Database For Annotation Research Articles

Integrated systems biology approach to identify key candidate genes, signaling pathways and therapeutic targets of oral squamous cell carcinoma

Abstract Background: This research aims to uncover gene signatures associated with oral squamous cell carcinoma (OSCC), the sixth most prevalent cancer globally, constituting about 5% of all malignant tumors. The study focuses on biomarker screening and identifying therapeutic targets, addressing a significant global issue. Materials and Methods: The study involved a comprehensive analysis of publicly available gene expression datasets, namely GSE30784 and GSE74530. The researchers conducted gene ontology (GO) and pathway analyses on genes that displayed differential expression (DEGs). They utilized the Database for Annotation, Visualization, and Integrated Discovery database to accomplish this. The String database also established a protein–protein interaction (PPI) network. This network was visualized through Cytoscape, and further exploration was carried out using Molecular Complex Detection and CytoHubba plugins. These steps aimed to identify crucial hub genes and functional modules. Furthermore, the study investigated transcription factor-gene regulatory networks focusing on the identified hub genes. The researchers employed the Gene Expression Profiling Interactive Analysis 2 tool to evaluate these hub genes’ expression levels and prognostic significance. Results: A comparison between OSCC and normal oral mucosa revealed 1210 DEGs across two databases. The obtained DEG list was cross-referenced with the DisGeNET disease database, identifying 205 potential DEG candidates. These DEGs exhibited enrichments in various biological functions, including angiogenesis, protein binding, focal adhesion, cell surface interactions, and extracellular matrix components. Among the enriched pathways, the interleukin (IL)-17 and tumor necrosis factor signaling pathways related to viral protein interactions showed significant enrichment. Further exploration involved extracting the top five hub genes from intricate PPI networks. These selected hub genes were subsequently verified in clinical samples, paving the way for subsequent in-depth analysis. Conclusion: In the study context, IL-6, chemokine (C-C motif) ligand 2, C-X-C Motif Chemokine Ligand 8, IL-1beta, and prostaglandin-endoperoxide synthase 2 emerged as the leading hub genes linked to the IL-10 signaling pathway in OSCC. These findings offer valuable insights into the potential mechanisms underlying the advancement of OSCC. However, it is essential to underscore that further research must validate these findings conclusively. The study’s outcomes have broadened our understanding of oral cancer’s molecular intricacies by identifying differentially expressed and pivotal hub genes. This newfound knowledge has the potential to catalyze the development of novel biomarkers, thereby enhancing the efficacy of both diagnostic and therapeutic strategies.

FRAGMENTISE: A user-friendly, cross-platform tool to create and analyze comprehensive small-molecule fragment databases.

The ongoing COVID-19 pandemic, and constant demand for new therapies in unmet clinical needs, necessitates strategies to identify drug candidates for rapid clinical deployment. Over the years, fragment-based drug design (FBDD) has emerged as a mainstream lead discovery strategy in academia, biotechnology start-ups, and large pharma. Chemical building block libraries are the fundamental component of virtually any FBDD campaign. Current trends focus on smaller and smarter libraries that offer synthetically amenable starting points for rational lead generation. Therefore, there remains an ever-increasing need for new methods to generate fragment libraries to seed early-stage drug discovery programs. Here, we present FRAGMENTISE-a new user-friendly, cross-platform tool for user-tunable retrosynthetic small-molecule fragmentation. FRAGMENTISE allows for visualization, similarity search, annotation, and in-depth analysis of the fragment databases in the medicinal chemistry context. FRAGMENTISE is available as standalone software for Linux, Windows, and macOS users, with a graphical interface or command-line version.

Mechanisms of Si-Wu Decoction in the treatment of ulcerative colitis revealed by network pharmacology and experimental verification

Ethnopharmacological relevanceSi-Wu Decoction (SWD) is a traditional Chinese medicine decoction. SWD is commonly used to treat blood deficiency syndrome. It is also used to treat some ulcerative colitis (UC) patients now, but the mechanism of action remains unclear. Aim of the studyThis study explored the efficacy and mechanism of action of SWD in treating UC based on network pharmacology and related experimental validation. Materials and MethodsSeveral databases were used to screen SWD for major active ingredients, targets of the ingredients, and UC disease genes. Cytoscape 3.8.2 software was used for topological analysis to construct the drug-compound-disease gene-target relationship network. The String database platform was used to construct the target protein interaction network. The DAVID (Database for Annotation, Visualization and Integrated Discovery) database was used to perform the GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis for the key targets. DSS (Dextran Sulfate Sodium)-induced UC mouse model was used to evaluate the in-vivo activity of SWD. Western Blot analysis and quantitative polymerase chain reaction were performed to verify the targets in the related pathways. ResultsNetwork pharmacology revealed that the SWD targeted pathway network involved 12 core targets and 15 major pathways. SWD may play a part by targeting key targets such as nuclear factor-kappaB (NF-κB), Janus kinase (JAK)-signal transducer and activator of transcription 3 (STAT3) pathway, and several mitogenic pathways. We showed that SWD largely restored the colorectal structure in UC model mice. Compared to the model group, the SWD group showed reduced infiltration of inflammatory cells. SWD significantly decreased the mRNA levels of IL-6 (Interleukin-6), TNF-α (Tumor necrosis factor-alpha), IL-1b (Interleukin-1beta) and other pro-inflammatory factors. Western Blot results showed that SWD concentration-dependently inhibited STAT3 and NF-κB activation in DSS-treated colon tissue. ConclusionOur findings suggest that SWD treats UC by inhibiting STAT3 and NF-κB signaling pathways, reducing the expression of inflammatory cytokines, and improving epithelial repair in experimental colitis, thus shedding light on the mechanisms by which SWD exerts its effects on UC.

