Differential expression of circRNAs during osteogenic/odontogenic differentiation of stem cells from apical papilla promoted by blue light-emitting diode.

BACKGROUND: Circular RNA (circRNA) is a key player in regulating the multidirectional differentiation of stem cells. Previous research by our group found that the blue light-emitting diode (LED) had a promoting effect on the osteogenic/odontogenic differentiation of human stem cells from apical papilla (SCAPs). This research aimed to investigate the differential expression of circRNAs during the osteogenic/odontogenic differentiation of SCAPs regulated by blue LED. MATERIALS AND METHODS: SCAPs were divided into the irradiation group (4 J/cm2) and the control group (0 J/cm2), and cultivated in an osteogenic/odontogenic environment. The differentially expressed circRNAs during osteogenic/odontogenic differentiation of SCAPs promoted by blue LED were detected by high-throughput sequencing, and preliminarily verified by qRT-PCR. Functional prediction of these circRNAs was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the circRNA-miRNA-mRNA networks were also constructed. RESULTS: It showed 301 circRNAs were differentially expressed. GO and KEGG analyses suggested that these circRNAs were associated with some signaling pathways related to osteogenic/odontogenic differentiation. And the circRNA-miRNA-mRNA networks were also successfully constructed. CONCLUSION: CircRNAs were involved in the osteogenic/odontogenic differentiation of SCAPs promoted by blue LED. In this biological process, circRNA-miRNA-mRNA networks served an important purpose, and circRNAs regulated this process through certain signaling pathways.

Prognostic significance of CHCHD2P9 and ZNF204P in breast cancer: exploring their expression patterns and associations with malignancy-related genes.

BACKGROUND: Non-coding RNAs (ncRNAs) have a crucial impact on diverse cellular processes, influencing the progression of breast cancer (BC). The objective of this study was to identify novel ncRNAs in BC with potential effects on patient survival and disease progression. METHODS: We utilized the cancer genome atlas data to identify ncRNAs associated with BC pathogenesis. We explored the association between these ncRNA expressions and survival rates. A risk model was developed using candidate ncRNA expression and beta coefficients obtained from a multivariate Cox regression analysis. Co-expression networks were constructed to determine potential relationships between these ncRNAs and molecular pathways. For validation, we employed BC samples and the RT-qPCR method. RESULTS: Our findings revealed a noteworthy increase in the expression of AC093850.2 and CHCHD2P9 in BC, which was correlated with a poor prognosis. In contrast, ADAMTS9-AS1 and ZNF204P displayed significant downregulation and were associated with a favorable prognosis. The risk model, incorporating these four ncRNAs, robustly predicted patient survival. The co-expression network showed an effective association between levels of AC093850.2, CHCHD2P9, ADAMTS9-AS1, and ZNF204P and genes involved in pathways like metastasis, angiogenesis, metabolism, and DNA repair. The RT-qPCR results verified notable alterations in the expression of CHCHD2P9 and ZNF204P in BC samples. Pan-cancer analyses revealed alterations in the expression of these two ncRNAs across various cancer types. CONCLUSION: This study presents a groundbreaking discovery, highlighting the substantial dysregulation of CHCHD2P9 and ZNF204P in BC and other cancers, with implications for patient survival.

Investigation of normalization procedures for transcriptome profiles of compounds oriented toward practical study design.

The transcriptome profile is a representative phenotype-based descriptor of compounds, widely acknowledged for its ability to effectively capture compound effects. However, the presence of batch differences is inevitable. Despite the existence of sophisticated statistical methods, many of them presume a substantial sample size. How should we design a transcriptome analysis to obtain robust compound profiles, particularly in the context of small datasets frequently encountered in practical scenarios? This study addresses this question by investigating the normalization procedures for transcriptome profiles, focusing on the baseline distribution employed in deriving biological responses as profiles. Firstly, we investigated two large GeneChip datasets, comparing the impact of different normalization procedures. Through an evaluation of the similarity between response profiles of biological replicates within each dataset and the similarity between response profiles of the same compound across datasets, we revealed that the baseline distribution defined by all samples within each batch under batch-corrected condition is a good choice for large datasets. Subsequently, we conducted a simulation to explore the influence of the number of control samples on the robustness of response profiles across datasets. The results offer insights into determining the suitable quantity of control samples for diminutive datasets. It is crucial to acknowledge that these conclusions stem from constrained datasets. Nevertheless, we believe that this study enhances our understanding of how to effectively leverage transcriptome profiles of compounds and promotes the accumulation of essential knowledge for the practical application of such profiles.

