Public Data Research Articles

Diversity of Freshwater Turtles Melanochelys trijuga in and Around Sankarapuram Wetland Complex on Kallakurichi District, Tamil Nadu, India

India is one of the most species-rich regions of freshwater turtles in the world, also, they are being collected, for slaughter, eaten, and traded in huge numbers. The scope of this study on Melanochelys trijuga, also known as the Indian Black Turtle, focuses on collecting comprehensive information about the species in India, specifically in Tamil Nadu, with a special emphasis on Sankarapuram.The study aims to address the lack of current data about this turtle species and contribute to the understanding of its taxonomy, distribution, habitat, behavior, and conservation status. Various methods are employed in this study such as visual sighting of habitat to encounter the target animal, and, indirectly through interview-based survey information from local communities. Visual survey was conducted on 20 locations of the sankarapuram study area. On the surveyed 20 locations the tortoise was observed in five locations viz. two Coconut farm, two ordinary well which used for irrigation purpose and a lake side muddy shallow area. Totally 10 individuals were observed in this study period. We had an opportunity to examine the specimens very closely once it comes out behind the open well. The captured specimens were later examined by comparison with the standard key characteristics and manuals in order to identify the specimen and found as Melanochelys trijuga species. In this study we observed both male and female individuals. The individuals are found as adults. The characteristics and ecology information were compared with the existing data in previous record and publications and manuals. Obtained Melanochelys trijuga species was found as Nearly Threatened status in the International Union for Conservation of Nature (IUCN) category.

The ultraviolet continuum slopes of high-redshift galaxies: evidence for the emergence of dust-free stellar populations at z > 10

ABSTRACT We present an analysis of the ultraviolet (UV) continuum slopes (β) for a sample of 172 galaxy candidates at 8 < zphot < 16 selected from a combination of JWST NIRCam imaging and COSMOS/UltraVISTA ground-based near-infrared imaging. Focusing primarily on a new sample of 121 galaxies at 〈z〉 ≃ 11 selected from ≃ 320 arcmin2 of public JWST imaging data across 15 independent data sets, we investigate the evolution of β in the galaxy population at z ≥ 9. We find a significant trend between β and redshift, with the inverse-variance weighted mean UV slope evolving from 〈β〉 = −2.17 ± 0.06 at z = 9.5 to 〈β〉 = −2.59 ± 0.06 at z = 11.5. Based on a comparison with stellar population models including nebular continuum emission, we find that at z > 10.5 the average UV continuum slope is consistent with the intrinsic blue limit of dust-free stellar populations (βint ≃ −2.6). These results suggest that the moderately dust-reddened galaxy population at z < 10 was essentially unattenuated at z ≃ 11. The extremely blue galaxies being uncovered at z > 10 place important constraints on dust attenuation in galaxies in the early Universe, and imply that the already observed galaxy population is likely supplying an ionizing photon budget capable of maintaining ionized intergalactic medium fractions of ≳ 5 per cent at z ≃ 11.

Constructing a novel prognostic model for triple-negative breast cancer based on genes associated with vasculogenic mimicry.

Research has shown a connection between vasculogenic mimicry (VM) and cancer progression. However, the functions of genes related to VM in the emergence and progression of TNBC have not been completely elucidated. A survival risk model was constructed by screening biomarkers using DESeq2 and WGCNA based on public TNBC transcriptome data. Furthermore, gene set enrichment analysis was performed, and tumor microenvironment and drug sensitivity were analyzed. The selected biomarkers were validated via quantitative PCR detection, immunohistochemical staining, and protein detection in breast cancer cell lines. Biomarkers related to the proliferation and migration of TNBC cells were validated via in vitro experiments. The findings revealed that 235 target genes were connected to the complement and coagulation cascade pathways. The risk score was constructed using KCND2, NRP1, and VSTM4. The prognosis model using the risk score and pathological T stage yielded good validation results. The clinical risk of TNBC was associated with the angiogenesis signaling pathway, and the low-risk group exhibited better sensitivity to immunotherapy. Quantitative PCR and immunohistochemistry indicated that the expression levels of KCND2 in TNBC tissues were higher than those in adjacent nontumor tissues. In the TNBC cell line, the protein expression of KCND2 was increased. Knockdown of KCND2 and VSTM4 inhibited the proliferation and migration of TNBC cells in vitro. In this study, three VM-related biomarkers were identified, including KCND2, NRP1, and VSTM4. These findings are likely to aid in deepening our understanding of the regulatory mechanism of VM in TNBC.