Identification of Hub Genes and Upstream Regulatory Factors Based on Cell Adhesion in Triple-negative Breast Cancer by Integrated Bioinformatical Analysis.

Triple-negative breast cancer (TNBC) is characterized by metastasis and invasion, as well as poor prognosis, with chemotherapy being the main treatment option. Cell adhesion regulates tumorigenesis and new blood vessel formation. Thus, accurately identifying effective targets for TNBC and cell adhesion is challenging. Herein, we screened for differentially expressed genes between TNBC and normal cancer-free tissues to identify genes contributing to TNBC. Microarray data were obtained using a comprehensive gene-expression database. We used Database for Annotation, Visualization and Integrated Discovery, Kyoto Encyclopedia of Genes and Genomes and Functional Enrichment (FunRich) to perform Gene Ontology functional enrichment and predict signal pathways. The protein interaction network was predicted using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape v. 3.8.2. for visualization of results. TargetScan, miRanda, miRDB, miRWalk and RNA22 were used to predict miRNAs regulating key genes, and long non-coding RNAs (lncRNAs) regulating miRNAs were predicted using StarBase V2.0 from a comprehensive gene-expression database. Differentially expressed genes were mainly concentrated in the biological process of cell-cell adhesion. The protein-protein interaction network identified eight hub genes: Fibronectin 1 (FN1), Rac family small GTPase 1 (RAC1), heat-shock protein 90 alpha family class B member 1 (HSP90AB1), tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ), heat-shock protein family A member 8 (HSPA8), IQ motif containing GTPase-activating protein 1 (IQGAP1), CD44 molecule (CD44), and catenin beta 1 (CTNNB1). miRNAs related to TNBC occurrence and development were hsa-miR-142-5p, hsa-miR-144, hsa-miR-28-5p, hsa-miR-548d-3p, hsa-miR-587, hsa-miR-641, and hsa-miR-708. StarBase v2.0 predicted 12 lncRNAs, namely NEAT1, XIST, OIP5-AS1, MALAT1, AL035425.3, NORAD, AL391069.4, AC118758.3, AC026362.1, AC009065.4, AC016876.2, and AC093010.3, as upstream molecules that regulate miRNAs and which may regulate TNBC. Overall, mRNA-miRNA-lncRNA interactions appear to play a role in TNBC development.

Automatic sleep-stage classification of heart rate and actigraphy data using deep and transfer learning approaches

Manual sleep-stage scoring based on full-night polysomnography data recorded in a sleep lab has been the gold standard of clinical sleep medicine. This costly and time-consuming approach is unfit for long-term studies as well as assessment of sleep on a population level. With the vast amount of physiological data becoming available from wrist-worn devices, deep learning techniques provide an opportunity for fast and reliable automatic sleep-stage classification tasks. However, training a deep neural network requires large annotated sleep databases, which are not available for long-term epidemiological studies. In this paper, we introduce an end-to-end temporal convolutional neural network able to automatically score sleep stages from raw heartbeat RR interval (RRI) and wrist actigraphy data. Moreover, a transfer learning approach enables the training of the network on a large public database (Sleep Heart Health Study, SHHS) and its subsequent application to a much smaller database recorded by a wristband device. The transfer learning significantly shortens training time and improves sleep-scoring accuracy from 68.9% to 73.8% and inter-rater reliability (Cohen’s kappa) from 0.51 to 0.59. We also found that for the SHHS database, automatic sleep-scoring accuracy using deep learning shows a logarithmic relationship with the training size. Although deep learning approaches for automatic sleep scoring are not yet comparable to the inter-rater reliability among sleep technicians, performance is expected to significantly improve in the near future when more large public databases become available. We anticipate those deep learning techniques, when combined with our transfer learning approach, will leverage automatic sleep scoring of physiological data from wearable devices and enable the investigation of sleep in large cohort studies.

