Keying Modulation Research Articles

Identification of SPP1+ macrophages as an immune suppressor in hepatocellular carcinoma using single-cell and bulk transcriptomics

IntroductionMacrophages and T cells play crucial roles in liver physiology, but their functional diversity in hepatocellular carcinoma (HCC) remains largely unknown.MethodsTwo bulk RNA-sequencing (RNA-seq) cohorts for HCC were analyzed using gene co-expression network analysis. Key gene modules and networks were mapped to single-cell RNA-sequencing (scRNA-seq) data of HCC. Cell type fraction of bulk RNA-seq data was estimated by deconvolution approach using single-cell RNA-sequencing data as a reference. Survival analysis was carried out to estimate the prognosis of different immune cell types in bulk RNA-seq cohorts. Cell-cell interaction analysis was performed to identify potential links between immune cell types in HCC.ResultsIn this study, we analyzed RNA-seq data from two large-scale HCC cohorts, revealing a major and consensus gene co-expression cluster with significant implications for immunosuppression. Notably, these genes exhibited higher enrichment in liver macrophages than T cells, as confirmed by scRNA-seq data from HCC patients. Integrative analysis of bulk and single-cell RNA-seq data pinpointed SPP1+ macrophages as an unfavorable cell type, while VCAN+ macrophages, C1QA+ macrophages, and CD8+ T cells were associated with a more favorable prognosis for HCC patients. Subsequent scRNA-seq investigations and in vitro experiments elucidated that SPP1, predominantly secreted by SPP1+ macrophages, inhibits CD8+ T cell proliferation. Finally, targeting SPP1 in tumor-associated macrophages through inhibition led to a shift towards a favorable phenotype.DiscussionThis study underpins the potential of SPP1 as a translational target in immunotherapy for HCC.

Automated Visceral Adipose Tissue Segmentation and Quantification from Abdominal MRI using an Enhanced U-Net and Region-growing

The study focuses on enhancing the accuracy and reliability of visceral adipose tissue (VAT) segmentation and quantification from abdominal MRI images. Accurate segmentation of VAT is crucial for assessing obesity-related health risks, as traditional methods struggle with irregular shapes and varying intensities. The research utilizes a methodology consisting of three key modules: homomorphic filtering for intensity inhomogeneity correction, a U-Net architecture with attention mechanisms for primary segmentation, and a region-growing algorithm for refining segmentation. Homomorphic filtering effectively separates bias fields, enhancing image quality by transforming multiplicative artifacts into additive ones and removing them with high-pass filtering. This process ensures precise segmentation by maintaining high-frequency anatomical details. The U-Net model incorporates attention mechanisms and skip connections to focus on VAT regions, utilizing both local and global image contexts.The Combined Healthy Abdominal Organ Segmentation (CHAOS) challenge dataset and the Cancer Imaging Archive (TCIA) dataset are used to train and evaluate the model. It achieves a Dice Similarity Coefficient (DSC) of up to 0.985 on the CHAOS dataset and 0.972 on the TCIA dataset, outperforming existing methods in terms of segmentation accuracy. The region-growing algorithm further refines the segmentation by expanding VAT regions from high-confidence seed points, ensuring accurate boundary delineation and reducing noise. The study's results, evaluated using k-fold cross-validation, show that the proposed methodology significantly improves VAT segmentation efficiency, achieving a median DSC of 0.96 for the CHAOS dataset and 0.95 for the TCIA dataset in the most comprehensive experimental scenario. Comparative analysis indicates that the proposed approach outperforms other models, with higher sensitivity and specificity values, highlighting its potential for clinical applications in obesity management..

Rate-Compatible, Bandwidth-Efficient, Low-Density Parity-Check (LDPC) Codes for Aeronautical Telemetry

Low-density parity-check (LDPC) codes form part of the IRIG-106 standard and have been successfully deployed for the Telemetry Group version of shaped-offset quadrature phase shift keying (SOQPSK-TG) modulation. Recently, LDPC code solutions have been proposed and optimized for continuous phase modulations (CPMs), including pulse code modulation/frequency modulation (PCM/FM) and the multi-h CPM developed by the Advanced-Range TeleMetry program (ARTM CPM), the latter of which was shown to perform around one dB from channel capacity. In this paper, we consider the effect of the random puncturing and shortening of these LDPC codes to further improve spectrum efficiency. We perform asymptotic analyses of the ARTM0 code ensembles and present numerical simulation results that affirm the robust decoding performance promised by LDPC codes designed for ARTM CPM.