What to Compare When Comparing Inequalities in Educational Systems? Beyond School Disparities, Social Reproduction

Objective: International comparisons influence educational policies, but their foundations are seldom examined. The academic dimension is emphasized, despite the goal of integrating youth into the social and economic life of most countries. This research supports this perspective and aims to assess the role of educational systems in the reproduction of social inequalities in Latin America. Theoretical Framework: The research is grounded in the theories of social reproduction and conflict, addressing concepts such as school segregation and the valuation of diplomas in social mobility. Method: A qualitative and comparative methodology was adopted, focusing on the educational systems of Latin America. It was structured into four components: literature review, comparative analysis, analysis of public data, and synthesis of findings. Data collection involved the analysis of secondary data. Results and Discussion: The results revealed persistent educational inequalities in the region, such as school segregation and differences in diploma valuation. The discussion highlighted how schools and diplomas perpetuate social inequalities, influencing the social and economic trajectories of young people. Research Implications: It emphasizes the need to evaluate educational systems beyond academic performance, considering their impact on social integration. It underscores the urgency of reforms for inclusion, equity, and intersectoral collaboration. Originality/Value: This study offers a new perspective on educational inequality in Latin America, examining the impact of school segregation and diploma valuation on social mobility. It enriches academic dialogue and educational practices.

Blockade of histamine receptor H1 augments immune checkpoint therapy by enhancing MHC-I expression in pancreatic cancer cells

BackgroundAlthough immune checkpoint blockade (ICB) therapy has proven to be extremely effective at managing certain cancers, its efficacy in treating pancreatic ductal adenocarcinoma (PDAC) has been limited. Therefore, enhancing the effect of ICB could improve the prognosis of PDAC. In this study, we focused on the histamine receptor H1 (HRH1) and investigated its impact on ICB therapy for PDAC.MethodsWe assessed HRH1 expression in pancreatic cancer cell (PCC) specimens from PDAC patients through public data analysis and immunohistochemical (IHC) staining. The impact of HRH1 in PCCs was evaluated using HRH1 antagonists and small hairpin RNA (shRNA). Techniques including Western blot, flow cytometry, quantitative reverse transcription polymerase chain reaction (RT-PCR), and microarray analyses were performed to identify the relationships between HRH1 and major histocompatibility complex class I (MHC-I) expression in cancer cells. We combined HRH1 antagonism or knockdown with anti-programmed death receptor 1 (αPD-1) therapy in orthotopic models, employing IHC, immunofluorescence, and hematoxylin and eosin staining for assessment.ResultsHRH1 expression in cancer cells was negatively correlated with HLA-ABC expression, CD8+ T cells, and cytotoxic CD8+ T cells. Our findings indicate that HRH1 blockade upregulates MHC-I expression in PCCs via cholesterol biosynthesis signaling. In the orthotopic model, the combined inhibition of HRH1 and αPD-1 blockade enhanced cytotoxic CD8+ T cell penetration and efficacy, overcoming resistance to ICB therapy.ConclusionsHRH1 plays an immunosuppressive role in cancer cells. Consequently, HRH1 intervention may be a promising method to amplify the responsiveness of PDAC to immunotherapy.