Focused cancer pathway analysis revealed unique therapeutic targets in retinoblastoma.

Retinoblastoma (RB) is a pediatric cancer of the eye that occurs in 1/15000 live births worldwide. Albeit RB is initiated by the inactivation of RB1 gene, the disease progression relies largely on transcriptional alterations. Therefore, evaluating gene expression is vital to unveil the therapeutic targets in RB management. In this study, we employed an RT2 Profiler™ PCR array for a focused analysis of 84 cancer-specific genes in RB. An interaction network was built with gene expression data to identify the dysregulated pathways in RB. The key transcript alterations identified in 13 tumors by RT2 Profiler™ PCR array was further validated in 15 tumors by independent RT-qPCR. Out of 84 cancer-specific genes, 68 were dysregulated in RB tumors. Among the 68 genes, 23 were chosen for further analysis based on statistical significance and abundance across multiple tumors. Pathway analysis of altered genes showed the frequent perturbations of cell cycle, angiogenesis and apoptotic pathways in RB. Notably, upregulation of MCM2, MKI67, PGF, WEE1, CDC20 and downregulation of COX5A were found in all the tumors. Western blot confirmed the dysregulation of identified targets at protein levels as well. These alterations were more prominent in invasive RB, correlating with the disease pathogenesis. Our molecular analysis thus identified the potential therapeutic targets for improving retinoblastoma treatment. We also suggest that PCR array can be used as a tool for rapid and cost-effective gene expression analysis.

A single-cell and spatial RNA-seq database for Alzheimer's disease (ssREAD).

Alzheimer's Disease (AD) pathology has been increasingly explored through single-cell and single-nucleus RNA-sequencing (scRNA-seq & snRNA-seq) and spatial transcriptomics (ST). However, the surge in data demands a comprehensive, user-friendly repository. Addressing this, we introduce a single-cell and spatial RNA-seq database for Alzheimer's disease (ssREAD). It offers a broader spectrum of AD-related datasets, an optimized analytical pipeline, and improved usability. The database encompasses 1,053 samples (277 integrated datasets) from 67 AD-related scRNA-seq & snRNA-seq studies, totaling 7,332,202 cells. Additionally, it archives 381 ST datasets from 18 human and mouse brain studies. Each dataset is annotated with details such as species, gender, brain region, disease/control status, age, and AD Braak stages. ssREAD also provides an analysis suite for cell clustering, identification of differentially expressed and spatially variable genes, cell-type-specific marker genes and regulons, and spot deconvolution for integrative analysis. ssREAD is freely available at https://bmblx.bmi.osumc.edu/ssread/ .

iIMPACT: integrating image and molecular profiles for spatial transcriptomics analysis.

Current clustering analysis of spatial transcriptomics data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, we have developed a multi-stage statistical method called iIMPACT. It identifies and defines histology-based spatial domains based on AI-reconstructed histology images and spatial context of gene expression measurements, and detects domain-specific differentially expressed genes. Through multiple case studies, we demonstrate iIMPACT outperforms existing methods in accuracy and interpretability and provides insights into the cellular spatial organization and landscape of functional genes within spatial transcriptomics data.

Joint effects of CD8A and ICOS in Long QT Syndrome (LQTS) and Beckwith-Wiedemann Syndrome (BWS).

