The effect of justice sensitivity on malevolent creativity: the mediating role of anger and the moderating role of emotion regulation strategies.

BACKGROUND: the AMORAL model emphasizes the close connection of individuals' belief system and malevolent creativity. Belief in a just world theory (BJW) states that people have a basic need to believe that the world they live in is just, and everyone gets what they deserve. Therefore, justice matters to all people. Justice sensitivity, as one of individual trait, has been found associated with negative goals. However, relevant studies have not tested whether justice sensitivity can affect malevolent creativity and its psychological mechanisms. Additionally, researchers have found that both anger and emotion regulation linked with justice sensitivity and malevolent creativity, but their contribution to the relationship between justice sensitivity and malevolent creativity remained unclear. The current study aims to explore the influence of justice sensitivity on malevolent creativity, the mediating effect of trait anger/state anger on the relationship between justice sensitivity and malevolent creativity, and the moderating effect of emotion regulation on this mediating effect. METHODS: A moderated mediating model was constructed to test the relationship between justice sensitivity and malevolent creativity. A sample of 395 Chinese college students were enrolled to complete the questionnaire survey. RESULTS: Justice sensitivity positively correlated with malevolent creativity, both trait anger and state anger partly mediated the connection between justice sensitivity and malevolent creativity. Moreover, emotion regulation moderated the indirect effect of the mediation model. The indirect effect of justice sensitivity on malevolent creativity through trait anger/state anger increased as the level of emotion regulation increased. The results indicated that justice sensitivity can affect malevolent creativity directly and indirectly through the anger. The level of emotion regulation differentiated the indirect paths of justice sensitivity on malevolent creativity. CONCLUSIONS: Justice sensitivity and malevolent creativity was mediated by trait anger/state anger. The higher sensitivity to justice, the higher level of trait anger/state anger, which in turn boosted the tendency of malevolent creativity. This indirect connection was moderated by emotion regulation, individuals with high emotion regulation are better able to buffer anger from justice sensitivity.

Single-cell spatial transcriptomics reveals a dystrophic trajectory following a developmental bifurcation of myoblast cell fates in facioscapulohumeral muscular dystrophy.

Facioscapulohumeral muscular dystrophy (FSHD) is linked to abnormal de-repression of the transcription activator DUX4. This effect is localized to a low percentage of cells, requiring single-cell analysis. However, single-cell/nucleus RNA-seq cannot fully capture the transcriptome of multinucleated large myotubes. To circumvent these issues, we use MERFISH (Multiplexed Error Robust Fluorescent In Situ Hybridization) spatial transcriptomics that allows profiling of RNA transcripts at a subcellular resolution. We simultaneously examined spatial distributions of 140 genes, including 24 direct DUX4 targets, in in vitro differentiated control, isogenic D4Z4 contraction mutant and FSHD patient myotubes and unfused mononuclear cells (MNCs), as well as the individual nuclei within them. We find myocyte nuclei segregate into 2 clusters defined by expression of DUX4 target genes, which is exclusively found in patient/mutant nuclei, while MNCs cluster based on developmental state. Patient/mutant myotubes are found in "FSHD-hi" and "FSHD-lo" states with the former signified by high DUX4 target expression and decreased muscle gene expression. Pseudotime analyses reveal a clear bifurcation of myoblast differentiation into control and FSHD-hi myotube branches, with variable numbers of DUX4 target expressing nuclei found in multinucleated FSHD-hi myotubes. Gene coexpression modules related to extracellular matrix and stress gene ontologies are significantly altered in patient/mutant myotubes compared to control. We also identify distinct subpathways within the DUX4 gene network that may differentially contribute to the disease transcriptomic phenotype. Taken together, our MERFISH-based study provides effective gene network profiling of multinucleated cells and identifies FSHD-induced transcriptomic alterations during myoblast differentiation.

New rabies viral resources for multi-scale neural circuit mapping.