AI-Driven Solutions for a Low-Carbon Transition: Evaluating Effectiveness and Limitations in Climate Change Mitigation

Climate change, primarily caused by human activities, poses a significant global challenge. Countries worldwide are integrating efforts to combat climate change through initiatives such as the Paris Agreement and setting targets to reach net-zero emissions by 2050. This paper explores the potential of artificial intelligence (AI) as a promising solution to address climate change, particularly through the analysis of mass data. AI can aid in environmental decision-making processes, optimize renewable energy use, and accelerate the global transition to a low-carbon economy. Using public data from the OECD, the study investigates the effectiveness of AI in promoting a low-carbon economy by examining its impact on greenhouse gas emissions, carbon footprint, investment in research and development, renewable energy production, and recycling rates. The findings suggest that AI has been considerably effective in supporting the growth of renewable energy and recycling while restraining gas emissions and carbon footprint. However, the study also identifies potential limitations, such as the carbon release from AI itself, and suggests further improvements to AI models.

Clinically defining the opioid-exposed birthing person and infant as a dyad to support bedside care, surveillance, and research.

An increased incidence of maternal opioid use disorder (OUD) and neonatal abstinence syndrome (NAS) has prompted recommendations supporting a dyadic approach to care for birthing persons and their infants. However, there are no consensus guidelines outlining how the dyad is clinically defined. To examine how the opioid-exposed birthing person-infant dyad has been defined for purposes of data collection and research, a literature review applying the RAND/UCLA Appropriateness Method was conducted. The search yielded 320 abstracts, with 110 articles identified as having a dyadic focus. While no articles included a specific definition for the dyad, 33 (30%) contained a descriptive reference to the birthing person-infant dyad. Thematic analysis revealed eight recurring elements characteristic of the dyad: (1) engagement, (2) communication, (3) bonding, (4) attachment, (5) mutual responsiveness, (6) reciprocity, (7) synchrony, and (8) attunement. Integrating these elements revealed the interactional relationship between the opioid-exposed birthing person and infant as the foundational principle that defines the dyad. This definition shifts the focus of the opioid-exposed dyad from two individual patient populations to an interactional relationship that has broad applicability for clinical use, public health data collection, and research considerations.

Analysis of Peatland Research Trends Based on BERTopic

Peatlands comprise approximately 3% of the land area worldwide. Peatland exists in most countries, including tropical, subtropical, and boreal regions. Accordingly, peatland has garnered increased research attention as a potential countermeasure against climate change. Therefore, it is necessary to identify and comprise the topics constituting global peatland research. In this study, we applied BERTopic—a topic modeling technique—to analyze relatedness between research topics to classify global peatland research trends, evaluate changes over time, and analyze the relationships between topics. To this end, we searched the keyword “peatland” on ScienceDirect—a global academic publication data platform—and collected the titles and abstracts from 10,158 publications from 1953 to 2022 for dynamic topic modeling and network analysis. Eighty-two peatland research topics were identified, which were combined into 15 main topics. Over time, an increasing trend was observed in topics related to production, management, and fire. In addition, upon analysis of the relationships between topics, three groups centered on fire, peatland value, and carbon were identified. We anticipate that the findings of this study can be expanded to analyze trends in research related to fires in peatlands, regional characteristics of peat soil, prediction of greenhouse gases emission and mitigation due to peatland fires, and prediction of future peatland research topics.

Cost Evaluation for Capacity Planning Based on Patients’ Pathways via Semi-Markov Reward Modelling

In the present paper, we develop a non-homogeneous semi-Markov reward model, deriving expressions for a healthcare system’s expected structure along with the expected costs generated by medical services and patients’ holding times in the states. We provide a novel definition and investigation for states’ availability, which is critical for capacity planning based on service demand in an environment of limited resources. The study is based on patients’ mobility through hospital care, where each patient spends an amount of time in every state of the hospital (emergency room, short-term acute care, hospitalization, surgery room, and intensive care unit). Multiple outcomes, such as discharge or death, can also be taken into account. We envisage a situation where any discharges are immediately replaced by a number of new admissions that carry on the pathways of the patients who exit. By assuming an expanding system, the new idea of states’ inflows is considered due to new patients who create pathways through hospital care, along with internal entrances. The theoretical results are illustrated numerically with simulated hospital data informed by aggregated public data of the Greek public health sector. The framework can be used for both strategic planning and cost evaluation purposes for hospital resources.