Machine learning model based on the time domain regular perturbation-based theory for performance estimation in arbitrary heterogeneous optical links

To meet the required high demands for the capacity of optical networks, there are several efforts in recent years to further reduce the system margin. To achieve this goal, a fast and reliable QoT estimation tool is needed. The key module of such QoT tool is the nonlinear interference variance estimation. This paper presents a novel machine learning tool to speed up the exact model by six orders of magnitude without scarifying the accuracy and the scalability of this semi-analytical model. Moreover, to further enhance the scalability of the model, we used the cross-correlation functions to re-write the equations. The proposed ML-based framework KerrNet, based on a bank of small ANNs, can handle any arbitrary heterogeneous link up to ten thousand km composed of different fiber span. The transmitting C-band WDM channels in both fully loaded and sparsely occupied configurations are evaluated. The crucial steps for the machine learning algorithm to converge, which are the data preparation and the choice of training data, are presented in detail.

Detection of low-power RF signals using active mode-locking based coupled optoelectronic oscillator

A novel method to detect and amplify broadband low power radio frequency (RF) signal with high gain based on coupled optoelectronic oscillator (COEO) is proposed and experimentally demonstrated. When the frequency of the weak signal matches the COEO oscillation mode, it can obtain the high gain of active mode-locking in the COEO optical loop. The system can selectively detect and amplify RF signals from 1 to 10 GHz by observing the optical pulse repetition rate state. The experimental results show that sensitivity of detection as low as −89 dBm with a maximum gain of 34.32 dB at 1.002 GHz, and an average gain of 30.52 dB for low power RF signals. The real application of the method for detecting low power Quadrature Phase Shift Keying (QPSK) modulation is investigated and the spectral waveforms is discussed.

A memristor‐sourced key for encrypted power and data synchronous transmission in switched mode power supplies

AbstractIn power and data synchronous transmission (PDST), it is crucial to modulate additional signals onto the voltage bus of the converters. This reduces the cost of monitoring and control of the power grid. There is thus the need to ensure that the data transmitted is well secured. This study, designs an electronic system that uses the common coupling point for PDST. Then Chua's memristive circuit is introduced as an entropy source for generating a random bit sequence for encrypted PDST in a switched mode power supply. The encrypted data signal is modulated using phase shift key modulation. Thereafter, the carrier signal is pulse width modulation modulated to control the switching operation of the power switch. The use of Chua's memristive circuit for generating a random number is not new; however, there is little study directly adopting its use in PDST. This allows for resource sharing since multiple signal sources can share the same bus without interference or security risk. The generated random bits are tested for randomness using National Institute of Standards and Technology test suite and autocorrelation test. MATLAB/Simulink and experimental results both confirm the effectiveness of the proposed data encryption method.

Multi-modal transcriptomics: integrating machine learning and convolutional neural networks to identify immune biomarkers in atherosclerosis

BackgroundAtherosclerosis, a complex chronic vascular disorder with multifactorial etiology, stands as the primary culprit behind consequential cardiovascular events, imposing a substantial societal and economic burden. Nevertheless, our current understanding of its pathogenesis remains imprecise. In this investigation, our objective is to establish computational models elucidating molecular-level markers associated with atherosclerosis. This endeavor involves the integration of advanced machine learning techniques and comprehensive bioinformatics analyses.Materials and methodsOur analysis incorporated data from three publicly available the Gene Expression Omnibus (GEO) datasets: GSE100927 (104 samples, 30,558 genes), which includes atherosclerotic lesions and control arteries from carotid, femoral, and infra-popliteal arteries of deceased organ donors; GSE43292 (64 samples, 23,307 genes), consisting of paired carotid endarterectomy samples from 32 hypertensive patients, comparing atheroma plaques and intact tissues; and GSE159677 (30,498 single cells, 33,538 genes), examining single-cell transcriptomes of calcified atherosclerotic core plaques and adjacent carotid artery tissues from patients undergoing carotid endarterectomy. Utilizing single-cell sequencing, highly variable atherosclerotic monocyte subpopulations were systematically identified. We analyzed cellular communication patterns with temporal dynamics. The bioinformatics approach Weighted Gene Co—expression Network Analysis (WGCNA) identified key modules, constructing a Protein-Protein Interaction (PPI) network from module-associated genes. Three machine-learning models derived marker genes, formulated through logistic regression and validated via convolutional neural network(CNN) modeling. Subtypes were clustered based on Gene Set Variation Analysis (GSVA) scores, validated through immunoassays.ResultsThree pivotal atherosclerosis-associated genes—CD36, S100A10, CSNK1A1—were unveiled, offering valuable clinical insights. Profiling based on these genes delineated two distinct isoforms: C2 demonstrated potent microbicidal activity, while C1 engaged in inflammation regulation, tissue repair, and immune homeostasis. Molecular docking analyses explored therapeutic potential for Estradiol, Zidovudine, Indinavir, and Dronabinol for clinical applications.ConclusionThis study introduces three signature genes for atherosclerosis, shaping a novel paradigm for investigating clinical immunological medications. It distinguishes the high biocidal C2 subtype from the inflammation-modulating C1 subtype, utilizing identified signature gene as crucial targets.