Comparisons and linkage between multiple imaging scales are essential for neural circuit connectomics. Here, we report 20 new recombinant rabies virus (RV) vectors that we have developed for multi-scale and multi-modal neural circuit mapping tools. Our new RV tools for mesoscale imaging express a range of improved fluorescent proteins. Further refinements target specific neuronal subcellular locations of interest. We demonstrate the discovery power of these new tools including the detection of detailed microstructural changes of rabies-labeled neurons in aging and Alzheimer's disease mouse models, live imaging of neuronal activities using calcium indicators, and automated measurement of infected neurons. RVs that encode GFP and ferritin as electron microscopy (EM) and fluorescence microscopy reporters are used for dual EM and mesoscale imaging. These new viral variants significantly expand the scale and power of rabies virus-mediated neural labeling and circuit mapping across multiple imaging scales in health and disease.

Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer.

PURPOSE: A combination of fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard for adjuvant therapy of resected early-stage colon cancer (CC). Oxaliplatin leads to lasting and disabling neurotoxicity. Reserving the regimen for patients who benefit from oxaliplatin would maximize efficacy and minimize unnecessary adverse side effects. METHODS: We trained a new machine learning model, referred to as the colon oxaliplatin signature (COLOXIS) model, for predicting response to oxaliplatin-containing regimens. We examined whether COLOXIS was predictive of oxaliplatin benefits in the CC adjuvant setting among 1,065 patients treated with 5-fluorouracil plus leucovorin (FULV; n = 421) or FULV + oxaliplatin (FOLFOX; n = 644) from NSABP C-07 and C-08 phase III trials. The COLOXIS model dichotomizes patients into COLOXIS+ (oxaliplatin responder) and COLOXIS- (nonresponder) groups. Eight-year recurrence-free survival was used to evaluate oxaliplatin benefits within each of the groups, and the predictive value of the COLOXIS model was assessed using the P value associated with the interaction term (int P) between the model prediction and the treatment effect. RESULTS: Among 1,065 patients, 526 were predicted as COLOXIS+ and 539 as COLOXIS-. The COLOXIS+ prediction was associated with prognosis for FULV-treated patients (hazard ratio [HR], 1.52 [95% CI, 1.07 to 2.15]; P = .017). The model was predictive of oxaliplatin benefits: COLOXIS+ patients benefited from oxaliplatin (HR, 0.65 [95% CI, 0.48 to 0.89]; P = .0065; int P = .03), but COLOXIS- patients did not (COLOXIS- HR, 1.08 [95% CI, 0.77 to 1.52]; P = .65). CONCLUSION: The COLOXIS model is predictive of oxaliplatin benefits in the CC adjuvant setting. The results provide evidence supporting a change in CC adjuvant therapy: reserve oxaliplatin only for COLOXIS+ patients, but further investigation is warranted.

Comprehensive analysis of ALG3 in pan-cancer and validation of ALG3 as an onco-immunological biomarker in breast cancer.

ALG3 has significant modulatory function in the process of tumor development. Yet how ALG3 involves in the advancement of different malignancies isn't fully understood. We performed a pan-cancer assessment on ALG3 utilizing datasets from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) to examine its tumor-related roles across malignancies and its link to particular molecules and cells in the tumor microenvironment (TME). Furthermore, we focused on breast cancer to examine the influence of ALG3-mediated signaling pathways and intercellular interactions in the advancement of tumors. The biological effects of ALG3 were verified by breast cancer cells. Enhanced ALG3 expression was discovered to be substantially linked to patients' grim prognoses in a number of malignancies. Furthermore, the expression of ALG3 in the TME was linked to the infiltration of stromal and immune cells, and ALG3-related immune checkpoints, TMB, and MSI were also discovered. We also discovered that cancer patients having a high level of ALG3 exhibited a lower probability of benefiting from immunotherapy. Furthermore, our research found that KEGG enrichment, single-cell RNA and spatial sequencing analyses were effective in identifying key signaling pathways in ALG3-associated tumor growth. In vitro, knockdown of ALG3 could decrease the proliferation of breast cancer cells. In summary, our research offers a comprehensive insight into the advancement of tumors under the mediation of ALG3. ALG3 appears to be intimately associated with tumor development in the TME. ALG3 might be a viable treatment target for cancer therapy, particularly in the case of breast cancer.

Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future.

Interactions between tumor cells and immune cells in the tumor microenvironment (TME) play a vital role the mechanisms of immune evasion, by which cancer cells escape immune elimination. Thus, the characterization and quantification of different components in the TME is a hot topic in molecular biology and drug discovery. Since the development of transcriptome sequencing in bulk tissue, single cells and spatial dimensions, there are increasing methods emerging to deconvolute and subtype the TME. This review discusses and compares such computational strategies and downstream subtyping analyses. Integrative analyses of the transcriptome with other data, such as epigenetics and T-cell receptor sequencing, are needed to obtain comprehensive knowledge of the dynamic TME.

scGEM: Unveiling the Nested Tree-Structured Gene Co-Expressing Modules in Single Cell Transcriptome Data.

BACKGROUND: Single-cell transcriptome analysis has fundamentally changed biological research by allowing higher-resolution computational analysis of individual cells and subsets of cell types. However, few methods have met the need to recognize and quantify the underlying cellular programs that determine the specialization and differentiation of the cell types. METHODS: In this study, we present scGEM, a nested tree-structured nonparametric Bayesian model, to reveal the gene co-expression modules (GEMs) reflecting transcriptome processes in single cells. RESULTS: We show that scGEM can discover shared and specialized transcriptome signals across different cell types using peripheral blood mononuclear single cells and early brain development single cells. scGEM outperformed other methods in perplexity and topic coherence (p < 0.001) on our simulation data. Larger datasets, deeper trees and pre-trained models are shown to be positively associated with better scGEM performance. The GEMs obtained from triple-negative breast cancer single cells exhibited better correlations with lymphocyte infiltration (p = 0.009) and the cell cycle (p < 0.001) than other methods in additional validation on the bulk RNAseq dataset. CONCLUSIONS: Altogether, we demonstrate that scGEM can be used to model the hidden cellular functions of single cells, thereby unveiling the specialization and generalization of transcriptomic programs across different types of cells.

Machine learning in the prediction of post-stroke cognitive impairment: a systematic review and meta-analysis.

Objective: Cognitive impairment is a detrimental complication of stroke that compromises the quality of life of the patients and poses a huge burden on society. Due to the lack of effective early prediction tools in clinical practice, many researchers have introduced machine learning (ML) into the prediction of post-stroke cognitive impairment (PSCI). However, the mathematical models for ML are diverse, and their accuracy remains highly contentious. Therefore, this study aimed to examine the efficiency of ML in the prediction of PSCI. Methods: Relevant articles were retrieved from Cochrane, Embase, PubMed, and Web of Science from the inception of each database to 5 December 2022. Study quality was evaluated by PROBAST, and c-index, sensitivity, specificity, and overall accuracy of the prediction models were meta-analyzed. Results: A total of 21 articles involving 7,822 stroke patients (2,876 with PSCI) were included. The main modeling variables comprised age, gender, education level, stroke history, stroke severity, lesion volume, lesion site, stroke subtype, white matter hyperintensity (WMH), and vascular risk factors. The prediction models used were prediction nomograms constructed based on logistic regression. The pooled c-index, sensitivity, and specificity were 0.82 (95% CI 0.77-0.87), 0.77 (95% CI 0.72-0.80), and 0.80 (95% CI 0.71-0.86) in the training set, and 0.82 (95% CI 0.77-0.87), 0.82 (95% CI 0.70-0.90), and 0.80 (95% CI 0.68-0.82) in the validation set, respectively. Conclusion: ML is a potential tool for predicting PSCI and may be used to develop simple clinical scoring scales for subsequent clinical use. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=383476.