Unsupervised detecting anomalies in multivariate time series by Robust Convolutional LSTM Encoder–Decoder (RCLED)

Monitoring modern industrial systems generates a large amount of multivariate time series data. One of the critical tasks of monitoring these systems is anomaly detection. The auto-encoder has emerged as a promising solution for detecting anomalies in systems lacking explicit anomaly data in their historical records; however, its performance can be sensitive to noisy data. This work aims at developing a robust anomaly detection model based on the autoencoder for multivariate time series. The autoencoder architecture consists of convolution, long short-term memory, and self-attention layers for better extraction of complex features from multivariate time series. In addition, a novel training technique, inherited from the idea of robust principal component analysis, was developed to efficiently train the proposed model when the input data is affected by noise. To evaluate the model’s performance, we tested it on two data sets: synthetic, real-industrial, and three public data. We also compare the performance of our model to that of the other state-of-the-art models. The results show that the proposed model outperforms all the latest models, especially when the noise level is considerable.

Project Hephaistos – II. Dyson sphere candidates from Gaia DR3, 2MASS, and WISE

ABSTRACT The search for extraterrestrial intelligence is currently being pursued using multiple techniques and in different wavelength bands. Dyson spheres, megastructures that could be constructed by advanced civilizations to harness the radiation energy of their host stars, represent a potential technosignature, that in principle may be hiding in public data already collected as part of large astronomical surveys. In this study, we present a comprehensive search for partial Dyson spheres by analysing optical and infrared observations from Gaia, 2MASS, and WISE. We develop a pipeline that employs multiple filters to identify potential candidates and reject interlopers in a sample of five million objects, which incorporates a convolutional neural network to help identify confusion in WISE data. Finally, the pipeline identifies seven candidates deserving of further analysis. All of these objects are M-dwarfs, for which astrophysical phenomena cannot easily account for the observed infrared excess emission.

Use and Abuse of Social Media as a Punitive Remedy in Light of Criminal Law: A Tool or a Court? Analysis of the Chilean Regulation

Over the last few years, Chile’s judicial system has witnessed a rise in criminal assumptions generated through social networks, under its hypotheses of funas, doxing, flagging or, in general, the exposure of the personal data of an individual, whether motivated by the performance of conduct of criminal relevance or simply of dubious morality or social appropriateness. Although these conducts originated as a form of digital social empowerment, they have turned into a criminal matter on their own right, posing a series of issues regarding the correct application of the law and casting doubt on the appropriate legal mechanisms to handle these types of accusations, as cases involving these kinds of conducts have been resolved both on a criminal and constitutional level. Thus, the purpose of this article is to determine whether the current Chilean national regulation provides sufficient tools that, with a reasonable interpretation, allow it to correctly handle these sorts of cases. Moreover, this article aims to, through a comprehensive analysis of Germany’s current legislation, determine whether Chile requires a new all-encompassing regulatory approach to these hypotheses similar to the German solution, or if the current rules provide a proper solution to the problem of public personal data exposure on social media as a dangerous behaviour for the person whose data is being exposed.

Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects.

Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects-from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Sharing insect data through GBIF: novel monitoring methods, opportunities and standards.

Technological advancements in biological monitoring have facilitated the study of insect communities at unprecedented spatial scales. The progress allows more comprehensive coverage of the diversity within a given area while minimizing disturbance and reducing the need for extensive human labour. Compared with traditional methods, these novel technologies offer the opportunity to examine biological patterns that were previously beyond our reach. However, to address the pressing scientific inquiries of the future, data must be easily accessible, interoperable and reusable for the global research community. Biodiversity information standards and platforms provide the necessary infrastructure to standardize and share biodiversity data. This paper explores the possibilities and prerequisites of publishing insect data obtained through novel monitoring methods through GBIF, the most comprehensive global biodiversity data infrastructure. We describe the essential components of metadata standards and existing data standards for occurrence data on insects, including data extensions. By addressing the current opportunities, limitations, and future development of GBIF's publishing framework, we hope to encourage researchers to both share data and contribute to the further development of biodiversity data standards and publishing models. Wider commitments to open data initiatives will promote data interoperability and support cross-disciplinary scientific research and key policy indicators. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Loss of heterozygosity impacts MHC expression on the immune microenvironment in CDK12-mutated prostate cancer