Performance of underwater data transmission using incoherent modulation MFSK in very shallow waters

Ensuring universal and stable underwater communication in shallow waters for various environmental conditions is a difficult scientific and engineering task. This applies in particular to underwater communication systems that use acoustic waves in very shallow underwater channels, where multipath propagation permanently occurs. The article provides assumptions for a system working with incoherent M-ary Frequency-Shift Keying (MFSK) modulation, along with guidelines for eliminating the impact of the multipath phenomenon. The results of experimental tests carried out in a lake for two seasons and, therefore, different sound velocity profiles are presented. For comparison purposes, three transducers placed at different depths, including at the bottom of the reservoir, were used to receive the transmitted signals.

RIS-assisted differential transmitted spatial modulation design

In this paper, we propose a novel reconfigurable intelligent surface (RIS)-assisted wireless communication design called the RIS-assisted differential transmitted spatial modulation (DTSM) scheme. The encoding process of the differential spatial modulation (DSM) is integrated into the DTSM scheme, where only one transmit antenna is activated per time slot to transmit the M-ary phase shift keying (PSK) modulation symbol through the RIS. Due to the detection characteristics of DSM, the bit error rate (BER) performance remains satisfactory without requiring channel state information estimation, thereby enhancing robustness. The RIS application in the proposed scheme mitigates the effects of shadow area fading by adjusting the phase of the reflected signals to improve the signal-to-noise ratio at the receiver. In simulation results by comparing to other RIS-assisted spatial modulation schemes, we can find that the proposed DTSM scheme demonstrates good BER performance across various scenarios, including Nakagami-m fading, which also indicates its potential for practical applications.

High-order direct modulation terahertz communications with a wideband time-coding metachip modulator.

Terahertz communication technology based on on-off keying (OOK) direct modulation is vital for sixth-generation communication systems, especially in short-distance and high-rate applications. However, low-order OOK modulation often leads to suboptimal anti-interference capabilities and a heightened demodulation threshold. Here, we propose a high-order direct modulation terahertz communication framework using a wideband time-coding metachip modulator. The modulator leverages the electromagnetic resonance properties within the metaunit structure, with control enabled by gallium arsenide Schottky diodes. By manipulating the timing of voltage pulses applied to these diodes, the equivalent electromagnetic resonance distributions can be precisely regulated in the time domain. This enables independent and accurate control over the amplitude and phase of terahertz harmonics. Leveraging this technique, three high-order modulation schemes-quadrature phase-shift keying, 16-phase-shift keying, and 16-quadrature amplitude modulation- are achieved in a direct modulation and direct detection system, demonstrating the real-time image transmission. The proposed method offers an important way to develop integrated and low-complexity terahertz wireless communication systems.

Lignocellulose-Mediated Functionalization of Liquid Metals toward the Frontiers of Multifunctional Materials.

Lignocellulose-mediated liquid metal (LM) composites, as emerging functional materials, show tremendous potential for a variety of applications. The abundant hydroxyl, carboxyl, and other polar groups in lignocellulose facilitate the formation of strong chemical bonds with LM surfaces, enhancing wettability and adhesion for improved interface compatibility. Beyond serving as a supportive matrix, lignocellulose can be tailored to optimize the microstructure of the composites, adapting them for diverse applications. This review comprehensively summarizes the fundamental principles and recent advancements in lignocellulose-mediated LM composites, highlighting the advantages of lignocellulose in composite fabrication, including facile synthesis, versatile interactions, and inherent functionalities. Key modulation strategies for LMs and innovative synthesis methods for functionalized lignocellulose composites are discussed. Furthermore, the roles and structure-performance relationships of these composites in electromagnetic shielding, flexible sensors, and energy storage devices are systematically summarized. Finally, the obstacles and prospective advancements pertaining to lignocellulose-mediated LM composites are thoroughly scrutinized and deliberated upon. This review is expected to provide basic guidance for researchers to boost the popularity of LMs in diverse applications and provide useful references for design strategies of state-of-the-art LMs.