BACKGROUND: Long QT Syndrome (LQTS) and Beckwith-Wiedemann Syndrome (BWS) are complex disorders with unclear origins, underscoring the need for in-depth molecular investigations into their mechanisms. The main aim of this study is to identify the shared key genes between LQTS and BWS, shedding light on potential common molecular pathways underlying these syndromes. METHODS: The LQTS and BWS datasets are available for download from the GEO database. Differential expression genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) was used to detect significant modules and central genes. Gene enrichment analysis was performed. CIBERSORT was used for immune cell infiltration analysis. The predictive protein interaction (PPI) network of core genes was constructed using STRING, and miRNAs regulating central genes were screened using TargetScan. RESULTS: Five hundred DEGs associated with Long QT Syndrome and Beckwith-Wiedemann Syndrome were identified. GSEA analysis revealed enrichment in pathways such as T cell receptor signaling, MAPK signaling, and adrenergic signaling in cardiac myocytes. Immune cell infiltration indicated higher levels of memory B cells and naive CD4 T cells. Four core genes (CD8A, ICOS, CTLA4, LCK) were identified, with CD8A and ICOS showing low expression in the syndromes and high expression in normal samples, suggesting potential inverse regulatory roles. CONCLUSION: The expression of CD8A and ICOS is low in long QT syndrome and Beckwith-Wiedemann syndrome, indicating their potential as key genes in the pathogenesis of these syndromes. The identification of shared key genes between LQTS and BWS provides insights into common molecular mechanisms underlying these disorders, potentially facilitating the development of targeted therapeutic strategies.

A reanalysis and integration of transcriptomics and proteomics datasets unveil novel drug targets for Mekong schistosomiasis.

Schistosomiasis, caused by Schistosoma trematodes, is a significant global health concern, particularly affecting millions in Africa and Southeast Asia. Despite efforts to combat it, the rise of praziquantel (PZQ) resistance underscores the need for new treatment options. Protein kinases (PKs) are vital in cellular signaling and offer potential as drug targets. This study focused on focal adhesion kinase (FAK) as a candidate for anti-schistosomal therapy. Transcriptomic and proteomic analyses of adult S. mekongi worms identified FAK as a promising target due to its upregulation and essential role in cellular processes. Molecular docking simulations assessed the binding energy of FAK inhibitors to Schistosoma FAK versus human FAK. FAK inhibitor 14 and PF-03814735 exhibited strong binding to Schistosoma FAK with minimal binding for human FAK. In vitro assays confirmed significant anti-parasitic activity against S. mekongi, S. mansoni, and S. japonicum, comparable to PZQ, with low toxicity in human cells, indicating potential safety. These findings highlight FAK as a promising target for novel anti-schistosomal therapies. However, further research, including in vivo studies, is necessary to validate efficacy and safety before clinical use. This study offers a hopeful strategy to combat schistosomiasis and reduce its global impact.

An integrated bioinformatic analysis of microarray datasets to identify biomarkers and miRNA-based regulatory networks in leishmaniasis.

Micro RNAs (miRNAs, miRs) and relevant networks might exert crucial functions during differential host cell infection by the different Leishmania species. Thus, a bioinformatic analysis of microarray datasets was developed to identify pivotal shared biomarkers and miRNA-based regulatory networks for Leishmaniasis. A transcriptomic analysis by employing a comprehensive set of gene expression profiling microarrays was conducted to identify the key genes and miRNAs relevant for Leishmania spp. infections. Accordingly, the gene expression profiles of healthy human controls were compared with those of individuals infected with Leishmania mexicana, L. major, L. donovani, and L. braziliensis. The enrichment analysis for datasets was conducted by utilizing EnrichR database, and Protein-Protein Interaction (PPI) network to identify the hub genes. The prognostic value of hub genes was assessed by using receiver operating characteristic (ROC) curves. Finally, the miRNAs that interact with the hub genes were identified using miRTarBase, miRWalk, TargetScan, and miRNet. Differentially expressed genes were identified between the groups compared in this study. These genes were significantly enriched in inflammatory responses, cytokine-mediated signaling pathways and granulocyte and neutrophil chemotaxis responses. The identification of hub genes of recruited datasets suggested that TNF, SOCS3, JUN, TNFAIP3, and CXCL9 may serve as potential infection biomarkers and could deserve value as prognostic biomarkers for leishmaniasis. Additionally, inferred data from miRWalk revealed a significant degree of interaction of a number of miRNAs (hsa-miR-8085, hsa-miR-4673, hsa-miR-4743-3p, hsa-miR-892c-3p, hsa-miR-4644, hsa-miR-671-5p, hsa-miR-7106-5p, hsa-miR-4267, hsa-miR-5196-5p, and hsa-miR-4252) with the majority of the hub genes, suggesting such miRNAs play a crucial role afterwards parasite infection. The hub genes and hub miRNAs identified in this study could be potentially suggested as therapeutic targets or biomarkers for the management of leishmaniasis.