Network pharmacology-based approach to research the effect and mechanism of Si-Miao-Yong-An decoction against thromboangiitis obliterans

Background Si-Miao-Yong-An decoction (SMYAD) is a conventional therapeutic formula for treat thromboangiitis obliterans (TAO), consisting of four Chinese herbs: Lonicerae japonicae Thunb. (Jinyinhua), Scrophularia ningpoensis Hemsl. (Xuanshen), Angelica sinensis (Oliv.) Diels (Danggui) and Glycyrrhiza uralensis Fisch. (Gancao). However, the mechanism of SMYAD in TAO treatment remains unclear. Methods Components, as well as potential targets of SMYAD in TAO therapy, were downloaded from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Subsequently, with the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server, the gene ontology (GO) biological processes and the Kyoto encyclopedia of genes and genomes (KEGG) signalling pathways of the targets enrichment were performed. Next, based on STRING online database, the protein interaction network of vital targets was built and analysed. Molecular docking and calculation of the binding affinity were performed using AutoDock. The PyMOL software was employed to observe docking outcomes of active compounds and protein targets. Based on the predicted outcomes of network pharmacology, in vivo and in vitro tests were performed for validation. In vivo experiment, the TAO rats model was established using sodium laurate injection into the femoral artery. The symptoms as well as pathological changes of the femoral artery were observed. Besides, the predicted targets were verified by the RT-qPCR, in vitro experiment. The cell viability in LPS-induced human umbilical vein endothelial cells (HUVECs) was detected using CCK-8 kit, and the predicted targets were also verified by the RT-qPCR. Results In the network pharmacology analysis, we obtained 105 chemical components in SMYAD and 24 therapeutic targets. We found that the mechanism SMYAD in TAO therapy was primarily associated with inflammation and angiogenesis by constructing multiple networks. Quercetin, vestitol and beta-sitosterol were important compounds, and interleukin-6 (IL6), MMP9, and VEGFA were key targets. According to molecular docking, active compounds (quercetin, vestitol and beta-sitosterol) and targets (IL6, MMP9 and VEGFA) showed good binding interactions. In in vivo experiment, SMYAD ameliorated the physical signs and pathological changes, inhibited the expression of IL6 and MMP9, and enhanced the expression of VEGFA. In an in vitro experiment, SMYAD increased the cell viability of LPS-induced HUVECs and the expression of VEGFA, and reduced the expression of IL6 and MMP9. Conclusions This study showed that SMYAD improves TAO symptoms and inhibits the development of TAO. The mechanism could be associated with anti-inflammatory and therapeutic angiogenesis.

Characterization of natural peptides in Pheretima by integrating proteogenomics and label-free peptidomics

Pheretima, also called “earthworms”, is a well-known animal-derived traditional Chinese medicine that is extensively used in over 50 Chinese patent medicines (CPMs) in Chinese Pharmacopoeia (2020 edition). However, its zoological origin is unclear, both in the herbal market and CPMs. In this study, a strategy for integrating in-house annotated protein databases constructed from close evolutionary relationship-sourced RNA sequencing data from public archival resources and various sequencing algorithms (restricted search, open search, and de novo) was developed to characterize the phenotype of natural peptides of three major commercial species of Pheretima, including Pheretima aspergillum (PA), Pheretima vulgaris (PV), and Metaphire magna (MM). We identified 10,477 natural peptides in the PA, 7,451 in PV, and 5,896 in MM samples. Five specific signature peptides were screened and then validated using synthetic peptides; these demonstrated robust specificity for the authentication of PA, PV, and MM. Finally, all marker peptides were successfully applied to identify the zoological origins of Brain Heart capsules and Xiaohuoluo pills, revealing the inconsistent Pheretima species used in these CPMs. In conclusion, our integrated strategy could be used for the in-depth characterization of natural peptides of other animal-derived traditional Chinese medicines, especially non-model species with poorly annotated protein databases.

Efficacy of Buzhong Yiqi decoction on benign prostatic hyperplasia and its possible mechanism.