A fuzzy decision-making network model for offshore wind turbine selection based on simulated annealing algorithm

As global demand for renewable energy surges, offshore wind power has become an indispensable component in the transition towards a more sustainable energy landscape. Selecting appropriate wind turbines for offshore wind power facilities is a critical process influenced by various factors, including turbine parameters, environmental conditions, and economic indicators. Traditional decision-making methods often fall short in addressing the complexity and dynamic nature of real-world scenarios, relying heavily on expert experience. This study proposes a novel and scalable method for offshore wind turbine selection by leveraging a fuzzy decision-making network and a conditional probability table optimized through a simulated annealing algorithm. The method is structured into three key modules. It allows for learning from existing similar cases and transferring knowledge to the current selection task in the absence of expert opinion. It has proven its worth in a case study at the Rudong Wind Farm in Jiangsu Province, China. The results show that this method can account for the complexities inherent in turbine selection and serve as a more comprehensive and adaptable tool for decision-makers. This study contributes to the renewable energy sector by providing a novel, data-driven, and practical method for offshore wind turbine selection and other real-world related applications.

Sevoflurane exposure in early life: mitochondrial dysfunction and neurotoxicity in immature rat brains without long-term memory loss

Neurotoxic effects of general anesthetics, particularly sevoflurane, on pediatric neurodevelopment are a global concern. This study investigated the molecular and metabolic impacts of repeated short exposures to sevoflurane in neonatal rats. Metabolomics analysis revealed significant changes in fatty acid and mitochondrial energy metabolism. Transcriptomic analysis identified altered gene expression related to neurodevelopment and mitochondrial function. Various analyses emphasized upregulation in oxidative phosphorylation and DNA repair pathways. Weighted gene co-expression network analysis (WGCNA) identified key gene modules associated with sevoflurane exposure. Despite these acute changes, no significant long-term memory impairments were detected. These findings highlight the impact of sevoflurane on mitochondrial energy metabolism, oxidative stress, and neuroinflammation, emphasizing its relevance to pediatric neurodevelopment. The absence of substantial long-term memory impairments provides insights into the safety and implications of sevoflurane use in pediatric anesthesia, calling for further research.

DDKG: A Dual Domain Knowledge Guidance strategy for localization and diagnosis of non-displaced femoral neck fractures

X-ray is the primary tool for diagnosing fractures, crucial for determining their type, location, and severity. However, non-displaced femoral neck fractures (ND-FNF) can pose challenges in identification due to subtle cracks and complex anatomical structures. Most deep learning-based methods for diagnosing ND-FNF rely on cropped images, necessitating manual annotation of the hip location, which increases annotation costs. To address this challenge, we propose Dual Domain Knowledge Guidance (DDKG), which harnesses spatial and semantic domain knowledge to guide the model in acquiring robust representations of ND-FNF across the whole X-ray image. Specifically, DDKG comprises two key modules: the Spatial Aware Module (SAM) and the Semantic Coordination Module (SCM). SAM employs limited positional supervision to guide the model in focusing on the hip joint region and reducing background interference. SCM integrates information from radiological reports, utilizes prior knowledge from large language models to extract critical information related to ND-FNF, and guides the model to learn relevant visual representations. During inference, the model only requires the whole X-ray image for accurate diagnosis without additional information. The model was validated on datasets from four different centers, showing consistent accuracy and robustness. Codes and models are available at https://github.com/Yjing07/DDKG.

Deciphering the Genetic and Biochemical Drivers of Fruit Cracking in Akebia trifoliata.