Machine learning survival prediction using tumor lipid metabolism genes for osteosarcoma.

Osteosarcoma is a primary malignant tumor that commonly affects children and adolescents, with a poor prognosis. The existence of tumor heterogeneity leads to different molecular subtypes and survival outcomes. Recently, lipid metabolism has been identified as a critical characteristic of cancer. Therefore, our study aims to identify osteosarcoma's lipid metabolism molecular subtype and develop a signature for survival outcome prediction. Four multicenter cohorts-TARGET-OS, GSE21257, GSE39058, and GSE16091-were amalgamated into a unified Meta-Cohort. Through consensus clustering, novel molecular subtypes within Meta-Cohort patients were delineated. Subsequent feature selection processes, encompassing analyses of differentially expressed genes between subtypes, univariate Cox analysis, and StepAIC, were employed to pinpoint biomarkers related to lipid metabolism in TARGET-OS. We selected the most effective algorithm for constructing a Lipid Metabolism-Related Signature (LMRS) by utilizing four machine-learning algorithms reconfigured into ten unique combinations. This selection was based on achieving the highest concordance index (C-index) in the test cohort of GSE21257, GSE39058, and GSE16091. We identified two distinct lipid metabolism molecular subtypes in osteosarcoma patients, C1 and C2, with significantly different survival rates. C1 is characterized by increased cholesterol, fatty acid synthesis, and ketone metabolism. In contrast, C2 focuses on steroid hormone biosynthesis, arachidonic acid, and glycerolipid and linoleic acid metabolism. Feature selection in the TARGET-OS identified 12 lipid metabolism genes, leading to a model predicting osteosarcoma patient survival. The LMRS, based on the 12 identified genes, consistently accurately predicted prognosis across TARGET-OS, testing cohorts, and Meta-Cohort. Incorporating 12 published signatures, LMRS showed robust and significantly superior predictive capability. Our results offer a promising tool to enhance the clinical management of osteosarcoma, potentially leading to improved clinical outcomes.

HyperGCN: an effective deep representation learning framework for the integrative analysis of spatial transcriptomics data.

BACKGROUND: Advances of spatial transcriptomics technologies enabled simultaneously profiling gene expression and spatial locations of cells from the same tissue. Computational tools and approaches for integration of transcriptomics data and spatial context information are urgently needed to comprehensively explore the underlying structure patterns. In this manuscript, we propose HyperGCN for the integrative analysis of gene expression and spatial information profiled from the same tissue. HyperGCN enables data visualization and clustering, and facilitates downstream analysis, including domain segmentation, the characterization of marker genes for the specific domain structure and GO enrichment analysis. RESULTS: Extensive experiments are implemented on four real datasets from different tissues (including human dorsolateral prefrontal cortex, human positive breast tumors, mouse brain, mouse olfactory bulb tissue and Zabrafish melanoma) and technologies (including 10X visium, osmFISH, seqFISH+, 10X Xenium and Stereo-seq) with different spatial resolutions. The results show that HyperGCN achieves superior clustering performance and produces good domain segmentation effects while identifies biologically meaningful spatial expression patterns. This study provides a flexible framework to analyze spatial transcriptomics data with high geometric complexity. CONCLUSIONS: HyperGCN is an unsupervised method based on hypergraph induced graph convolutional network, where it assumes that there existed disjoint tissues with high geometric complexity, and models the semantic relationship of cells through hypergraph, which better tackles the high-order interactions of cells and levels of noise in spatial transcriptomics data.

Growth behavior and mRNA expression profiling during growth of IPEC-J2 cells.

