Non-negative Matrix Factorization Analysis Research Articles

Deciphering the significance of anoikis in bladder cancer and systematic analysis of S100A7 as a potential therapeutic target

BackgroundBladder cancer is an epidemic and life-threating urologic carcinoma. Anoikis is a unusual type of programmed cell death which plays a vital role in tumor survival, invasion and metastasis. Nevertheless, the relationship between anoikis and bladder cancer has not been understood thoroughly.MethodsWe downloaded the transcriptome and clinical information of BLCA patients from TCGA and GEO databases. Then, we analyzed different expression of anoikis-related genes and established a prognostic model based on TCGA database by univariate Cox regression, lasso regression, and multivariate Cox regression. Then the Kaplan–Meier survival analysis and receiver operating characteristic (ROC) curves were performed. GEO database was used for external validation. BLCA patients in TCGA database were divided into two subgroups by non-negative matrix factorization (NMF) classification. Survival analysis, different gene expression, immune cell infiltration and drug sensitivity were calculated. Finally, we verified the function of S100A7 in two BLCA cell lines.ResultsWe developed a prognostic risk model based on three anoikis-related genes including TPM1, RAC3 and S100A7. The overall survival of BLCA patients in low-risk groups was significantly better than high-risk groups in training sets, test sets and external validation sets. Subsequently, the checkpoint and immune cell infiltration had significant difference between two groups. Then we identified two subtypes (CA and CB) through NMF analysis and found CA had better OS and PFS than CB. Besides, the accuracy of risk model was verified by ROC analysis. Finally, we identified that knocking down S100A7 gene expression restrained the proliferation and invasion of bladder cancer cells.ConclusionWe established and validated a bladder cancer prognostic model consisting of three genes, which can effectively evaluate the prognosis of bladder cancer patients. Additionally, through cellular experiments, we demonstrated the significant role of S100A7 in the metastasis and invasion of bladder cancer, suggesting its potential as a novel target for future treatments.

PATH-05. PRIMARY AND SECONDARY GLIOSARCOMA: RADIOLOGICAL AND PROGNOSTIC DIFFERENCES USING COMPREHENSIVE MULTI-OMICS ANALYSIS

Abstract Purpose Gliosarcoma (GS), a rare variant of glioblastoma (GB), is further classified into primary (de novo) gliosarcoma (PGS), and secondary gliosarcoma (SGS), which is defined as recurrence after a diagnosis of diffuse glioma. Poorer survival of SGS than PGS has been reported, however, the detailed mechanism remains unknown. METHODS: A retrospective retrieval of pathological confirmed GS was performed. All the clinical, radiological, and pathological comparisons were based on the diagnosis of GS. A multi-omics, including whole exome sequencing, transcriptome, proteome and phosphoproteome, approach was used to acquire the molecular profiles of these specimens. RESULTS: From 2003 to 2020, a total of 28 GSs were identified, including 19 PGSs and 9 SGSs. The age tended to be younger in SGS group than PGS group (52.1 ± 10.9 vs. 60.8 ± 11.5, p=0.069). SGSs tended to present with dural attachment (p=0.071) and were significantly associated with major vessel encasement (p=0.002) than PGSs. The Ki-67 index tended to be higher in SGSs than PGSs (p=0.081). The GS survival was significantly shorter in SGS group than PGS group (median: 7.9 vs. 32.4 months, p< 0.001). The multi-omics analysis demonstrated SGSs had significantly higher expression of negative regulation of megakaryocyte differentiation, telomere capping, and DNA replication nucleosome assembly pathways than PGSs. Next, using proteome / phosphoproteome nonnegative matrix factorization analysis, PGS was categorized into two clusters (PGS_C1 versus PGS_C2) with distinct multi-omics features. Of which, PGS_C1 (n=4) tended to have shorter survival than PGS_C2 (n=15, p=0.054). Notably, the survival of patients in PGS_C1, who had similar multi-omics signature to SGS, were as poor as those of SGSs. CONCLUSIONS: In summary, this study provides, for the first time, in depth evidence for understanding GS and presents multi-omics biomarkers with prognostic significance. Further therapeutic translation should based on current findings to benefit GS patients.