Amphiregulin couples IL1RL1+ regulatory T cells and cancer-associated fibroblasts to impede antitumor immunity.

Regulatory T (Treg) cells and cancer-associated fibroblasts (CAFs) jointly promote tumor immune tolerance and tumorigenesis. The molecular apparatus that drives Treg cell and CAF coordination in the tumor microenvironment (TME) remains elusive. Interleukin 33 (IL-33) has been shown to enhance fibrosis and IL1RL1+ Treg cell accumulation during tumorigenesis and tissue repair. We demonstrated that IL1RL1 signaling in Treg cells greatly dampened the antitumor activity of both IL-33 and PD-1 blockade. Whole tumor single-cell RNA sequencing (scRNA-seq) analysis and blockade experiments revealed that the amphiregulin (AREG)-epidermal growth factor receptor (EGFR) axis mediated cross-talk between IL1RL1+ Treg cells and CAFs. We further demonstrated that the AREG/EGFR axis enables Treg cells to promote a profibrotic and immunosuppressive functional state of CAFs. Moreover, AREG mAbs and IL-33 concertedly inhibited tumor growth. Our study reveals a previously unidentified AREG/EGFR-mediated Treg/CAF coupling that controls the bifurcation of fibroblast functional states and is a critical barrier for cancer immunotherapy.

Revealing the Impact of Genomic Alterations on Cancer Cell Signaling with an Interpretable Deep Learning Model.

Cancer is a disease of aberrant cellular signaling resulting from somatic genomic alterations (SGAs). Heterogeneous SGA events in tumors lead to tumor-specific signaling system aberrations. We interpret the cancer signaling system as a causal graphical model, where SGAs affect signaling proteins, propagate their effects through signal transduction, and ultimately change gene expression. To represent such a system, we developed a deep learning model called redundant-input neural network (RINN) with a transparent redundant-input architecture. Our findings demonstrate that by utilizing SGAs as inputs, the RINN can encode their impact on the signaling system and predict gene expression accurately when measured as the area under ROC curves. Moreover, the RINN can discover the shared functional impact (similar embeddings) of SGAs that perturb a common signaling pathway (e.g., PI3K, Nrf2, and TGF). Furthermore, the RINN exhibits the ability to discover known relationships in cellular signaling systems.

An interpretable deep learning framework for genome-informed precision oncology.

Cancers result from aberrations in cellular signaling systems, typically resulting from driver somatic genome alterations (SGAs) in individual tumors. Precision oncology requires understanding the cellular state and selecting medications that induce vulnerability in cancer cells under such conditions. To this end, we developed a computational framework consisting of two components: 1) A representation-learning component, which learns a representation of the cellular signaling systems when perturbed by SGAs, using a biologically-motivated and interpretable deep learning model. 2) A drug-response-prediction component, which predicts the response to drugs by leveraging the information of the cellular state of the cancer cells derived by the first component. Our cell-state-oriented framework significantly enhances the accuracy of genome-informed prediction of drug responses in comparison to models that directly use SGAs as inputs. Importantly, our framework enables the prediction of response to chemotherapy agents based on SGAs, thus expanding genome-informed precision oncology beyond molecularly targeted drugs.

Anatomical organization of temporally correlated neural calcium activity in the hippocampal CA1 region.

Hippocampal CA1 neuronal ensembles generate sequential patterns of firing activity that contribute to episodic memory formation and spatial cognition. Here we used in vivo calcium imaging to record neural ensemble activities in mouse hippocampal CA1 and identified CA1 excitatory neuron sub-populations whose members are active across the same second-long period of time. We identified groups of hippocampal neurons sharing temporally correlated neural calcium activity during behavioral exploration and found that they also organized as clusters in anatomical space. Such clusters vary in membership and activity dynamics with respect to movement in different environments, but also appear during immobility in the dark suggesting an internal dynamic. The strong covariance between dynamics and anatomical location within the CA1 sub-region reveals a previously unrecognized form of topographic representation in hippocampus that may guide generation of hippocampal sequences across time and therefore organize the content of episodic memory.