To explored the mechanism of Buzhong Yigi decoction ( BZYQD) in inhibiting prostatic cell proliferation effect. The compounds of BZYQD consisted with eight herbs were searched in TCMSP databases and the putative targets of BZYQD were collected in Drugbank database. Then, "Benign prostatic hyperplasia" (BPH) was used to find the targets based on the GeneCards, Online Mendelian Inheritance in Man (OMIM) and Therapeutic Target Database (TTD) databases, and they were further used to collect further collect the intersection targets between BZYQD and BPH by counter-selection. Next, Herb-Compound-Target-Disease network was constructed by Cytoscape software and protein interaction network was built by Search tool for recurring instances of neighbouring genes (STRING) database. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed by Database for Annotation, Visualization and Integrated Discovery (DAVID) database to predict the mechanism of the intersection targets. Mitogen activated protein kinase 8 (MAPK8), interleukin 6 (IL-6) and quercetin were chosen to perform molecular docking. Then 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay was to detect the bility of BPH-1 (BPH epithelial cell line) by treated with quercetin at the concentrations of 15, 30, 60, 120 μM for 12, 24, 48, 72 h. The production of IL-6, tumor necrosis factor-α (TNF-α), IL-1β and were mRNA expression detected by enzyme-linked immunosorbent assay kit and quantitative real-time polymerase chain reaction. Western blot was used to detect the expression of phospho-p38 mitogen-activated protein kinase (p-P38) and matrix metalloprotein-9 (MMP-9). A total 151 chemical ingredients of 8 herbs and 1756 targets in BZYQD, 105 common targets of BZYQD and BPH which mainly involving with MAPK8, IL-6, and so on. GO enrichment analysis got 352 GO entries (0.05) which included 208 entries of biological process, 64 entries of cell component and 80 entries of molecular function. KEGG pathway Enrichment analyses got 20 significant pathways which mainly involved with MAPK signaling way. MTT assay indicated quercetin inhibited the viability of BPH-1 cells by time-and dose-dependent manner. Quercetin decreased the IL-6, TNF-α and IL-1β production and mRNA expression, and the expression of p-P38 and MMP-9 were also obviously reduced after treated with quercetin. BZYQD inhibited BPH through suppressing inflammatory response which might involving with regulating the MAPK signaling way.

Risk factors and predictors of urogenous sepsis after percutaneous nephrolithotomy for idiopathic calcium oxalate nephrolithiasis.

The aim of this study was to use bioinformatics approaches to screen and identify the key genes of idiopathic calcium oxalate nephrolithiasis, and explore its potential molecular mechanism. The GSE73680 kidney stone data set was downloaded from the Gene Expression Omnibus (GEO). R software (The R Foundation for Statistical Computing) was used to screen differentially expressed genes. GeneMANIA and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) databases were used to analyze related genes interacting with crucial genes, and a protein-protein interaction (PPI) network was constructed. The differential genes were then subjected to the Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. The clinical data of 156 patients who received percutaneous nephrolithotomy (PCNL) therapy at our facility between January 2013 and December 2017 were retrospectively analyzed. The various parameters associated with postoperative urogenous sepsis were identified using multivariable logistic regression analysis. The study discovered one differentially expressed gene was nucleotide-binding oligomerization domain-containing protein 2 (NOD2). GO and KEGG analysis showed that NOD2 might affect the occurrence of idiopathic calcium oxalate kidney stones by affecting inflammation, receptor expression, immune environment, necrosis, apoptosis, and other pathways. The clinical parameter of patients who participated in the study, including preoperative urinary white blood cell (WBC) count, preoperative urinary nitrite, stone diameter, operation time, WBC count, and WBC D values, were statistically different between the systemic inflammatory response syndrome (SIRS) group and the urosepsis group. According to multivariate logistic regression analysis, the preoperative urine nitrite, calculus diameter, blood WBC, and NOD2 expression 3 hours after surgery were all independently associated with the urosepsis development. Preoperative urinary nitrite positive status, postoperative WBC count ≥2.98×109/L 3 hours after operation, stone diameter >6 cm, and low expression of NOD2 in renal papillary tissue are more likely to cause the urinary source of idiopathic calcium oxalate nephrolithiasis after PCNL urogenous sepsis. These parameters also offer a viable treatment paradigm for the perioperative management of PCNL in treating idiopathic calcium oxalate kidney stones.

Decreased zinc-fingers and homeoboxes family expression was associated with unfavorable outcomes and immune infiltration in lung adenocarcinoma.

The zinc-fingers and homeoboxes (ZHX) family is a group of nuclear homodimeric transcriptional repressors that play an essential role in developing and progressing diverse malignancies. However, the association of ZHX family expression with prognosis and immune infiltration in lung adenocarcinoma (LUAD) is still unclear. The current study aimed to investigate the relationship between ZHX family expression and clinical outcomes and immune infiltration in LUAD patients. ZHXs family expression was determined by using the Oncomine database and Cancer Cell Line Encyclopedia (CCLE). The impact of ZHXs family expression on prognosis was analyzed by using the Kaplan-Meier-plotter online database. The Search Tool for the Retrieval of Interacting Genes (STRING) database was utilized to construct the interaction network based on the selected differentially expressed genes associated with ZHXs. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform the enrichment of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The functional state of the ZHXs family in diverse types of malignancies was determined by CancerSEA. The Tumor Immune Estimation Resource (TIMER) database was used to evaluate the association of the ZHXs family with immune cell infiltrates. ZHXs family expression was validated by the Gene Expression Omnibus (GEO) database and real-time polymerase chain reaction (RT-PCR) in 10 paired tumors and normal tissues. ZHX1-3 expression level significantly decreased in LUAD compared with normal tissues. Attenuated ZHXs expression was significantly associated with unfavorable overall survival in LUAD patients. ZHX family members were positively associated with immune infiltration of monocytes, tumor-associated macrophages (TAMs), M1 and M2 macrophages in LUAD. ZHX family expression was also significantly related to a variety of immune marker sets in LUAD. GEO analysis and RT-PCR validated the significant decrease of ZHXs expression level in LUAD. The current study revealed that ZHX family expression was significantly correlated with unfavorable outcomes and immune infiltration in LUAD. The findings herein provide a promising basis for further study into the potential biological function of the ZHX family in LUAD and lay a foundation for developing therapeutic targets for LUAD patients.