Population genetics of queen conch, Aliger gigas (Linnaeus, 1758) (Gastropoda: Strombidae) at the Cuban island platform

Abstract The queen conch, Aliger gigas is a large marine gastropod mollusc that inhabits shallow seagrass meadows and sandplain habitats throughout the Caribbean. Owing to overfishing and degradation of the environment, it has become an endangered species in most of its habitat. Research on Cuban populations of A. gigas is scarce and mostly focused on conch exploitation. The present study assesses, for the first time, the genetic diversity and population structure of A. gigas from six locations on the Cuban platform, using five microsatellite loci. At most of the sampled locations, the observed heterozygosity was lower than expected and the FIS value was significantly positive, both of which suggest inbreeding. Also, we found evidence of bottlenecks for two of the locations with a deficit of heterozygotes. Although statistically significant, only 1.66% of the total variance was explained by genetic differentiation among populations, and discriminant analysis of principal components showed different degrees of overlapping between all locations. Accordingly, the results showed that there is an extensive and symmetrical exchange of genetic information. Sparse non-negative matrix factorization analysis determined the existence of three ancestral populations admixed across the sampled locations. Thus, our results suggest that A. gigas from the Cuban island platform could be a unique population, which is relevant for management and conservation strategies.

Membrane tension-mediated stiff and soft tumor subtypes closely associated with prognosis for prostate cancer patients

BackgroundProstate cancer (PCa) is usually considered as cold tumor. Malignancy is associated with cell mechanic changes that contribute to extensive cell deformation required for metastatic dissemination. Thus, we established stiff and soft tumor subtypes for PCa patients from perspective of membrane tension.MethodsNonnegative matrix factorization algorithm was used to identify molecular subtypes. We completed analyses using software R 3.6.3 and its suitable packages.ResultsWe constructed stiff and soft tumor subtypes using eight membrane tension-related genes through lasso regression and nonnegative matrix factorization analyses. We found that patients in stiff subtype were more prone to biochemical recurrence than those in soft subtype (HR 16.18; p < 0.001), which was externally validated in other three cohorts. The top ten mutation genes between stiff and soft subtypes were DNAH, NYNRIN, PTCHD4, WNK1, ARFGEF1, HRAS, ARHGEF2, MYOM1, ITGB6 and CPS1. E2F targets, base excision repair and notch signaling pathway were highly enriched in stiff subtype. Stiff subtype had significantly higher TMB and T cells follicular helper levels than soft subtype, as well as CTLA4, CD276, CD47 and TNFRSF25.ConclusionsFrom the perspective of cell membrane tension, we found that stiff and soft tumor subtypes were closely associated with BCR-free survival for PCa patients, which might be important for the future research in the field of PCa.

Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis

BackgroundAtherosclerosis is the common pathological basis for many cardiovascular and cerebrovascular diseases. The purpose of this study is to identify the diagnostic biomarkers related to atherosclerosis through machine learning algorithm.MethodsClinicopathological parameters and transcriptomics data were obtained from 4 datasets (GSE21545, GSE20129, GSE43292, GSE100927). A nonnegative matrix factorization algorithm was used to classify arteriosclerosis patients in GSE21545 dataset. Then, we identified prognosis-related differentially expressed genes (DEGs) between the subtypes. Multiple machine learning methods to detect pivotal markers. Discrimination, calibration and clinical usefulness of the predicting model were assessed using area under curve, calibration plot and decision curve analysis respectively. The expression level of the feature genes was validated in GSE20129, GSE43292, GSE100927.Results2 molecular subtypes of atherosclerosis was identified, and 223 prognosis-related DEGs between the 2 subtypes were identified. These genes are not only related to epithelial cell proliferation, mitochondrial dysfunction, but also to immune related pathways. Least absolute shrinkage and selection operator, random forest, support vector machine- recursive feature elimination show that IL17C and ACOXL were identified as diagnostic markers of atherosclerosis. The prediction model displayed good discrimination and good calibration. Decision curve analysis showed that this model was clinically useful. Moreover, IL17C and ACOXL were verified in other 3 GEO datasets, and also have good predictive performance.ConclusionIL17C and ACOXL were diagnostic genes of atherosclerosis and associated with higher incidence of ischemic events.