BackgroundIn prostate cancer (PCa), well-established biomarkers such as MSI status, TMB high, and PDL1 expression serve as reliable indicators for favorable responses to immunotherapy. Recent studies have suggested a potential association between CDK12 mutations and immunotherapy response; however, the precise mechanisms through which CDK12 mutation may influence immune response remain unclear. A plausible explanation for immune evasion in this subset of CDK12-mutated PCa may be reduced MHC expression.ResultsUsing genomic data of CDK12-mutated PCa from 48 primary and 10 metastatic public domain samples and a retrospective cohort of 53 low-intermediate risk primary PCa, we investigated how variation in the expression of the MHC genes affected associated downstream pathways. We classified the patients based on gene expression quartiles of MHC-related genes and categorized the tumors into “High” and “Low” expression levels. CDK12-mutated tumors with higher MHC-expressed pathways were associated with the immune system and elevated PD-L1, IDO1, and TIM3 expression. Consistent with an inflamed tumor microenvironment (TME) phenotype, digital cytometric analyses identified increased CD8 + T cells, B cells, γδ T cells, and M1 Macrophages in this group. In contrast, CDK12-mutated tumors with lower MHC expression exhibited features consistent with an immune cold TME phenotype and immunoediting. Significantly, low MHC expression was also associated with chromosome 6 loss of heterozygosity (LOH) affecting the entire HLA gene cluster. These LOH events were observed in both major clonal and minor subclonal populations of tumor cells. In our retrospective study of 53 primary PCa cases from this Institute, we found a 4% (2/53) prevalence of CDK12 mutations, with the confirmation of this defect in one tumor through Sanger sequencing. In keeping with our analysis of public domain data this tumor exhibited low MHC expression at the RNA level. More extensive studies will be required to determine whether reduced HLA expression is generally associated with primary tumors or is a specific feature of CDK12 mutated PCa.ConclusionsThese data show that analysis of CDK12 alteration, in the context of MHC expression levels, and LOH status may offer improved predictive value for outcomes in this potentially actionable genomic subgroup of PCa. In addition, these findings highlight the need to explore novel therapeutic strategies to enhance MHC expression in CDK12-defective PCa to improve immunotherapy responses.

Inferring skin–brain–skin connections from infodemiology data using dynamic Bayesian networks

The relationship between skin diseases and mental illnesses has been extensively studied using cross-sectional epidemiological data. Typically, such data can only measure association (rather than causation) and include only a subset of the diseases we may be interested in. In this paper, we complement the evidence from such analyses by learning an overarching causal network model over twelve health conditions from the Google Search Trends Symptoms public data set. We learned the causal network model using a dynamic Bayesian network, which can represent both cyclic and acyclic causal relationships, is easy to interpret and accounts for the spatio-temporal trends in the data in a probabilistically rigorous way. The causal network confirms a large number of cyclic relationships between the selected health conditions and the interplay between skin and mental diseases. For acne, we observe a cyclic relationship with anxiety and attention deficit hyperactivity disorder (ADHD) and an indirect relationship with depression through sleep disorders. For dermatitis, we observe directed links to anxiety, depression and sleep disorders and a cyclic relationship with ADHD. We also observe a link between dermatitis and ADHD and a cyclic relationship between acne and ADHD. Furthermore, the network includes several direct connections between sleep disorders and other health conditions, highlighting the impact of the former on the overall health and well-being of the patient. The average R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} for a condition given the values of all conditions in the previous week is 0.67: in particular, 0.42 for acne, 0.85 for asthma, 0.58 for ADHD, 0.87 for burn, 0.76 for erectile dysfunction, 0.88 for scars, 0.57 for alcohol disorders, 0.57 for anxiety, 0.53 for depression, 0.74 for dermatitis, 0.60 for sleep disorders and 0.66 for obesity. Mapping disease interplay, indirect relationships, and the key role of mediators, such as sleep disorders, will allow healthcare professionals to address disease management holistically and more effectively. Even if we consider all skin and mental diseases jointly, each disease subnetwork is unique, allowing for more targeted interventions.