The CIRCULATE Spain study: Circulating tumor DNA based decision for adjuvant treatment in localized colon cancer.

TPS3635 Background: A substantial proportion of colon cancer (CC) patients do not benefit from adjuvant chemotherapy, and almost 40% of them will develop metastases despite initial optimal treatment. We have designed the phase 2 CIRCULATE-SPAIN trial (EudraCT Number: 2021-000507-2) to prove the feasibility of using liquid biopsy detection of minimal residual disease to guide the post-surgical clinical management of localized CC patients. Moreover, it is important to define if conventional versus intensive adjuvant chemotherapy could convert plasma circulating tumor DNA (ctDNA) positive into a ctDNA negative status. Methods: CIRCULATE-SPAIN is a trial to prove the feasibility of using liquid biopsy detection of MRD to guide the post-surgical clinical management of early colon cancer patients. It is a multicenter, randomized, open-label phase II “Single-To-double ARm Transition (START)” study in which patients are firstly enrolled into a single arm (Phase IIa) study expandable to a randomized double arm (Phase IIb). The CIRCULATE-SPAIN trial involves two primary endpoints, one for the Phase IIa and a second one for the Phase IIb. The primary objective for Phase IIa is to analyze the potential effect of FOLFOXIRI as an intensive adjuvant treatment for ctDNA clearance as a surrogate biomarker of treatment efficacy. Only if the pre-fixed result of 14 out of 40 patients achieves ctDNA clearance, the trial will be expanded to a phase IIb in which patients will be enrolled to randomly receive intensive adjuvant treatment (FOLFOXIRI) versus conventional adjuvant therapy (CAPOX). The primary objective in this phase is to study differences on the conversion rate between both groups. An academic assay based on tumor informed approach is used for ctDNA determination. Tumor and germline samples analysis are carried out by an in-house-developed bioinformatics pipeline specialized in variant-calling analysis using a custom 29-gene panel. Somatic variants are identified by the germline mutations extraction. Different annotation databases and an internal pathological mutation database are used to rule out potential benign somatic variants. Those candidate somatic mutations with the highest allelic frequency are selected and a specific panel of amplicons is designed to monitor their presence in ctDNA using NGS. Plasma samples analysis is carried out by an in-house pipeline specialized in reducing potential background noise. To assess panel performance in both sample types, the bioinformatics pipelines include a set of methods to calculate and filter according to different quality control metrics. Recruitment for the Phase IIa is ongoing with a total of 67 pre-screened out of which two patients were eligible. Clinical trial information: 2021-000. 507-20 .

Alternative allele frequency recallibration after NGS data analysis.

3077 Background: With the spread of NGS in routine clinical practice data analysis methods actively evolve to increase and refine information yield. Not much attention is given to variant allele fraction (VAF) estimation though it impacts clinical insignificance of subclonal somatic variants and identification of variant origin. Methods: Real-world sequencing data of 2379 samples (including 781 tumor-only and 1598 blood-only sequencing datasets) obtained via amplicon-based sequencing was used for the retrospective analysis of variant allele frequency estimation of identified genetic variants employing different methods including ITVC, SiNVICT, Mutect2 and SGA. Original method (AODvAF) utilizing alignment-free algorithm based on wild-type and alternative genome subsequence abundance estimation was developed and compared with the original output from NGS data analysis software. Anticipated VAF estimation was based on analyte type and database annotation. Results: Across 3447 identified potentially germline variations (PGV) VAF estimations provided by ITVC, SiNVICT and SGA were highly correlated (Pearson correlation coefficient [PCC] 1.00), while Mutect2 and AODvAF demonstrated discordant results (Mutect2/AODvAF vs ITVC PCC 0.87/0.82). VAF miscalculation defined by absolute difference between estimated and anticipated VAF of 10% and more was noted for 25%, 26%, 22%, 15% and 7% for ITVC, SiNVICT, SGA, Mutect2 and AODvAF respectively. Across 308 pathogenic BRCA1, BRCA2, PALB2 mutations identified in blood-only sequencing 25, 15, 16, 10 and 5 were characterized by VAF of 30% and lower based on ITVC, SiNVICT, SGA, Mutect2 and AODvAF estimations respectively (54, 49, 33, 51 and 9 for VAF 40% and lower), which may lead to misinterpretation of these variants as post-zygotic. Automatic in-silico annotation of variant origin (germline vs somatic) based on ISOWN was correct for 89%, 91%, 94%, 93% and 98% of these variants. Variant origin for 55 pathogenic variants of genes associated with hereditary cancer syndromes identified in tumor-only sequencing was verified by Sanger sequencing. Cohen's kappa coefficient for variant origin discrimination (Sanger vs in-silico prediction) was 0.91, 0.90, 0.91, 0.94 and 0.97 based on VAF provided by ITVC, SiNVICT, SGA, Mutect2 and AODvAF respectively. Conclusions: Accurate VAF calculation allows precise variant origin identification and impacts interpretation of variants. Developed method allows correction of VAF after NGS data core analysis.