Polar Nitrated Aromatic Compounds in Urban Fine Particulate Matter: A Focus on Formation via an Aqueous-Phase Radical Mechanism.

Polar nitrated aromatic compounds (pNACs) are key ambient brown carbon chromophores; however, their formation mechanisms, especially in the aqueous phase, remain unclear. We developed an advanced technique for pNACs and measured 1764 compounds in atmospheric fine particulate matter sampled in urban Beijing, China. Molecular formulas were derived for 433 compounds, of which 17 were confirmed using reference standards. Potential novel species with up to four aromatic rings and a maximum of five functional groups were found. Higher concentrations were detected in the heating season, with a median of 82.6 ng m-3 for Σ17pNACs. Non-negative matrix factorization analysis indicated that primary emissions particularly coal combustion were dominant in the heating season. While in the non-heating season, aqueous-phase nitration could generate abundant pNACs with the carboxyl group, which was confirmed by their significant association with the aerosol liquid water content. Aqueous-phase formation of 3- and 5-nitrosalicylic acids instead of their isomer of 4-hydroxy-3-nitrobenzoic acid suggests the existence of an intermediate where the intramolecular hydrogen bond favors kinetics-controlled NO2• nitration. This study provides not only a promising technique for the pNAC measurement but also evidence for their atmospheric aqueous-phase formation, facilitating further evaluation of pNACs' climatic effects.

An Angiogenesis-Related lncRNA Signature Is Associated with Prognosis and Tumor Immune Microenvironment in Breast Cancer.

Angiogenesis is crucial in the development and progression of tumors. This study examined the relationship between angiogenesis-related lncRNAs (AR-lncRNAs) and breast cancer (BC) immunity and prognosis. We used univariate Cox regression analysis to obtain AR-lncRNAs closely related to BC prognosis. Cluster analysis of BC patients was performed using non-negative matrix factorization (NMF) analysis according to the expression of AR-lncRNAs that were prognostically relevant. An AR-lncRNA risk model (AR-lncM) was created using LASSO regression analysis to predict the prognosis and survival of BC patients. Subsequently, the effect of LINC01614 on cell migration and invasion was verified by Transwell and Western blot assays, and the CCK-8 assay detected its impact on cell sensitivity to tamoxifen. Finally, we obtained 17 AR-lncRNAs from the TCGA database that were closely associated with the prognosis of BC patients. Based on the expression of these AR-lncRNAs, BC patients were divided into five clusters using NMF analysis. Cluster 1 was found to have a better prognosis, higher expression of immune checkpoints, and higher levels of immune cell infiltration. Furthermore, an AR-LncM model was created using ten prognostic-related AR-lncRNAs. The model's risk predictive performance was validated using survival analysis, timeROC curves, and univariate and multivariate Cox analysis. The most interesting gene in the model, LINC01614, was found to regulate epithelial-mesenchymal transition (EMT) and tamoxifen sensitivity in BC cells, implying that LINC01614 could be a potential therapeutic target for BC patients.

Unraveling the evolution of dissolved organic matter in full-scale A/A/O wastewater treatment process using size exclusion chromatography with PARAFAC and nonnegative matrix factorization analysis

Changes in spectral features and molecular weight (MW) of dissolved organic matter (DOM) along the A/A/O processes in eight full-scale wastewater treatment plants (WWTPs) were characterized using size exclusion chromatography with a diode array detector, a fluorescence detector and an organic carbon detector in tandem (SEC-DAD-FLD-OCD) as well as bulk water quality parameters. The parallel factor (PARAFAC) and the nonnegative matrix factorization (NMF) analyses have been effectively applied to the postprocessing of SEC-FLD fingerprints and SEC-OCD chromatograms, respectively. Individual SEC-FLD-PARAFAC or SEC-OCD-NMF components may span a broad range of MW, indicating that these SEC fractions within the same component were cognate and varied coherently across the dataset samples. The SEC-FLD-PARAFAC modeling and SEC-OCD-NMF analysis have clearly and concisely presented that the dramatic decreases of dissolved organic carbon, UV absorbance at 254 nm and protein-like fluorescence at Ex280/Em350 nm in the anaerobic process were primarily associated with the degradation of the large MW proteinaceous and polysaccharide-like biopolymers. It has also revealed that fluorescence of humic acid-like fractions increased significantly during the anaerobic process, but fluorescence of fulvic acid-like and humic substances’ building blocks decreased slightly. Laboratory experiments further confirmed the presence of the humification process in anaerobic processes, and the formation of humic acid-like fluorophores may be associated with carbohydrate metabolism. The combination of SEC-FLD-PARAFAC and SEC-OCD-NMF helped to establish the links between changes in bulk water quality parameters and the evolution of SEC MW fractions, which provides a more in-depth insight into wastewater DOM treatability and enables the optimization of wastewater treatment processes.