Increasing interest in data literacy: The quantitative public health data literacy training program

Due to the COVID-19 pandemic, the presentation of public health data to lay audiences has increased without most people having the knowledge to understand what these statistics mean. Recognizing that minoritized populations are deeply impacted by the pandemic and wanting to improve the racial representation in biostatistics we developed a training program aimed at increasing the data literacy of high school and college students from minoritized groups. The program introduced the basics of public health, data literacy, statistical software, descriptive statistics, and data ethics. The instructors taught eight synchronous sessions consisting of lectures and experiential group exercises. Five of the sessions were also offered asynchronously. Of the 209 students, 76% were college students; 90% identified as Black, Asian, or Latino/a/x; and the average age was 21 years. In synchronous sessions, 56% of students attended all sessions. All course sessions were rated as good/excellent by most (≥70%) students. The program recruited, engaged, and retained a large cohort (N > 100) of underrepresented students in biostatistics/data science for a virtual data literacy training. The program demonstrates the feasibility of developing and implementing public health training programs designed to increase racial and gender diversity in the field.

Exploring Climate Change Through Students’ Place Connections and Public Data Sets

Exploring Climate Change Through Students’ Place Connections and Public Data Sets

Abstract PO3-09-03: Dysbiosis of gut microbiota in patients with breast cancer

Abstract [Background] The effects of gut microbiome on various diseases have become widely known, and many studies reported that there was a correlation between the change of gut microbiota, that is dysbiosis, and the progression of digestive tract cancer. In addition, it is well-known that gut microbiota was correlated to the efficacy of immune checkpoint inhibitors for multiple types of cancer. However, the significance of gut microbiota in patients with breast cancer still remains unclear. The aim of this study is to analyze the gut microbiota in patients with breast cancer, compared with healthy women. [Subjects and Methods] We used the pre-treatment feces of 79 patients with primary breast cancer who received treatment at our facility and performed metagenome analysis of the V3-4 region of 16S rDNA. We also analyzed the breast cancer patients' microbiota with QIIME2 analysis method, compared to randomly selected 100 women aged 30-77 years from NIBIOHN's public data . We also assessed the characteristics of the gut microbiota of breast cancer patients by subtypes and its relationship with clinical and pathological factors. [Results] In Japanese gut microbiota, the Firmicutes and Bacteroidetes phyla make up the majority, but Firmicutes was the most common bacterial phylum in 52% of the public cohort, whereas 96.2% (76/79) of the breast cancer patients’ group (BC) showed a significantly higher proportion (p< 0.0001). In addition, ANCOM analysis revealed that the relative bacterial amounts of the Firmicutes, Bacteroidetes, and Proteobacteria phyla were significantly decreased in BC compared to the public cohort (PC), and at the bacterial genus level, the Feacalibacterium genus was significantly decreased in BC. Furthermore, the percentage of Fusobacteria phylum, which is a well-known oral bacteria that exacerbates digestive tract cancer, was 49.3% in BC, compared to 35% in PC (p=0.04). Among BC, the relative frequency of Fusobacteria phylum was significantly higher in the HER2-negative group than in the HER2-positive group (p< 0.0001). In BC, both alpha diversity (p< 0.0001) and beta diversity (p=0.001) were significantly lower than in PC. [Conclusion] An increase in the Firmicutes phylum is known to be associated with obesity, and also likely to affect the incidence risk of breast cancer. The results suggest that the Fusobacteria phylum and Feacalibacterium genus may be risk or preventive factors for breast cancer. Dysbiosis of gut microbiota is suggested to be associated with various diseases, and in this study the decrease in bacterial amount and diversity was observed in BC compared to PC, which may affect the onset and progression of breast cancer. Citation Format: Mai Yamada, Takafumi Morisaki, Yo Sato, Kimihisa Mizoguchi, Yuka Takao, Yurina Ochiai, Yoshiki Ootubo, Saori Hayashi, Masafumi Nakamura, Makoto Kubo. Dysbiosis of gut microbiota in patients with breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-09-03.