OBJECTIVE: The IPEC-J2 cell line is used as an in vitro small intestine model for swine, but it is also used as a model for the human intestine, presenting a relatively unique setting. By combining electric cell-substrate impedance sensing, with next-generation-sequencing technology, we showed that mRNA gene expression profiles and related pathways can depend on the growth phase of IPEC-J2 cells. Our investigative approach welcomes scientists to reproduce or modify our protocols and endorses putting their gene expression data in the context of the respective growth phase of the cells. RESULTS: Three time points are presented: (TP1) 1 h after medium change (= 6 h after seeding of cells), (TP2) the time point of the first derivative maximum of the cell growth curve, and a third point at the beginning of the plateau phase (TP3). Significantly outstanding at TP1 compared to TP2 was upregulated PLEKHN1, further FOSB and DEGS2 were significantly downregulated at TP2 compared to TP3. Any provided data can be used to improve next-generation experiments with IPEC-J2 cells.

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer's disease dementia.

BACKGROUND: Transcriptome-wide association study (TWAS) is an influential tool for identifying genes associated with complex diseases whose genetic effects are likely mediated through transcriptome. TWAS utilizes reference genetic and transcriptomic data to estimate effect sizes of genetic variants on gene expression (i.e., effect sizes of a broad sense of expression quantitative trait loci, eQTL). These estimated effect sizes are employed as variant weights in gene-based association tests, facilitating the mapping of risk genes with genome-wide association study (GWAS) data. However, most existing TWAS of Alzheimer's disease (AD) dementia are limited to studying only cis-eQTL proximal to the test gene. To overcome this limitation, we applied the Bayesian Genome-wide TWAS (BGW-TWAS) method to leveraging both cis- and trans- eQTL of brain and blood tissues, in order to enhance mapping risk genes for AD dementia. METHODS: We first applied BGW-TWAS to the Genotype-Tissue Expression (GTEx) V8 dataset to estimate cis- and trans- eQTL effect sizes of the prefrontal cortex, cortex, and whole blood tissues. Estimated eQTL effect sizes were integrated with the summary data of the most recent GWAS of AD dementia to obtain BGW-TWAS (i.e., gene-based association test) p-values of AD dementia per gene per tissue type. Then we used the aggregated Cauchy association test to combine TWAS p-values across three tissues to obtain omnibus TWAS p-values per gene. RESULTS: We identified 85 significant genes in prefrontal cortex, 82 in cortex, and 76 in whole blood that were significantly associated with AD dementia. By combining BGW-TWAS p-values across these three tissues, we obtained 141 significant risk genes including 34 genes primarily due to trans-eQTL and 35 mapped risk genes in GWAS Catalog. With these 141 significant risk genes, we detected functional clusters comprised of both known mapped GWAS risk genes of AD in GWAS Catalog and our identified TWAS risk genes by protein-protein interaction network analysis, as well as several enriched phenotypes related to AD. CONCLUSION: We applied BGW-TWAS and aggregated Cauchy test methods to integrate both cis- and trans- eQTL data of brain and blood tissues with GWAS summary data, identifying 141 TWAS risk genes of AD dementia. These identified risk genes provide novel insights into the underlying biological mechanisms of AD dementia and potential gene targets for therapeutics development.

Investigating molecular markers linked to acute myocardial infarction and cuproptosis: bioinformatics analysis and validation in the AMI mice model.

Cuproptosis-related key genes play a significant role in the pathological processes of acute myocardial infarction (AMI). However, a complete understanding of the molecular mechanisms behind this participation remains elusive. This study was designed to identify genes and immune cells critical to AMI pathogenesis. Based on the GSE48060 dataset (31 AMI patients and 21 healthy persons, GPL570-55999), we identified genes associated with dysregulated cuproptosis and the activation of immune responses between normal subjects and patients with a first myocardial attack. Two molecular clusters associated with cuproptosis were defined in patients with AMI. Immune infiltration analysis showed that there was significant immunity heterogeneity among different clusters. Multiple immune responses were closely associated with Cluster2-specific differentially expressed genes (DEGs). The generalized linear model machine model presented the best discriminative performance with relatively lower residual and root mean square error, and a higher area under the curve (AUC = 0.870). A final two-gene-based generalized linear model was constructed, exhibiting satisfactory performance in two external validation datasets (AUC = 0.719, GSE66360 and AUC = 0.856, GSE123342). Column graph, calibration curve, and decision curve analyses also proved the accuracy of AMI prediction. We also constructed a mouse C57BL/6 model of AMI (3 h, 48 h, and 1 week) and used qRT-PCR and immunofluorescence to detect the expression changes of CBLB and ZNF302. In this study, we present a systematic analysis of the complex relationship between cuproptosis and a first AMI attack, and provide new insights into the diagnosis and treatment of AMI.