Prognostic model for prostate cancer based on glycolysis-related genes and non-negative matrix factorization analysis

Background: Establishing an appropriate prognostic model for PCa is essential for its effective treatment. Glycolysis is a vital energy-harvesting mechanism for tumors. Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential. Methods: First, gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and glycolysis-related genes were obtained from the Molecular Signatures Database (MSigDB). Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained, which were used in non-negative matrix factorization (NMF) to identify clusters. The correlation between clusters and clinical features was discussed, and the differentially expressed genes (DEGs) between the two clusters were investigated. Based on the DEGs, we investigated the biological differences between clusters, including immune cell infiltration, mutation, tumor immune dysfunction and exclusion, immune function, and checkpoint genes. To establish the prognostic model, the genes were filtered based on univariable Cox regression, LASSO, and multivariable Cox regression. Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model. A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting. Result: The genes obtained from MSigDB were enriched in glycolysis functions. Two clusters were identified by NMF analysis based on 272 glycolysis-related genes, and a prognostic model based on DEGs between the two clusters was finally established. The prognostic model consisted of LAMPS, SPRN, ATOH1, TANC1, ETV1, TDRD1, KLK14, MESP2, POSTN, CRIP2, NAT1, AKR7A3, PODXL, CARTPT, and PCDHGB2. All sample, training, and test cohorts from The Cancer Genome Atlas (TCGA) and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups. The area under the ROC curve showed great performance of this prognostic model. Conclusion: A prognostic model based on glycolysis-related genes was established, with great performance and potential significance to the clinical application.

Glycosylation-related molecular subtypes and risk score of hepatocellular carcinoma: Novel insights to clinical decision-making.

Hepatocellular carcinoma (HCC) is the fifth most common cancer and the third leading cause of cancer deaths worldwide, seriously affecting human community health and care. Emerging evidence has shown that aberrant glycosylation is associated with tumor progression and metastasis. However, the role of glycosylation-related genes in HCC has notbeen reported. Weighted gene coexpression network analysis and non-negative matrix factorization analysis were applied to identify functional modules and molecularm subtypes in HCC. The least absolute shrinkage and selection operator Cox regression was used to construct the glycosylation-related signature. The independent prognostic value of the risk model was confirmed and validated by systematic techniques, including principal component analysis, T-distributed random neighbor embedding analysis, Kaplan-Meier survival analysis, the ROC curve, multivariate Cox regression, the nomogram, and the calibration curve. The single-sample gene set enrichment analysis, gene set variation analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses were evaluated by the immune microenvironment and potential biological processes. The quantitative real-time polymerase chain reaction and immunohistochemistry analysis were used to verify the expression of five genes. We identified the glycosylation-related genes with bioinformatics analysis to construct and validate a five-gene signature for the prognosis of HCC patients. Patients with HCC in the high-risk group had a worse prognosis. The risk score could be an independent factor and was associated with clinical features, such as the grade and stage. The nomogram exhibited an accurate score that included the risk score and clinical parameters. The infiltration levels of antitumor cells were upregulated in the low-risk group, including B_cells, Mast_cells, neutrophils, NK_cells, and T_helper_cells. Moreover, glycosylation was more sensitive to immunotherapy, and may play a critical role in the metabolic processes of HCC, such as bile acid metabolism and fatty acid metabolism. In addition, the five-gene messenger RNA (mRNA) and protein expression were overexpressed in HCC cells and tissues. The glycosylation-related signature is effective for prognostic recognition, immune efficacy evaluation, and substance metabolism in HCC, providing a novel insight for therapeutic target prediction and clinical decision-making.