This study investigates the molecular mechanisms underlying fruit cracking in Akebia trifoliata, a phenomenon that significantly impacts fruit quality and marketability. Through comprehensive physiological, biochemical, and transcriptomic analyses, we identified key changes in cell wall components and enzymatic activities during fruit ripening. Our results revealed that ventral suture tissues exhibit significantly elevated activities of polygalacturonase (PG) and β-galactosidase compared to dorsoventral line tissues, indicating their crucial roles in cell wall degradation and structural weakening. The cellulose content in VS tissues peaked early and declined during ripening, while DL tissues maintained relatively stable cellulose levels, highlighting the importance of cellulose dynamics in fruit cracking susceptibility. Transcriptomic analysis revealed differentially expressed genes (DEGs) associated with pectin biosynthesis and catabolism, cell wall organization, and oxidoreductase activities, indicating significant transcriptional regulation. Key genes like AKT032945 (pectinesterase) and AKT045678 (polygalacturonase) were identified as crucial for cell wall loosening and pericarp dehiscence. Additionally, expansin-related genes AKT017642, AKT017643, and AKT021517 were expressed during critical stages, promoting cell wall loosening. Genes involved in auxin-activated signaling and oxidoreductase activities, such as AKT022903 (auxin response factor) and AKT054321 (peroxidase), were also differentially expressed, suggesting roles in regulating cell wall rigidity. Moreover, weighted gene co-expression network analysis (WGCNA) identified key gene modules correlated with traits like pectin lyase activity and soluble pectin content, pinpointing potential targets for genetic manipulation. Our findings offer valuable insights into the molecular basis of fruit cracking in A. trifoliata, laying a foundation for breeding programs aimed at developing crack-resistant varieties to enhance fruit quality and commercial viability.

Trusted commonsense knowledge enhanced depression detection based on three-way decision

Depression detection on social media aims to identify depressive tendencies within textual posts, providing timely intervention by the early detection of mental health issues. In predominant approaches, the Pre-trained Language Models(PLMs) are trained solely on public datasets, falling short of vertical scenarios due to insufficient domain-specific and commonsense knowledge. In addition, ambiguous commonsense knowledge could be misleading to PLMs and results in false judgments. Therefore, it poses significant challenges to select commonsense knowledge that is trusted. To address this, we propose CoKE, a model that incorporates trusted commonsense knowledge based on three-way decision theory to enhance depression detection. CoKE comprises three key modules: trusted screening, knowledge generation, and knowledge fusion. First, we utilize psychiatric clinical scales and three-way decision theory to screen out the uncertain domain from the massive user posts. Then, an adaptive framework is applied to generate and refine trusted commonsense knowledge that can explain the true semantics of posts in the uncertain domain. Finally, a dynamic integration of posts with highly trusted knowledge is achieved through a gating mechanism, resulting in embeddings enhanced by trusted commonsense knowledge that are more effective in determining depressive tendencies. We evaluate our model on two prominent datasets, eRisk2017 and eRisk2018, demonstrating its superiority over previous state-of-the-art baseline models.

Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition.

The integration of communication and sensing (ICAS) in optical networks is an inevitable trend in building intelligent, multi-scenario, application-converged communication systems. However, due to the impact of nonlinear effects, co-fiber transmission of sensing signals and communication signals can cause interference to the communication signals, leading to an increased bit error rate (BER). This paper proposes a noncoherent solution based on the alternate polarization chirped return-to-zero frequency shift keying (Apol-CRZ-FSK) modulation format to realize a 4 × 100 Gbps dense wavelength division multiplexing (DWDM) optical network. Simulation results show that compared to traditional modulation formats, such as chirped return-to-zero frequency shift keying (CRZ-FSK) and differential quadrature phase shift keying (DQPSK), this solution demonstrates superior resistance to nonlinear effects, enabling longer transmission distances and better transmission performance. Moreover, to meet the transmission requirements and signal sensing and recognition needs in future optical networks, this study employs the Inception-ResNet-v2 convolutional neural network model to identify three modulation formats. Compared with six deep learning methods including AlexNet, ResNet50, GoogleNet, SqueezeNet, Inception-v4, and Xception, it achieves the highest performance. This research provides a low-cost, low-complexity, and high-performance solution for signal transmission and signal recognition in high-speed optical networks designed for integrated communication and sensing.

Identification of diagnostic genes and the miRNA‒mRNA‒TF regulatory network in human oocyte aging via machine learning methods.