Sauchinone inhibits breast cancer cell proliferation through regulating microRNA-148a-3p/HER-2 axis.

BACKGROUND: Sauchinone is extracted from the root of Saururus chinensis and exhibits potent antitumor effects in various human cancers. However, how sauchinone is involved in breast cancer has not been well studied. METHODS: Cells apoptosis, cell proliferation, and cycle distribution were evaluated. Xenograft tumor mouse model was constructed to investigate the roles of sauchinone. The relevant protein expression was detected by western blot. RESULTS: We found that sauchinone significantly reduced proliferation and survival, also induced apoptosis of MCF-7 and Bcap-37 cells in vitro. Sauchinone significantly increased miR-148a-3p expression, and human epidermal growth factor receptor (HER)-2 targeted on miR-148a-3p. Sauchinone exposure downregulated HER-2 expression whose overexpression partly eliminated the inhibitory effect of sauchinone. Further, sauchinone efficiently inhibited breast cancer progression through downregulating HER-2 expression in vivo. CONCLUSION: Our results indicate that sauchinone efficiently inhibits breast cancer progression through regulating miR-148a-3p/HER-2 axis, suggesting that sauchinone could be an effective anticancer agent for breast cancer.

Degenerate mapping of environmental location presages deficits in object-location encoding and memory in the 5xFAD mouse model for Alzheimer's disease.

A key challenge in developing diagnosis and treatments for Alzheimer's disease (AD) is to detect abnormal network activity at as early a stage as possible. To date, behavioral and neurophysiological investigations in AD model mice have yet to conduct a longitudinal assessment of cellular pathology, memory deficits, and neurophysiological correlates of neuronal activity. We therefore examined the temporal relationships between pathology, neuronal activities and spatial representation of environments, as well as object location memory deficits across multiple stages of development in the 5xFAD mice model and compared these results to those observed in wild-type mice. We performed longitudinal in vivo calcium imaging with miniscope on hippocampal CA1 neurons in behaving mice. We find that 5xFAD mice show amyloid plaque accumulation, depressed neuronal calcium activity during immobile states, and degenerate and unreliable hippocampal neuron spatial tuning to environmental location at early stages by 4 months of age while their object location memory (OLM) is comparable to WT mice. By 8 months of age, 5xFAD mice show deficits of OLM, which are accompanied by progressive degradation of spatial encoding and, eventually, impaired CA1 neural tuning to object-location pairings. Furthermore, depressed neuronal activity and unreliable spatial encoding at early stage are correlated with impaired performance in OLM at 8-month-old. Our results indicate the close connection between impaired hippocampal tuning to object-location and the presence of OLM deficits. The results also highlight that depressed baseline firing rates in hippocampal neurons during immobile states and unreliable spatial representation precede object memory deficits and predict memory deficits at older age, suggesting potential early opportunities for AD detecting.

Cognitively impaired aged Octodon degus recapitulate major neuropathological features of sporadic Alzheimer's disease.