Decoding the colorectal cancer ecosystem emphasizes the cooperative role of cancer cells, TAMs and CAFsin tumor progression

BackgroundSingle-cell transcription data provided unprecedented molecular information, enabling us to directly encode the ecosystem of colorectal cancer (CRC). Characterization of the diversity of epithelial cells and how they cooperate with tumor microenvironment cells (TME) to endow CRC with aggressive characteristics at single-cell resolution is critical for the understanding of tumor progression mechanism.MethodsIn this study, we comprehensively analyzed the single-cell transcription data, bulk-RNA sequencing data and pathological tissue data. In detail, cellular heterogeneity of TME and epithelial cells were analyzed by unsupervised classification and consensus nonnegative matrix factorization analysis, respectively. Functional status of epithelial clusters was annotated by CancerSEA and its crosstalk with TME cells was investigated using CellPhoneDB and correlation analysis. Findings from single-cell transcription data were further validated in bulk-RNA sequencing data and pathological tissue data.ResultsA distinct cellular composition was observed between tumor and normal tissues, and tumors exhibited immunosuppressive phenotypes. Regarding epithelial cells, we identified one highly invasiveQuery cluster, C4, that correlated closely with tumor-associated macrophages (TAMs) and cancer-associated fibroblasts (CAFs). Further analysis emphasized the TAMs subclass TAM1 and CAFs subclass S5 are closely related with C4.ConclusionsIn summary, our study elaborates on the cellular heterogeneity of CRC, revealing that TAMs and CAFs were critical for crosstalk network epithelial cells and TME cells. This in-depth understanding of cancer cell-TME network provided theoretical basis for the development of new drugs targeting this sophisticated network in CRC.

Integrative analysis of the characteristic of lipid metabolism-related genes for the prognostic prediction of hepatocellular carcinoma.

Background:Dysregulation of lipid metabolism is implicated in the progression of hepatocellular carcinoma (HCC). We therefore investigated the molecular characteristics of lipid-metabolism-related genes in HCC.Methods:Multi-dimensional bioinformatics analysis was conducted to comprehensively identify the lipid metabolism-related genes (LMRGs) from public databases, as well as the clinical information, immune features, and biological characteristics of HCC. The IMGR were then used to classify HCC into molecular phenotypes. Six lipid metabolism-related genes sets with the potential to predict the prognosis of HCC patients were identified.Results:A total of 770 HCC patients with complete clinical information and corresponding 776 LMRGs were downloaded from 3 databases. Univariate cox and non-negative matrix factorization analyses were used to classify HCC patients into 2 clusters. This molecular classification was associated with overall survival, clinical characteristics, and immune cells. The biological function of the differentially expressed LMRGs in the 2 clusters showed the genes associated with tumor-related metabolism pathways. A combination of multivariate/univariate cox regression and least absolute shrinkage and selection operator analyses were conducted to build a 6 LMRGs signature (6-IS) to predict the prognosis of HCC. The 6-IS signature was found to be an independent prognostic factor. Performance of the 6-IS prognostic signature was verified in a validation set and compared with an external data set. Results revealed the 6-IS signature could effectively predict the prognosis of patients with HCC.Conclusions:This study provides new insights into the role of LMRG in the pathogenesis of HCC and presents a novel prognostic signature 6-IS monitoring the clinical course of HCC.

