Gene Expression Profiling Data Research Articles

Deepgmd: A Graph-Neural-Network-Based Method to Detect Gene Regulator Module.

Regulatory module mining methods divide genes into multiple gene subgroups and explore potential biological mechanisms from omics data. By transforming gene expression profile data into gene co-expression network, we transform the task of gene module detection into the problem of finding community structure in the graph, and introduce the latest network representation learning method-graph neural network to optimize this problem. In order to systematically evaluate whether the algorithm allows overlap to affect such problems, we make two variants of the output of the algorithm, Deepgmd_cluster and Deepgmd. The difference between them is whether overlap is allowed. By comparing the known modules and the modules generated by the algorithm, we can evaluate the quality of the algorithm. We use this method to compare our algorithm with some current mainstream methods. The results show that our method has greater advantages. In the end, we analyze some typical modules from the modules found by the algorithm for visualization, and use the GO database and KEGG database to perform enrichment analysis and pathway analysis on these modules.

Identification of The Unique Subtype of Macrophages in Aneurysm Lesions at the Growth Phase

ObjectivesRecent experimental studies have defined intracranial aneurysms as a macrophage-mediated chronic inflammatory disease affecting intracranial arteries. Although there are various subtypes in macrophages, what type of macrophages is present in lesions during the disease development remains to be elucidated. MethodsThe previously-established aneurysm model of rats was used. Macrophages were labeled with the fluorescent protein and isolated by a laser-microdissection method. The comprehensive gene expression profile analyses and gene ontology analyses was then done to identify a macrophage subtype present in lesions at the growth phase. ResultsThe gene expression profile data of total 52 macrophages infiltrating into the lesions was acquired. The principal component analysis revealed the monotonous macrophage subtype. By comparing the profile identified with one from in vitro-differentiated M0 or M1 macrophages, the macrophages in the lesions were belonged to the simple and unique subtype. Because the perception of signaling from nervous system was highlighted as up-represented terms through gene ontology analyses, the macrophage subtype in lesions at the growth phase might be differentiated under the influence of nervous system in the microenvironment. The histopathological examinations supported the above notion by confirming the presence of nerves in the adventitia. ConclusionsThe findings from the present study have provided the useful insights about the macrophage subtype in aneurysm lesions at the growth phase and also proposed its ability as a therapeutic target.

CCR7-mediated T follicular helper cell differentiation is associated with the pathogenesis and immune microenvironment of spinal cord injury-induced immune deficiency syndrome.

Spinal cord injury-induced immune deficiency syndrome (SCI-IDS) is a disorder characterized by systemic immunosuppression secondary to SCI that dramatically increases the likelihood of infection and is difficult to treat. T follicular helper (Tfh) cells regulated by chemokine receptor CCR7 are associated with SCI-IDS after acute SCI. The present study explored the roles of CCR7 in SCI-IDS occurrence and immune microenvironment composition. Gene expression profile data of peripheral blood leukocytes from SCI and non-SCI subjects were collected from the Gene Expression Omnibus database. According to differential gene expression analysis, a protein-protein interaction (PPI) network, and risk model construction, the CCR7 expression level was prominently related to acute SCI and CCR7 expression was significantly downregulated after acute SCI. Next, we constructed a clinical prediction model and used it to identify patients with acute SCI. Using Gene Ontology (GO) analysis and gene set enrichment analysis (GSEA), we discovered that immune-related biological processes, such as T cell receptor signaling pathway, were suppressed, whereas chemokine-related signaling pathways were activated after acute SCI. Immune infiltration analysis performed using single sample GSEA and CIBERSORT suggested that Tfh cell function was significantly correlated with the CCR7 expression levels and was considerably reduced after acute SCI. Acute SCI was divided into two subtypes, and we integrated multiple classifiers to analyze and elucidate the immunomodulatory relationships in both subtypes jointly. The results suggested that CCR7 suppresses the immunodeficiency phenotype by activating the chemokine signaling pathway in Tfh cells. In conclusion, CCR7 exhibits potential as a diagnostic marker for acute SCI.

Phyto-Friendly Soil Bacteria and Fungi Provide Beneficial Outcomes in the Host Plant by Differently Modulating Its Responses through (In)Direct Mechanisms.

Sustainable agricultural systems based on the application of phyto-friendly bacteria and fungi are increasingly needed to preserve soil fertility and microbial biodiversity, as well as to reduce the use of chemical fertilizers and pesticides. Although there is considerable attention on the potential applications of microbial consortia as biofertilizers and biocontrol agents for crop management, knowledge on the molecular responses modulated in host plants because of these beneficial associations is still incomplete. This review provides an up-to-date overview of the different mechanisms of action triggered by plant-growth-promoting microorganisms (PGPMs) to promote host-plant growth and improve its defense system. In addition, we combined available gene-expression profiling data from tomato roots sampled in the early stages of interaction with Pseudomonas or Trichoderma strains to develop an integrated model that describes the common processes activated by both PGPMs and highlights the host’s different responses to the two microorganisms. All the information gathered will help define new strategies for the selection of crop varieties with a better ability to benefit from the elicitation of microbial inoculants.

Identification of hub genes for early detection of bone metastasis in breast cancer.