Exchanging genomics reports between pathology labs and medical centers using the Minimal Clinical Oncology Data Element (mCODE) FHIR implementation guide.

e13575 Background: The automated transfer of discrete molecular data from pathology labs into the electronic health record (EHR) is rapidly becoming commonplace and is designed to facilitate clinical decision making. Simple genomic changes are represented in existing EHR data transfer standards, but current data standards lack clinical fields that correspond to treatment insights such as associated medications and potential clinical trials. To address this, the mCODE effort defines a consensus oncology data standard that facilitates lightweight transmission of data from patients with cancer. mCODE’s support of genomic data leverages efforts by the HL7 Clinical Genomics Working Group (HL7 CGWG) focusing on oncology. Here, we describe pilot efforts involving both a pathology partner and academic medical centers to extend mCODE FHIR (Fast Healthcare Interoperability Resources) implementation guide (IG) support of genomic data for data interchange and clinical decision support use cases. Methods: From 2021-2022, validation of the mCODE FHIR IG genomic data elements was performed in CodeX, a FHIR accelerator program. These elements encompassed: lab facility, test order information, pathologist, patient demographics, specimen, molecular studies and therapeutic implications. Genomic FHIR data elements were selected for their conciseness and clinical relevance for inclusion in the mCODE FHIR IG under the supervision of two oncology informaticians. As part of the mCODE FHIR IG pilot, synthetic solid tumor molecular patient reports were generated by Tempus Labs. These synthetic molecular reports were transformed into mCODE-conformant FHIR files for transfer. The mCODE IG can be found at http://hl7.org/fhir/us/mcode/ . Results: Over 50 Tempus data elements were analyzed and found to be readily mapped to mCODE genomics elements. Two categories of data elements required additional transformation: 1. Data elements requiring additional genomic database annotations; 2. FHIR syntax constraints like the concatenation of medications in the same drug class. The mCODE file was transmitted to Vanderbilt and the Children's Hospital LA from Tempus for decoding and downstream uses. This effort further informed the HL7 CGWG of needed IG clarifications for compatibility with other standardization efforts and genomic use cases. Conclusions: mCODE’s support of genomic data elements provides a lightweight genomics interchange standard compatible with an existing pathology lab report. Mapping from existing molecular pathology reports to mCODE required nominal alterations. Furthermore, mCODE’s alignment with HL7 genomics efforts ensure FHIR-based compatibility in other clinical domains and with developing international standards. Continued pathology lab and academic medical center collaboration is essential for mCODE refinement and adoption.

Genome Sequence Resource of Curvularia clavata Causing Leaf Spot Disease on Tobacco by Oxford Nanopore PromethION.

Genome Sequence Resource of Curvularia clavata Causing Leaf Spot Disease on Tobacco by Oxford Nanopore PromethION.

(240) Penile Prosthesis Biofilms Harbor Bacteria, Viruses, and Protozoa, and Composition Differs by Device Type but Not Infection Status