183. IMPACTS OF NEOADJUVANT CHEMORADIOTHERAPY ON THE IMMUNE LANDSCAPE OF ESOPHAGEAL SQUAMOUS CELL CARCINOMA

Abstract Neoadjuvant chemoradiation (neoCRT) followed by surgery is the most common approach for locally advanced resectable esophageal squamous cell carcinoma (ESCC) patients. Tumor microenvironment (TME) has been implicated in tumor response to treatment. Here, we aimed to investigate the impacts of neoCRT on the complex immune landscape in ESCC TME. Single-cell RNA sequencing (scRNA-seq) were conducted to examine the neoCRT-driven cellular and molecular dynamics in ESCC samples obtained before and after neoCRT. Consensus non-negative matrix factorization analysis was applied to decipher coherent gene expression programs of tumor cells. Activities of pathways and transcription factors of individual cells were analyzed by gene set variation and gene regulatory network analyses, respectively. Cell fate tracing was inferred by RNA velocity analysis. Multiplex fluorescent immunohistochemistry was performed to validate the changes of immune cells identified by scRNA-seq in another 25 pairs of pre- and post-neoCRT ESCC samples. Expression programs of cell cycle, adhesion, stress response, and epithelial differentiation in pre-treatment tumor cells related to neoCRT response. Infiltration of CD8+ T cells with exhausted cells dominant increased in both major and minor responders after neoCRT. Treg cell differentiation decreased after neoCRT in major responders but increased in minor ones. NeoCRT promoted the maturation of cDC1s but decreased the number of cDC2s. Expression of M2 macrophage markers increased after neoCRT. Immune-related pathways were enriched at higher levels in CD8+ T cells and macrophages of major responders as compared to minor responders before neoCRT, which was reversed after neoCRT. Our comprehensive picture of the neoCRT-related dynamic immune ecosystem provides deeper insights into immunological mechanisms associated with ESCC response to neoCRT, which may aid in future development of immune-strategies for improving ESCC treatment.

Event prediction in social network through Twitter messages analysis.

Event detection using social media analysis has attracted researchers' attention. Prediction of events especially in the management of social crises can be of particular significance. In this study, events are predicted through analyzing Twitter messages and examining the changes in the rate of Tweets in a specified subject. In the proposed method, the Tweets are initially preprocessed in consecutive fixed-length time windows. Tweets are then categorized using the non-negative matrix factorization analysis and the distance dependent Chinese restaurant process incremental clustering. The categorization results show that a high rate of Tweets entering a cluster represents the occurrence of a new event in near future. Finally, a description of the event is presented in the form of some frequent words in each cluster. In this paper, investigations on a Tweet dataset during a 6-month period indicate that the rate of sending Tweets about predictable events considerably changes before their occurrence. The use of this feature can make it possible to predict events with high degrees of precision.

Abstract 4075: Meta-analysis of neuroblastoma single cell RNA-seq datasets identifies conserved and divergent gene expression programs across human and preclinical models

Abstract Neuroblastoma is a highly heterogeneous disease not only in the clinical presentation of individual patients, but also in the cellular composition of any given tumor. Insights into this diversity have only recently been enabled due to advancements in single cell technologies, which have facilitated investigation of this disease at unprecedented resolution and detail. Coinciding with the growing number of scRNA-seq technologies, so too are the number of single cell datasets encompassing neuroblastoma patients across several institutions. However, due to the rarity of the affliction and sample access, the cohort pool in each aforementioned scRNA-seq study is limited to a reduced representation of the spectrum of disease classifications, which limits the ability of any single study to draw conclusions about neuroblastoma as a whole. Moreover, inconsistencies in data acquisition and analytical approaches across these studies have led to diverging interpretations. As such, we decided to amass the entirety of publicly available neuroblastoma scRNA-seq studies, representing a more comprehensive cross-section of patient presentations, towards the goal of conducting an exhaustive meta-analysis of the underlying data. To this end, we have implemented a generalizable non-negative matrix factorization (NMF)-based framework targeted at discovering conserved gene expression programs in malignant neuroblastoma as well as the supporting microenvironment. Using graph-based network analyses for classification of gene expression programs, we have identified conserved signatures of malignant and non-tumor cell types in neuroblastoma. In addition to defining the landscape of expression programs in human neuroblastoma patients, we have also utilized the NMF analysis to assess the alignment of several preclinical models to human signatures. We have identified gene expression programs that align to malignant human expression programs as well as signatures more closely related to non-tumor cell types. These include previously characterized divergent mesenchymal and adrenergic programs, as well as undescribed liver/metabolic, neuronal, and glial signatures. When considering the affinity of neuroblastoma models to malignant human profiles we observed specific agreement between certain preclinical signatures and subtype classifications found in patient samples. Careful consideration of these results will allow researchers to guide preclinical studies by cross-referencing neuroblastoma models of interest with patient profiles. Overall, we characterize the most updated view of the landscape of neuroblastoma by documenting the full repertoire of gene expression programs across patient and preclinical models. Citation Format: Richard Chapple, Charlie Wright, Min Pan, Paul Geeleher. Meta-analysis of neuroblastoma single cell RNA-seq datasets identifies conserved and divergent gene expression programs across human and preclinical models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4075.