Reduced protein-coding transcript diversity in severe dengue emphasises the role of alternative splicing.

Dengue fever, a neglected tropical arboviral disease, has emerged as a global health concern in the past decade. Necessitating a nuanced comprehension of the intricate dynamics of host-virus interactions influencing disease severity, we analysed transcriptomic patterns using bulk RNA-seq from 112 age- and gender-matched NS1 antigen-confirmed hospital-admitted dengue patients with varying severity. Severe cases exhibited reduced platelet count, increased lymphocytosis, and neutropenia, indicating a dysregulated immune response. Using bulk RNA-seq, our analysis revealed a minimal overlap between the differentially expressed gene and transcript isoform, with a distinct expression pattern across the disease severity. Severe patients showed enrichment in retained intron and nonsense-mediated decay transcript biotypes, suggesting altered splicing efficiency. Furthermore, an up-regulated programmed cell death, a haemolytic response, and an impaired interferon and antiviral response at the transcript level were observed. We also identified the potential involvement of the RBM39 gene among others in the innate immune response during dengue viral pathogenesis, warranting further investigation. These findings provide valuable insights into potential therapeutic targets, underscoring the importance of exploring transcriptomic landscapes between different disease sub-phenotypes in infectious diseases.

Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have led to groundbreaking advancements in life sciences. To develop bioinformatics tools for scRNA-seq and SRT data and perform unbiased benchmarks, data simulation has been widely adopted by providing explicit ground truth and generating customized datasets. However, the performance of simulation methods under multiple scenarios has not been comprehensively assessed, making it challenging to choose suitable methods without practical guidelines. RESULTS: We systematically evaluated 49 simulation methods developed for scRNA-seq and/or SRT data in terms of accuracy, functionality, scalability, and usability using 152 reference datasets derived from 24 platforms. SRTsim, scDesign3, ZINB-WaVE, and scDesign2 have the best accuracy performance across various platforms. Unexpectedly, some methods tailored to scRNA-seq data have potential compatibility for simulating SRT data. Lun, SPARSim, and scDesign3-tree outperform other methods under corresponding simulation scenarios. Phenopath, Lun, Simple, and MFA yield high scalability scores but they cannot generate realistic simulated data. Users should consider the trade-offs between method accuracy and scalability (or functionality) when making decisions. Additionally, execution errors are mainly caused by failed parameter estimations and appearance of missing or infinite values in calculations. We provide practical guidelines for method selection, a standard pipeline Simpipe ( https://github.com/duohongrui/simpipe ; https://doi.org/10.5281/zenodo.11178409 ), and an online tool Simsite ( https://www.ciblab.net/software/simshiny/ ) for data simulation. CONCLUSIONS: No method performs best on all criteria, thus a good-yet-not-the-best method is recommended if it solves problems effectively and reasonably. Our comprehensive work provides crucial insights for developers on modeling gene expression data and fosters the simulation process for users.

Identification of hub genes related to metastasis and prognosis of osteosarcoma and establishment of a prognostic model with bioinformatic methods.