Abstract Introduction The penile prosthesis is the gold standard surgical treatment for erectile dysfunction refractory to medical treatment, but a subset of individuals require device revision for mechanical failure, pain, erosion or infection. Clinical cultures have been used to demonstrate that both infected and non-infected explanted devices harbor bacteria. We hypothesized that penile prostheses generate complex adherent biological structures called biofilms that consistently harbor microbes, even in the absence of infection, and that their composition would differ by device type and infection status. Objective Using shotgun metagenomics, we sought to determine the biofilm composition of indwelling penile prostheses and compare them by device type and infection status. Methods Approval for this study was obtained from our institutional IRB, and informed consent was provided by all participants who underwent removal and replacement of a penile prosthesis for any indication. Prior to access of the device, the dartos fascia was swabbed as a control. Upon access of the device, instruments and gloves from the surgical team were changed to minimize contamination from the surrounding tissue. The device was explanted, and each component was analyzed in parallel. Device components were subjected to sonication, DNA extraction, and shotgun metagenomics. The curated reads were then compared to annotated databases of known bacterial, viral, fungal, and protozoal species. Metabolic pathway genes were identified and compared to KEGG pathways to generate a functional metabolic analysis. Results 19 devices (16 Boston Scientific; 3 Coloplast) were included in the study. Indications for removal were infection (n=4), pain (n=2), mechanical failure (n=11), and impending erosion (n=2). Of non-host derived reads, approximately 65% were prokaryotic, 15% viral, 15% protozoan, and no fungal in origin (Figure, left). Beta-diversity of bacteria (p=0.046, Figure, right), virus (p=0.04), non-human genes (p=0.02), and gene pathways differed by device type (p=0.04). Interestingly, none of these differed by infection status (p>0.05). Sulfur metabolism was commonly enriched throughout devices, indicative of anaerobic growth conditions. Sphingobacterium hotanense, and the flavin mononucleotide metabolism pathway, were enriched in the context of infection. After exclusion of the 4 devices explanted for infection, there were 16 differentially abundant prokaryotic taxa by device type including 4 Pseudomonas species. Biofilm and antibiotic resistance genes were commonly represented independent of device type or infection status. Conclusions Both non-infected and infected devices consistently harbored microbiota in the form of biofilms, which were rich in prokaryotic, viral, and protozoal taxa. Device type was a greater driver of variability than infection status. Our data suggest that different device types harbor variable affinity for microbiota. Mechanistic studies are needed to understand whether the identified microbiota might protect against or contribute to the risk of infection, pain or erosion. Disclosure No

Identification of the hub genes associated with prostate cancer tumorigenesis.

Prostate cancer (PCa) is one of the most common malignant tumors of the male urogenital system; however, the underlying mechanisms remain largely unclear. This study integrated two cohort profile datasets to elucidate the potential hub genes and mechanisms in PCa. Gene expression profiles GSE55945 and GSE6919 were filtered from the Gene Expression Omnibus (GEO) database to obtain 134 differentially expressed genes (DEGs) (14 upregulated and 120 downregulated) in PCa. Gene Ontology and pathway enrichment were performed using the Database for Annotation, Visualization, and Integrated Discovery, showing that these DEGs were mainly involved in biological functions such as cell adhesion, extracellular matrix, migration, focal adhesion, and vascular smooth muscle contraction. The STRING database and Cytoscape tools were used to analyze protein-protein interactions and identify 15 hub candidate genes. Violin plot, boxplot, and prognostic curve analyses were performed using Gene Expression Profiling Interactive Analysis, which identified seven hub genes, including upregulated expressed SPP1 and downregulated expressed MYLK, MYL9, MYH11, CALD1, ACTA2, and CNN1 in PCa compared with normal tissue. Correlation analysis was performed using the OmicStudio tools, which showed that these hub genes were moderately to strongly correlated with each other. Finally, quantitative reverse transcription PCR and western blotting were performed to validate the hub genes, showing that the abnormal expression of the seven hub genes in PCa was consistent with the analysis results of the GEO database. Taken together, MYLK, MYL9, MYH11, CALD1, ACTA2, SPP1, and CNN1 are hub genes significantly associated with PCa occurrence. These genes are abnormally expressed, leading to the formation, proliferation, invasion, and migration of PCa cells and promoting tumor neovascularization. These genes may serve as potential biomarkers and therapeutic targets in patients with PCa.

Prognostic Values of Core Genes in Pilocytic Astrocytom

Pilocytic astrocytoma (PA) is the most common primary brain tumor in children and adolescents. Treatment strategy largely depends on its key genes and molecular mutations. This study aimed to identify potential biomarkers of PA closely related to its prognosis. The gene expression profiles (series numbers GSE50161, GSE66354, and GSE86574) of PA and normal brain tissues were downloaded from the Gene Expression Omnibus database. The Gene Expression Omnibus2R was used to identify differentially expressed genes. The overlapping differentially expressed genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. A protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. The Gene Expression Profiling Interactive Analysis 2 (GEPIA2) tool analyzed the impact of hub genes on PA prognosis based on the Kaplan-Meier curves. Compared with normal brain tissues (n= 36), a total of 37 upregulated and 144 downregulated genes were identified in PA (n= 40). In the protein-protein interaction network construction and GEPIA2 survival analysis, 2 of the top 10 hub genes were significantly associated with decreased overall survival of PA patients, namely Gamma-aminobutyric acid A receptor alpha 2 (hazard ratio = 2.8, P < 0.01) and regulating synaptic membrane exocytosis protein 1) (hazard ratio= 3.2, P < 0.01). This bioinformatics analysis reveals that low expression of Gamma-aminobutyric acid A receptor alpha 2 and regulating synaptic membrane exocytosis protein 1 is associated with a favorable prognosis for PA patients. These 2 hub genes could be novel biomarkers for prognosis assessment, furthermore a key element for treatment decisions in the future.