PAHs Source Identification in Sediments and Surrounding Soils of Poyang Lake in China Using Non-Negative Matrix Factorization Analysis

Identifying sources of soil and sediment PAHs and apportioning their contributions are key in building effective pollution abatement strategies, especially for Poyang Lake—the largest freshwater lake in China. PAHs were detected in all the monitored soil and sediment samples under three land use types, with the concentrations varying by area, ranging from moderate to relatively high. The order of PAHs content in different the land use types was as follows: industrial soil &gt; grassland soil &gt; agricultural soil. Although agricultural soil was dominated by LMW PAHs, industrial grassland soils were dominated by HMW PAHs. Based on factor analysis, non-negative matrix factorization analysis was effective in non-negative constrained skew rotation, especially for clear and interpretable source analysis of PAHs.

ATRT-10. Single-cell transcriptional profiling of ATRTs reveals heterogeneous signatures of tumor and non-malignant cell populations

Atypical Teratoid/Rhabdoid Tumors (ATRTs) are known for exhibiting high inter-tumor heterogeneity, even though they are almost all characterized by a common loss of SMARCB1 (or rarely SMARCA4). Three subgroups have been identified at bulk methylome and transcriptome level: ATRT-TYR, ATRT-SHH, and ATRT-MYC. To better understand the biology underlying each subgroup and potentially unveil their (different) cell(s) of origin, we performed single-cell transcriptomic analyses in 22 ATRTs using fresh frozen samples and both 10X and Smartseq technology. All data, grouped by technology, underwent quality control and normalization, regressing out the biases introduced by each sample. Tumor microenvironment (TME) and tumor bulk (TB) clusters were characterized by a combination of copy number variant analyses, enrichment in literature lists of marker genes for specific cell populations, and in-depth analysis of differentially enriched (DE) genes. Non-negative Matrix Factorization (NMF) was applied to TB to reveal major transcriptional profiles, which were grouped into meta-signatures. A total of 71 gene lists were retrieved from NMF (TB) and DE analyses (TME + TB), that gathered into 11 signature groups by Jaccard similarity, with one extra group accounting for unique signatures. Three groups targeted TME, accounting for either microglia, fibroblasts and endothelial cells, or OPCs, oligodendrocytes, astrocytes and neurons. These signatures are enriched in specific clusters across technologies. The remaining eight groups divide into two types, either enriched in clusters predominantly formed by cells of one or two ATRT subgroups or signatures enriched for a particular phenotype, such as cilial, cycling, axonogenesis or EM transition. While the first type is enriched across clusters in a gradient fashion, the second shows enrichment for selected clusters across technologies. Further analyses on the integrated dataset and additional samples are ongoing to validate and refine these 11 signature groups in ATRTs to see how this may lead to new treatment approaches.

Characterization of PM2.5 Mass in Relation to PM1.0 and PM10 in Megacity Seoul

This study examines the PM2.5 characteristics in Seoul in relation to those of PM1.0 and PM10. Samples were typically collected daily on filters and a few hours sampling were conducted during a few haze events (March 2007 to June 2008). Mean mass concentrations of PM1.0, PM2.5, and PM10 were 19.7 μg/m3, 26.0 μg/m3, and 48.2 μg/m3, respectively, and PM2.5 was reasonably correlated with PM1.0 (γ=0.79) and PM10 (γ=0.52). Three mass group types were mainly distinguished. Group 1 (31%): linear increase of PM1.0 with PM10 and high OC and NO3−; Group 2 (17%): PM10 considerably higher than PM1.0 and high Ca2+ and SO42−; Group 3 (52%): PM1.0 relatively more enhanced than PM10 and highest carbonaceous fraction against mass. The fine mode fraction was lowest (highest) in Group 2 (Group 3). Haze and dust episodes relating to Chinese outflows were mostly evident in Groups 1 and 2, respectively; average PM2.5 concentrations were visibly higher than in Group 3. Non-Negative Matrix Factorization analysis demonstrated that traffic-related urban primary (28%) and coal-fired industry (27%) emissions equally contributed to the PM2.5 mass, followed by aged urban secondary (19%), soil mineral (16%), and biomass combustion (10%) sources. Seasonal variations were apparent in air mass trajectories. Urban primary and coal-fired industry factors were predominant in Group 3 under stagnant conditions in the warm season and under a strong northerly wind in the cold season, respectively. However, contributions of the other three factors were higher in Groups 1 and 2. This study shows that the PM2.5 mass in Seoul is largely dependent on high concentration episodes occurring mostly in cold seasons. It also shows that local emissions contribute considerably during warm months, while the influence of Chinese outflow predominates during cold months.