Abstract PO1-25-04: PTN-positive luminal progenitors induce angiogenesis and metastasis in inflammatory breast cancer

Abstract Background Inflammatory breast cancer (IBC) is a lethal subtype of breast cancer characterized by rapidly arising diffuse erythema and edema of the overlying skin that was thought to contribute to rapid metastasis. Dissecting the intricate cellular ecosystems within tumors and affected skin and their complex interplay may be pivotal in unraveling the aggressive nature of IBC. Methods Integrative analysis containing single-cell RNA sequencing, bulk RNA sequencing, and whole exome sequencing, was performed on paired tumor and skin samples from 17 treatment-naive IBC patients and 5 non-IBC patients from multiple centers. Meanwhile, paraffin sections and serum specimens from two centers were conducted to validate the omics results. Additionally, public single-cell transcriptomic data of non-IBC tumors and normal mammary glands, RNA microarray data from IBC cases, and the Cancer Genome Atlas (TCGA) database were utilized to validate the characteristics of IBC microenvironment. The regulatory mechanism was verified by in vitro experiments. Results We delineated the landscapes of IBC tumor and affected skin microenvironment at the single-cell level and demonstrated that the luminal progenitors (LP) cells highly expressing pleiotrophin (PTN) were most significantly enriched in IBC tumors compared to non-IBC tumors and normal mammary glands, in two independent cohorts. Moreover, the serum concentrations of PTN were significantly higher in IBC patients compared with non-IBC patients. Notably, PTN secreted by PTN+ LP cells interacted with Neuropilin 1(NRP1) receptor on endothelial tip cells in both tumor and affected skin of IBC. Moreover, in vitro assay demonstrated knocking down the expression of NRP1 on human umbilical vein endothelial cells significantly blocked PTN-induced migration, tube formation and sprouting, which might be independent of VEGFA. In addition, two immature phenotypes of PVL_MMP9 and PVL_CCL19 cells recruited by endothelial tip cells via PDGFB/PDGFRB axis were accumulated in affected skin of IBC, which might be associated with promotion of malignant cell metastasis. Finally, we revealed that malignant cells in affected skin of IBC had a higher ability of epithelial-mesenchymal transition than IBC intratumoral malignant cells and identified enhanced crosstalk between immature PVL cells and malignant cells in the skin affected with IBC , through TNFSF12-TNFRSF12A, TGFB-TGFBR1/TGFBR2, MDK-SDC1/4/NCL/(ITGA6+ITGB1). Conclusion We identified a unique, non-malignant epithelial cell subpopulation PTN+ LP cells, which induce massive angiogenesis via the PTN-NRP1 axis, recruit the immature phenotype PVL cells accumulated in affected skin of IBC thus promoting tumor progression. Our findings offer comprehensive insights into the pathogenesis of IBC and uncover a previously unknown, promising therapeutic target. Patient of discovery cohort details Clinical and pathology details for inflammatory breast cancer(IBC) and non-IBC patients analysed by single-cell RNA sequencing(scRNA-seq), bulk RNA sequencing(bulk RNA-seq) and whole exome sequencing(WES) in this study. Clinicalpathological feature of validation cohort Citation Format: Mengmeng Zhang, Kaiwen Zhou, Ting Liu, Filipa Lynce, Filipa Lynce, Wendy Chen, Qianjun Chen, Xiaoping Li, Zhongyu Yuan, Juan Xu, Zhenhai Cai, Jianping Guo, Nan Shao, Ying Lin. PTN-positive luminal progenitors induce angiogenesis and metastasis in inflammatory breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-25-04.