The long-lived Chilean rodent (Octodon degus) has been reported to show spontaneous age-dependent neuropathology and cognitive impairments similar to those observed in human AD. However, the handful of published papers on degus of differing genetic backgrounds yield inconsistent findings about sporadic AD-like pathological features, with notably differing results between lab in-bred degus versus outbred degus. This motivates more extensive characterization of spontaneously occurring AD-like pathology and behavior in degus. In the present study, we show AD-like neuropathological markers in the form of amyloid deposits and tau abnormalities in a cognitively impaired subset of aged outbred degus. Compared to the aged degus that show normal burrowing behavior, the age-matched degus with burrowing behavior deficits correlatively exhibit detectable human AD-like Aß deposits and tau neuropathology, along with neuroinflammatory markers that include enhanced microglial activation and higher numbers of reactive astrocytes in the brain. This subset of cognitively impaired aged degus also exhibits cerebral amyloid angiopathy and tauopathy. We find robust neurodegenerative features in behaviorally deficient aged degus, including hippocampal neuronal loss, altered parvalbumin and perineuronal net staining in the cortex, and increased c-Fos neuronal activation in the cortex that is consistent with the neural circuit hyperactivity reported in human AD patients. By focusing on the subset of aged degus that show AD-like behavioral deficits and correlative neuropathology, our findings establish outbred degus as a natural model of sporadic AD and demonstrate the potential importance of wild-type outbred genetic backgrounds for AD pathogenesis.

An individualized causal framework for learning intercellular communication networks that define microenvironments of individual tumors.

Cells within a tumor microenvironment (TME) dynamically communicate and influence each other's cellular states through an intercellular communication network (ICN). In cancers, intercellular communications underlie immune evasion mechanisms of individual tumors. We developed an individualized causal analysis framework for discovering tumor specific ICNs. Using head and neck squamous cell carcinoma (HNSCC) tumors as a testbed, we first mined single-cell RNA-sequencing data to discover gene expression modules (GEMs) that reflect the states of transcriptomic processes within tumor and stromal single cells. By deconvoluting bulk transcriptomes of HNSCC tumors profiled by The Cancer Genome Atlas (TCGA), we estimated the activation states of these transcriptomic processes in individual tumors. Finally, we applied individualized causal network learning to discover an ICN within each tumor. Our results show that cellular states of cells in TMEs are coordinated through ICNs that enable multi-way communications among epithelial, fibroblast, endothelial, and immune cells. Further analyses of individual ICNs revealed structural patterns that were shared across subsets of tumors, leading to the discovery of 4 different subtypes of networks that underlie disparate TMEs of HNSCC. Patients with distinct TMEs exhibited significantly different clinical outcomes. Our results show that the capability of estimating individual ICNs reveals heterogeneity of ICNs and sheds light on the importance of intercellular communication in impacting disease development and progression.

FOXD1-dependent RalA-ANXA2-Src complex promotes CTC formation in breast cancer.

BACKGROUND: Early metastasis is a key factor contributing to poor breast cancer (BC) prognosis. Circulating tumor cells (CTCs) are regarded as the precursor cells of metastasis, which are ultimately responsible for the main cause of death in BC. However, to date molecular mechanisms underlying CTC formation in BC have been insufficiently defined. METHODS: RNA-seq was carried out in primary tissues from early-stage BC patients (with CTCs≥5 and CTCs = 0, respectively) and the validation study was conducted in untreated 80 BC patients. Multiple in vitro and in vivo models were used in functional studies. Luciferase reporter, ChIP-seq, CUT&Tag-seq, and GST-pulldown, etc. were utilized in mechanistic studies. CTCs were counted by the CanPatrol™ CTC classification system or LiquidBiospy™ microfluidic chips. ERK1/2 inhibitor SCH772984 was applied to in vivo treatment. RESULTS: Highly expressed FOXD1 of primary BC tissues was observed to be significantly associated with increased CTCs in BC patients, particularly in early BC patients. Overexpressing FOXD1 enhanced the migration capability of BC cells, CTC formation and BC metastasis, via facilitating epithelial-mesenchymal transition of tumor cells. Mechanistically, FOXD1 was discovered to induce RalA expression by directly bound to RalA promotor. Then, RalA formed a complex with ANXA2 and Src, promoting the interaction between ANXA2 and Src, thus increasing the phosphorylation (Tyr23) of ANXA2. Inhibiting RalA-GTP form attenuated the interaction between ANXA2 and Src. This cascade culminated in the activation of ERK1/2 signal that enhanced metastatic ability of BC cells. In addition, in vivo treatment with SCH772984, a specific inhibitor of ERK1/2, was used to dramatically inhibit the CTC formation and BC metastasis. CONCLUSION: Here, we report a FOXD1-dependent RalA-ANXA2-Src complex that promotes CTC formation via activating ERK1/2 signal in BC. FOXD1 may serve as a prognostic factor in evaluation of BC metastasis risks. This signaling cascade is druggable and effective for overcoming CTC formation from the early stages of BC.