Learning precise feature via self-attention and self-cooperation YOLOX for smoke detection

Smoke detection is a key process for fire warning systems. However, the existing smoke detection methods are insufficient to extract precise smoke features due to the smoke’s transparency and variability. To solve this problem, we adopt the efficient YOLOX architecture and devise three strategies to enhance it. A self-cooperation mechanism is proposed to directly remove redundancy and then condense localization and semantic information. Moreover, we utilize the light-weight self-attention mechanism to emphasize the meaningful features of smoke. Finally, we equip the network with the piece-wise focal loss to consolidate its performance towards hard samples. The proposed method is termed as self-attention and self-cooperation YOLOX (SASC-YOLOX). In addition, we build a database that contains images from real scenes and manually annotate them, named annotated real smoke database of Xi’an Jiaotong University (XJTU-RS). SASC-YOLOX obtains 72.6% and 92.1% AP on our database and a synthetic database, respectively, outperforming the state-of-the-art methods. Extensive visualization experiments also validate that SASC-YOLOX has a strong feature extraction ability. Code is available at https://github.com/jingjing-maker/SASC-YOLOX.

Characterization of the natural peptidome of four leeches by integrated proteogenomics and pseudotargeted peptidomics

Animal-derived drugs are an indispensable part of folk medicine worldwide. However, their chemical constituents are poorly approached, which leads to the low level of the quality standard system of animal-derived drugs and further causes a chaotic market. Natural peptides are ubiquitous throughout the organism, especially in animal-derived drugs. Thus, in this study, we used multi-source leeches, including Hirudo nipponica (HN), Whitmania pigra (WP), Whitmania acranulata (WA), and Poecilobdella manillensis (PM), as a model. A strategy integrating proteogenomics and novel pseudotargeted peptidomics was developed to characterize the natural peptide phenotype and screen for signature peptides of four leech species. First, natural peptides were sequenced against an in-house annotated protein database of closely related species constructed from RNA-seq data from the Sequence Read Archive (SRA) website, which is an open-sourced public archive resource. Second, a novel pseudotargeted peptidomics integrating peptide ion pair extraction and retention time transfer was established to achieve high coverage and quantitative accuracy of the natural peptides and to screen for signature peptides for species authentication. In all, 2323 natural peptides were identified from four leech species whose databases were poorly annotated. The strategy was shown to significantly improve peptide identification. In addition, 36 of 167 differential peptides screened by pseudotargeted proteomics were identified, and about one-third of them came from the leucine-rich repeat domain (LRR) proteins, which are widely distributed in organisms. Furthermore, six signature peptides were screened with good specificity and stability, and four of them were validated by synthetic standards. Finally, a dynamic multiple reaction monitoring (dMRM) method based on these signature peptides was established and revealed that one-half of the commercial samples and all of the Tongxinluo capsules were derived from WP. All in all, the strategy developed in this study was effective for natural peptide characterization and signature peptide screening, which could also be applied to other animal-derived drugs, especially for modelless species that are less studied in protein database annotation.

Integrative computational approach for gene expression profiling of metastatic breast cancer

Background: Breast cancer (BC) is one of the most common cancers in women worldwide, including an estimated 570,000 deaths in 2015. More than 1.5 million women (25% of all women with cancer) are diagnosed with BC every year worldwide. BC is a metastatic cancer and usually spreads to distant organs such as bone, liver, lungs and the brain. Early detection of the disease can lead to better prognosis and higher survival rates. After genome sequencing, DNA microarray analysis has become the most widely used source of genome scale data in life sciences. Microarray's definition studies produce a large number of genetic expression and other genomics performance data, which promises to provide key information on genetic functioning and interaction within and within metabolic pathways. Aim: Prediction and functional enrichment of significant genes expressed in Breast Cancer. Materials and Methods: Microarray data for this study were downloaded from NCBI gene expression omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo), with accession number of GSE157737. It includes four different kind sets of samples, i.e., healthy control neutrophils, healthy donor monocytes, metastatic BC patient G-myeloid-derived suppressor cell (G-MDSCs) and sepsis patient G-MDSCs. The statistical method was applied to identify the differentially expressed gene (DEGs) using Limma package and significant analysis of microarray (SAM) test. Results: The top ten DEGs were identified between different sample sets using Limma package. Out of 32,321 genes, 93 genes were identified to be the significant genes through SAM test. The functional annotation of significant genes was done using different databases such as gene ontology, Kyoto Encyclopedia of Genes and Genomes and Database for Annotation, Visualisation and Integrated Discovery. Significant genes were found to be enriched in haematopoietic cell lineage pathway and the cancer-causing pathway. Conclusions: This research identifies the important genes that have function in BC and regulation of such genes and pathway can be a potential treatment for the BC and prevent the tumour growth.