A Novel Constrained Non-negative Matrix Factorization Method for Group Functional Magnetic Resonance Imaging Data Analysis of Adult Attention-Deficit/Hyperactivity Disorder.

Attention-deficit/hyperactivity disorder (ADHD) is a common childhood psychiatric disorder that often persists into adulthood. Extracting brain networks from functional magnetic resonance imaging (fMRI) data can help explore neurocognitive disorders in adult ADHD. However, there is still a lack of effective methods to extract large-scale brain networks to identify disease-related brain network changes. Hence, this study proposed a spatial constrained non-negative matrix factorization (SCNMF) method based on the fMRI real reference signal. First, non-negative matrix factorization analysis was carried out on each subject to select the brain network components of interest. Subsequently, the available spatial prior information was mined by integrating the interested components of all subjects. This prior constraint was then incorporated into the NMF objective function to improve its efficiency. For the sake of verifying the effectiveness and feasibility of the proposed method, we quantitatively compared the SCNMF method with other classical algorithms and applied it to the dynamic functional connectivity analysis framework. The algorithm successfully extracted ten resting-state brain functional networks from fMRI data of adult ADHD and healthy controls and found large-scale brain network changes in adult ADHD patients, such as enhanced connectivity between executive control network and right frontoparietal network. In addition, we found that older ADHD spent more time in the pattern of relatively weak connectivity. These findings indicate that the method can effectively extract large-scale functional networks and provide new insights into understanding the neurobiological mechanisms of adult ADHD from the perspective of brain networks.

Immune-related lncRNA classification of head and neck squamous cell carcinoma

BackgroundLong noncoding RNAs (lncRNAs) play a critical role in innate and adaptive immune responses. Thus, we aimed to identify ideal subtypes for head and neck squamous cell carcinoma (HNSCC) based on immune-related lncRNAs.MethodsTCGA HNSCC cohort was divided into two datasets (training and validation dataset), and 960 previously characterized immune-related lncRNAs were extracted for non-negative matrix factorization analysis. We characterized our HNSCC subtypes based on biological behaviors, immune landscape and response to immunotherapy in both training and validation cohort. A lncRNA-signature was generated to predict our HNSCC subtypes, and essential lncRNAs involved in tumor microenvironment (TME) were identified.ResultsWe developed and validated two HNSCC subtypes (C1 and C2) based on the 70 lncRNAs in the training and validation cohort. C2 subtype displayed good prognosis, high immune cell infiltration, immune-related genes expression and sensitivity to PD-1 blockade. C1 subtype was associated with high activity of mTORC1 signaling and glycolysis as well as high fraction of inactive immune cells. Finally, we generated a 31-lncRNA signature that could predict our above subtypes with high accurate. Additionally, TRG-AS1 was identified as the essential lncRNA involving TME formation. Knockdown of TRG-AS1 inhibited the expression of HLA-A, HLA-B, HLA-C, CXCL9, CXCL10 and CXCL11. High expression of TRG-AS1 indicated a favorable prognosis in HNSCC and anti-PD-L1 cohort (IMvigor210).ConclusionsOur study establishes a novel HNSCC classification on the basis of 31-lncRNA, helping to identify beneficiaries for anti-PD-1 treatment. In addition, a critical lncRNA TRG-AS1 is identified as a new potential prognosis biomarker as well as therapeutic target.