A Novel Bayesian Framework Infers Driver Activation States and Reveals Pathway-Oriented Molecular Subtypes in Head and Neck Cancer.

Head and neck squamous cell cancer (HNSCC) is an aggressive cancer resulting from heterogeneous causes. To reveal the underlying drivers and signaling mechanisms of different HNSCC tumors, we developed a novel Bayesian framework to identify drivers of individual tumors and infer the states of driver proteins in cellular signaling system in HNSCC tumors. First, we systematically identify causal relationships between somatic genome alterations (SGAs) and differentially expressed genes (DEGs) for each TCGA HNSCC tumor using the tumor-specific causal inference (TCI) model. Then, we generalize the most statistically significant driver SGAs and their regulated DEGs in TCGA HNSCC cohort. Finally, we develop machine learning models that combine genomic and transcriptomic data to infer the protein functional activation states of driver SGAs in tumors, which enable us to represent a tumor in the space of cellular signaling systems. We discovered four mechanism-oriented subtypes of HNSCC, which show distinguished patterns of activation state of HNSCC driver proteins, and importantly, this subtyping is orthogonal to previously reported transcriptomic-based molecular subtyping of HNSCC. Further, our analysis revealed driver proteins that are likely involved in oncogenic processes induced by HPV infection, even though they are not perturbed by genomic alterations in HPV+ tumors.

A genomic and transcriptomic study toward breast cancer.

Background: Breast carcinoma is well recognized to be having the highest global occurrence rate among all cancers, being the leading cause of cancer mortality in females. The aim of this study was to elucidate breast cancer at the genomic and transcriptomic levels in different subtypes so that we can develop more personalized treatments and precision medicine to obtain better outcomes. Method: In this study, an expression profiling dataset downloaded from the Gene Expression Omnibus database, GSE45827, was re-analyzed to compare the expression profiles of breast cancer samples in the different subtypes. Using the GEO2R tool, different expression genes were identified. Using the STRING online tool, the protein-protein interaction networks were conducted. Using the Cytoscape software, we found modules, seed genes, and hub genes and performed pathway enrichment analysis. The Kaplan-Meier plotter was used to analyze the overall survival. MicroRNAs and transcription factors targeted different expression genes and were predicted by the Enrichr web server. Result: The analysis of these elements implied that the carcinogenesis and development of triple-negative breast cancer were the most important and complicated in breast carcinoma, occupying the most different expression genes, modules, seed genes, hub genes, and the most complex protein-protein interaction network and signal pathway. In addition, the luminal A subtype might occur in a completely different way from the other three subtypes as the pathways enriched in the luminal A subtype did not overlap with the others. We identified 16 hub genes that were related to good prognosis in triple-negative breast cancer. Moreover, SRSF1 was negatively correlated with overall survival in the Her2 subtype, while in the luminal A subtype, it showed the opposite relationship. Also, in the luminal B subtype, CCNB1 and KIF23 were associated with poor prognosis. Furthermore, new transcription factors and microRNAs were introduced to breast cancer which would shed light upon breast cancer in a new way and provide a novel therapeutic strategy. Conclusion: We preliminarily delved into the potentially comprehensive molecular mechanisms of breast cancer by creating a holistic view at the genomic and transcriptomic levels in different subtypes using computational tools. We also introduced new prognosis-related genes and novel therapeutic strategies and cast new light upon breast cancer.