SVM classifier with RBF kernel achieved a classification accuracy of 0.9242 ± 0.03 when trained using the 80 biomarkers identified by NNBGWO-BRCAMarker, as evidenced by the experimental results. We conducted a comprehensive gene set analysis, prognostic analysis, and druggability analysis, unveiling 25 druggable genes, 16 enriched pathways strongly linked to specific subtypes of breast cancer, and 8 genes linked to prognostic outcomes. ConclusionsThe proposed framework successfully identified 80 biomarkers from the multi-omics data, enabling accurate classification of breast cancer subtypes. This discovery may offer novel insights for clinicians to pursue in further studies.

Efficient serum lipids profiling by TiO2-dopamin-assisted MALDI-TOF MS for breast cancer detection

Lipids serve as fundamental constituents of cell membranes and organelles. Recent studies have highlighted the significance of lipids as biomarkers in the diagnosis of breast cancer. Although liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is widely employed for lipid analysis in complex samples, it suffers from limitations such as complexity and time-consuming procedures. In this study, we have developed dopamine-modified TiO2 nanoparticles (TiO2-DA) and applied the materials to assist the analysis of lipids by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The TiO2-DA can provide large specific surface area and acidic environment, well suited for lipid analysis. The method was initially validated using standard lipid molecules. Good sensitivity, reproducibility and quantification performance was observed. Then, the method was applied to the analysis of 90 serum samples from 30 patients with breast cancer, 30 patients with benign breast disease and 30 healthy controls. Five lipid molecules were identified as potential biomarkers for breast cancer. We constructed a classification model based on the MALDI-TOF MS signal of the 5 lipid molecules, and achieved high sensitivity, specificity and accuracy for the differentiation of breast cancer from benign breast disease and healthy control. We further collected another 60 serum samples from 20 healthy controls, 20 patients with benign breast disease and 20 patients with breast cancer for MALDI-TOF MS analysis to verify the accuracy of the classification model. This advancement holds great promise for the development of diagnostic models for other lipid metabolism-related diseases.

Plasma microRNA signature as a potential early detection biomarker in breast cancer.

e13780 Background: The 5-year survival rate for women with stage I breast cancer (BC) is 99%, falling to 27% if the disease is diagnosed in stage IV. This demonstrates the importance of early detection for the effectiveness of BC treatment. Among the new diagnostic strategies, the use of liquid biopsies is emerging as a promising alternative for early cancer detection and patient follow-up. miRNAs are small non-coding RNA molecules useful as biomarkers, since they can be found in body fluids. Oncoliq’s approach focuses on the relative quantification of a panel of circulating miRNA biomarkers for the early detection of BC using predictive models built with machine learning techniques. Methods: The aim of this work was to identify a highly sensitive miRNA combination based on liquid biopsies together with predictive models, to generate an assay for BC early detection which we named Oncoliq breast. Human plasma samples (n=239) from BC patients and healthy donors (HD) were divided into two cohorts: exploratory (n=66) and validation (n=173). The exploratory cohort was used for the identification of miRNAs using expression microarrays and miRNA-seq. The validation cohort was used for the analytical validation of potential candidates by RT-qPCR. Results: Using ROC curve analysis, 11 miRNAs were identified as potential biomarkers. By RT-qPCR, we validated the expression of the 11 miRNAs. All of them were significantly increased in BC patient samples (n=100) compared to HD (n=73), in accordance with expression microarrays and miRNA-seq results. Using machine learning we established 7 predictive models that combine these miRNAs. After running all the predictive models using logistic regression, the metrics associated with each one were calculated. From these, model 7 showed a sensitivity of 90%, a specificity of 73% and an accuracy of 83%. Conclusions: MiRNAs show satisfactory advantages as biomarkers as they can circulate in body fluids, such as blood and urine. In the presence of diseases such as BC, the level of some specific miRNAs might be altered and this change can be harnessed for detection, diagnosis and prognosis. Here, we generated a panel of 11 miRNAs that showed increased expression in plasma samples from women diagnosed with BC. Oncoliq breast is a promising low cost and highly sensitive novel liquid biopsy test based on circulating miRNAs for BC screening.