Osteosarcoma (OS) is the most common primary malignant bone tumor occurring in children and adolescents. Improvements in our understanding of the OS pathogenesis and metastatic mechanism on the molecular level might lead to notable advances in the treatment and prognosis of OS. Biomarkers related to OS metastasis and prognosis were analyzed and identified, and a prognostic model was established through the integration of bioinformatics tools and datasets in multiple databases. 2 OS datasets were downloaded from the Gene Expression Omnibus database for data consolidation, standardization, batch effect correction, and identification of differentially expressed genes (DEGs); following that, gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the DEGs; the STRING database was subsequently used for protein-protein interaction (PPI) network construction and identification of hub genes; hub gene expression was validated, and survival analysis was conducted through the employment of the TARGET database; finally, a prognostic model was established and evaluated subsequent to the screening of survival-related genes. A total of 701 DEGs were identified; by gene ontology and KEGG pathway enrichment analyses, the overlapping DEGs were enriched for 249 biological process terms, 13 cellular component terms, 35 molecular function terms, and 4 KEGG pathways; 13 hub genes were selected from the PPI network; 6 survival-related genes were identified by the survival analysis; the prognostic model suggested that 4 genes were strongly associated with the prognosis of OS. DEGs related to OS metastasis and survival were identified through bioinformatics analysis, and hub genes were further selected to establish an ideal prognostic model for OS patients. On this basis, 4 protective genes including TPM1, TPM2, TPM3, and TPM4 were yielded by the prognostic model.

Prognostic prediction of patients having classical papillary thyroid carcinoma with a 4 mRNA-based risk model.

The dysregulation of protein-coding genes involved in various biological functions is closely associated with the progression of thyroid cancer. This study aimed to investigate the effects of dysregulated gene expressions on the prognosis of classical papillary thyroid carcinoma (cPTC). Using expression profiling datasets from the Cancer Genome Atlas (TCGA) database, we performed differential expression analysis to identify differentially expressed genes (DEGs). Cox regression and Kaplan-Meier analysis were used to identify DEGs, which were used to construct a risk model to predict the prognosis of cPTC patients. Functional enrichment analysis unveiled the potential significance of co-expressed protein-encoding genes in tumors. We identified 4 DEGs (SALL3, PPBP, MYH1, and SYNDIG1), which were used to construct a risk model to predict the prognosis of cPTC patients. These 4 genes were independent of clinical parameters and could be functional in cPTC carcinogenesis. Furthermore, PPBP exhibited a strong correlation with poorer overall survival (OS) in the advanced stage of the disease. This study suggests that the 4-gene signature could be an independent prognostic biomarker to improve prognosis prediction in cPTC patients older than 46.

First transcriptomic insight into the working muscles of racing pigeons during a competition flight.

BACKGROUND: The currently known homing pigeon is a result of a sharp one-sided selection for flight characteristics focused on speed, endurance, and spatial orientation. This has led to extremely well-adapted athletic phenotypes in racing birds. METHODS: Here, we identify genes and pathways contributing to exercise adaptation in sport pigeons by applying next-generation transcriptome sequencing of m.pectoralis muscle samples, collected before and after a 300 km competition flight. RESULTS: The analysis of differentially expressed genes pictured the central role of pathways involved in fuel selection and muscle maintenance during flight, with a set of genes, in which variations may therefore be exploited for genetic improvement of the racing pigeon population towards specific categories of competition flights. CONCLUSIONS: The presented results are a background to understanding the genetic processes in the muscles of birds during flight and also are the starting point of further selection of genetic markers associated with racing performance in carrier pigeons.

Disregarding multimappers leads to biases in the functional assessment of NGS data.

BACKGROUND: Standard ChIP-seq and RNA-seq processing pipelines typically disregard sequencing reads whose origin is ambiguous ("multimappers"). This usual practice has potentially important consequences for the functional interpretation of the data: genomic elements belonging to clusters composed of highly similar members are left unexplored. RESULTS: In particular, disregarding multimappers leads to the underrepresentation in epigenetic studies of recently active transposable elements, such as AluYa5, L1HS and SVAs. Furthermore, this common strategy also has implications for transcriptomic analysis: members of repetitive gene families, such the ones including major histocompatibility complex (MHC) class I and II genes, are under-quantified. CONCLUSION: Revealing inherent biases that permeate routine tasks such as functional enrichment analysis, our results underscore the urgency of broadly adopting multimapper-aware bioinformatic pipelines -currently restricted to specific contexts or communities- to ensure the reliability of genomic and transcriptomic studies.