Oocyte aging is a significant factor in the negative reproductive outcomes of older women. However, the pathogenesis of oocyte aging remains unclear. This study aimed to identify the hub genes involved in oocyte aging via bioinformatics methods. The oocyte aging datasets GSE155179 and GSE158802 were obtained from the GEO database and analyzed as the training set. The GSE164371 dataset was then defined as the validation set. Differentially expressed genes were analyzed via the limma package and weighted gene coexpression network analysis, and intersected with cellular senescence-associated genes from the Cell Senescence database. The hub genes were identified via three machine learning algorithms, namely, support vector machine recursive feature elimination, random forest, and least absolute shrinkage and selection operator logistic, which were also confirmed via the validation set. Finally, a microRNA-mRNA‒transcription factor regulatory network and single-gene gene set enrichment analysis were performed to clarify the pathogenesis of oocyte aging. A competing endogenous RNA network of GSE155179 and GSE158802 with 124 mRNAs, 31 long noncoding RNAs, and 31 miRNAs was constructed. Two modules with 814 genes were considered the key modules of oocyte aging. PDIK1L, SIRT1, and MCU were subsequently identified as hub genes; on the basis of these hub genes, a regulatory network of oocyte aging with 8 miRNAs, 3 mRNAs, and 227 TFs was ultimately constructed. This study contributes to a deeper understanding of oocyte aging and may aid in the development of therapeutic approaches to improve reproductive outcomes in older women.

Pilot Contamination Attack Detection Methods—An Exhaustive Performance Evaluation Through Probability Metrics and Statistical Classification Parameters

Among the numerous strategies that an attacker can initiate to enhance its eavesdropping capabilities is the Pilot Contamination Attack (PCA). Two promising methods, based on Phase-Shift Keying (PSK) modulation of Nth order—2-N-PSK and Shifted 2-N-PSK, can detect an existing PCA by means of analysis of the constellation that the correlation product of received pilot signals belongs to. The overall efficiency of the methods can be studied by the most commonly used probability metrics—detection probability and false alarm probability. However, this information may be insufficient for comparison purposes; therefore, to acquire a more holistic perspective on the methods’ performances, statistical evaluation metrics can be obtained. Depending on the particular application of the system in which the PCA detection methods are incorporated and the distribution of attack initiation among all samples, different classification parameters are of varying significance in the efficiency assessment. In this paper, 2-N-PSK and Shifted 2-N-PSK are comprehensively studied through their probability parameters. In addition, the methods are also compared by their most informative statistical parameters, such as accuracy, precision and recall, F1-score, specificity, and fall-out. A large number of simulations are carried out, the analyses of which indisputably prove the superior behavior of the Shifted 2-N-PSK compared to the 2-N-PSK detection method. Since a method’s performance is strongly related to the number of antenna elements at the base station, all simulations are conducted for scenarios with different antennae numbers. The most promising realization of Shifted 2-N-PSK improves the receiver operating characteristics results of the original 2-N-PSK by 7.38%, 4.33%, and 5.61%, and outperforms the precision recall analyses of 2-N-PSK by 10.02%, 4.82% and 3.86%, for the respective number of 10, 100 and 300 antenna elements at the base station.

The impact of hsa-miR-1972 on the expression of von Willebrand factor in breast cancer progression regulation.

Breast cancer (BC) is one of most frequent female malignancies that poses multiple challenges in treatment and prevention. This study aimed to explore the role of miRNAs and their target genes during the BC progression. Based on the BC data (113 normal and 1,118 tumor samples) from the TCGA-BRCA dataset, a single-sample gene set enrichment analysis (ssGSEA) was applied to calculate the cancer migration scores, and weighted gene co-expression network analysis (WGCNA) were performed using the WGCNA R package, with a focus on the set of genes associated with cancer migration. Key modules and hub genes related to cell migration and signaling pathways were identified. Survival analysis of hub genes was conducted using the survminer R package, and prediction of regulatory miRNAs were performed to analyze their impact on BC prognosis. In addition, the BC cell lines MCF-7 and MDA-MB-231 were used to further explore the effect of hsa-miR-1972 mimics on the gene expression and angiogenic factor regulation. The study classified important modules (MEblue, MEmagenta, MEpink, and MEfloralwhite) associated with cell migration and identified three hub genes, namely, MRPL20, COL4A1 and VWF. Survival analysis showed that certain hub genes with a low expression were related to a poor prognosis, whereas low-expressed COL4A1 and VWF were related to better survival outcomes. We also found that hsa-miR-1972 mimics significantly downregulated critical genes involved in BC metastasis and angiogenesis and effectively inhibited the proliferation of BC cell lines, showing a strong therapeutic potential. Manipulation of VWF expression in cells overexpressing hsa-miR-1972 had significant effects on the malignant markers and angiogenic factors, suggesting a novel therapeutic direction for BC treatment. Our study highlighted the complex interplay of genetic factors in BC progression as well as the therapeutic potential of targeting specific miRNAs and their related hub genes. These findings provided novel insights into the pathogenesis of BC and suggested new direction for the therapeutic development for the cancer.