Comparison of Breast Fine-Needle Aspiration Cytology and Tissue Sampling for High-Throughput Proteomic Analysis and Cancer Biomarker Detection

Introduction: Fine-needle aspiration cytology (FNAC) specimens are widely utilized for the diagnosis and molecular testing of various cancers. We performed a comparative proteomic analysis of three different sample types, including breast FNAC, core needle biopsy (CNB), and surgical resection tissues. Our goal was to evaluate the suitability of FNAC for in-depth proteomic analysis and for identifying potential therapeutic biomarkers in breast cancer. Methods: High-throughput proteomic analysis was conducted on matched FNAC, CNB, and surgical resection tissue samples obtained from breast cancer patients. The protein identification, including currently established or promising therapeutic targets, was compared among the three different sample types. Gene Ontology (GO) enrichment analysis was also performed on all matched samples. Results: Compared to tissue samples, FNAC testing revealed a comparable number of proteins (7,179 in FNAC; 7,196 in CNB; and 7,190 in resection samples). Around 85% of proteins were mutually identified in all sample types. FNAC, along with CNB, showed a positive correlation between the number of enrolled tumor cells and identified proteins. In the GO analysis, the FNAC samples demonstrated a higher number of genes for each pathway and GO terms than tissue samples. CCND1, CDK6, HER2, and IGF1R were found in higher quantities in the FNAC compared to tissue samples, while TUBB2A was only detected in the former. Conclusion: FNAC is suitable for high-throughput proteomic analysis, in addition to an emerging source that could be used to identify and quantify novel cancer biomarkers.

Involvement of N4BP2L1, PLEKHA4, and BEGAIN genes in breast cancer and muscle cell development.

Patients with breast cancer show altered expression of genes within the pectoralis major skeletal muscle cells of the breast. Through analyses of The Cancer Genome Atlas (TCGA)-breast cancer (BRCA), we identified three previously uncharacterized putative novel tumor suppressor genes expressed in normal muscle cells, whose expression was downregulated in breast tumors. We found that NEDD4 binding protein 2-like 1 (N4BP2L1), pleckstrin homology domain-containing family A member 4 (PLEKHA4), and brain-enriched guanylate kinase-associated protein (BEGAIN) that are normally highly expressed in breast myoepithelial cells and smooth muscle cells were significantly downregulated in breast tumor tissues of a cohort of 50 patients with this cancer. Our data revealed that the low expression of PLEKHA4 in patients with menopause below 50years correlated with a higher risk of breast cancer. Moreover, we identified N4BP2L1 and BEGAIN as potential biomarkers of HER2-positive breast cancer. Furthermore, low BEGAIN expression in breast cancer patients with blood fat, heart problems, and diabetes correlated with a higher risk of this cancer. In addition, protein and RNA expression analysis of TCGA-BRCA revealed N4BP2L1 as a promising diagnostic protein biomarker in breast cancer. In addition, the in silico data of scRNA-seq showed high expression of these genes in several cell types of normal breast tissue, including breast myoepithelial cells and smooth muscle cells. Thus, our results suggest their possible tumor-suppressive function in breast cancer and muscle development.

MIF as a potential diagnostic and prognostic biomarker for triple-negative breast cancer that correlates with the polarization of M2 macrophages.

Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine that plays a crucial role in antitumor immunity. However, the role of MIF in influencing the tumor microenvironment (TME) and prognosis of triple-negative breast cancer (TNBC) remains to be elucidated. Using R, we analyzed single-cell RNA sequencing (scRNA-seq) data of 41 567 cells from 10 TNBC tumor samples and spatial transcriptomic data from two patients. Relationships between MIF expression and immune cell infiltration, clinicopathological stage, and survival prognosis were determined using samples from The Cancer Genome Atlas (TCGA) and validated in a clinical cohort using immunohistochemistry. Analysis of scRNA-seq data revealed that MIF secreted by epithelial cells in TNBC patients could regulate the polarization of macrophages into the M2 phenotype, which plays a key role in modulating the TME. Spatial transcriptomic data also showed that epithelial cells (tumor cells) and MIF were proximally located. Analysis of TCGA samples confirmed that tumor tissues of patients with high MIF expression were enriched with M2 macrophages and showed a higher T stage. High MIF expression was significantly associated with poor patient prognosis. Immunohistochemical staining showed high MIF expression was associated with younger patients and worse clinicopathological staging. MIF secreted by epithelial cells may represent a potential biomarker for the diagnosis and prognosis of TNBC and may promote TNBC invasion by remodeling the tumor immune microenvironment.