BackgroundGlobally, among all women, the most frequently detected and diagnosed and the most lethal type of cancer is breast cancer (BC). In particular, bone is one of the most frequent distant metastases 24in breast cancer patients and bone metastasis arises in approximately 80% of advanced patients. Thus, we need to identify and validate early detection markers that can differentiate metastasis from non-metastasis breast cancers.MethodsGSE55715, GSE103357, and GSE146661 gene expression profiling data were downloaded from the GEO database. There was 14 breast cancer with bone metastasis samples and 8 breast cancer tissue samples. GEO2R was used to screen for differentially expressed genes (DEGs). The volcano plots, Venn diagrams, and annular heatmap were generated by using the ggplot2 package. By using the cluster Profiler R package, KEGG and GO enrichment analyses of DEGs were conducted. Through PPI network construction using the STRING database, key hub genes were identified by cytoHubba. Finally, K-M survival and ROC curves were generated to validate hub gene expression.ResultsBy GO enrichment analysis, 143 DEGs were enriched in the following GO terms: extracellular structure organization, extracellular matrix organization, leukocyte migration class II protein complex, collagen tridermic protein complex, extracellular matrix structural constituent, growth factor binding, and platelet-derived growth factor binding. In the KEGG pathway enrichment analysis, DEGs were enriched in Staphylococcus aureus infection, Complement and coagulation cascades, and Asthma. By PPI network analysis, we selected the top 10 genes, including SLCO2B1, STAB1, SERPING1, HLA-DOA, AIF1, GIMAP4, C1orf162, HLA-DMB, ADAP2, and HAVCR2. By using TCGA and THPA databases, we validated 2 genes, SERPING1 and GIMAP4, that were related to the early detection of bone metastasis in BC.Conclusions2 abnormally expressed hub genes could play a pivotal role in the breast cancer with bone metastasis by affecting bone homeostasis imbalance in the bone microenvironment.

Disease Markers and Therapeutic Targets for Rheumatoid Arthritis Identified by Integrating Bioinformatics Analysis with Virtual Screening of Traditional Chinese Medicine.

The aim of this study was to identify potentially important Rheumatoid arthritis (RA) targets related to immune cells based on bioinformatics analysis, and to identify small molecules of traditional Chinese medicine (TCM) associated with these targets that have potential therapeutic effects on RA. Gene expression profile data related to RA were downloaded from the Gene Expression Omnibus (GSE55235, GSE55457, and GSE77298), and datasets were merged by the batch effect removal method. The RA key gene set was identified by protein-protein interaction network analysis and machine learning-based feature extraction. Furthermore, immune cell infiltration analysis was carried out on all DEGs to obtain key RA markers related to immune cells. Batch molecular docking of key RA markers was performed on our previously compiled dataset of small molecules in TCM using AutoDock Vina. Moreover, in vitro experiments were performed to examine the inhibitory effect of screened compounds on the synovial cells of an RA rat model. The PPI network and feature extraction with machine learning classifiers identified eight common key RA genes: MYH11, CFP, LY96, IGJ, LPL, CD48, RAC2, and CSK. RAC2 was significantly correlated with the infiltration and expression of five immune cells, with significant differences in these immune cells in the normal and RA samples. Molecular docking and in vitro experiments also showed that sanguinarine, sesamin, and honokiol could effectively inhibit the proliferation of RA rat synovial cells, also could all effectively inhibit the secretion of TNF-α and IL-1β in synovial cells, and had a certain inhibitory effect on expression of the target protein RAC2. The core gene set of RA was screened from a new perspective, revealing biomarkers related to immune cell infiltration. Using molecular docking, we screened out TCM small molecules for the treatment of RA, providing methods and technical support for the treatment of RA with TCM.

Mining Potential Drug Targets and Constructing Diagnostic Models for Heart Failure Based on miRNA-mRNA Networks.

Heart failure (HF) is a globally prevalent cardiovascular disease, but effective drug targets and diagnostic models are still lacking. This study was designed to investigate effective drug targets and diagnostic models for HF in terms of miRNA targets, hoping to contribute to the understanding and treatment of HF. Using HF miRNA and gene expression profile data from the GEO database, we analyzed differentially expressed miRNAs/gene identification in HF using Limma and predicted miRNA targets by the online TargetScan database. Subsequently, gene set enrichment analysis and annotation were performed using WebGestaltR package. Protein-protein interactions were identified using the STRING database. The proximity of drugs to treat HF was also calculated and predicted for potential target therapeutic drug. In addition, further drug identification was performed by molecular docking. Finally, diagnostic models were constructed based on differential miRNAs. The GEO dataset was used to screen 66 differentially expressed miRNAs, incorporating 56 downregulated miRNAs and 10 upregulated miRNAs. The JAK-STAT signaling pathway, MAPK signaling pathway, p53 signaling pathway, Prolactin signaling pathway, and TGF-beta signaling pathway were enriched, as shown by KEGG enrichment analysis on the target genes. In addition, we found that 83 genes were upregulated and 92 genes were downregulated in HF patients vs. healthy individuals. Based on the inflammation-related score, hypoxia-related score, and energy metabolism-related score, we identified key miRNA-mRNA pairs and constructed an interaction network. Following that, TAP1, which had the highest expression and network connectivity in acute HF with crystal and molecular docking studies, was selected as a key candidate gene in the network. And the compound DB04847 was selected to produce a large number of favorable interactions with TAP1 protein. Finally, we constructed two diagnostic models based on the differential miRNAs hsa-miR-6785-5p and hsa-miR-4443. In conclusion, we identified TAP1, a key candidate gene in the diagnosis and treatment of HF, and determined that compound DB04847 is highly likely to be a potential inhibitor of TAP1. The TAP1 gene was also found to be regulated by hsa-miR-6785-5p and hsa-miR-4443, and a diagnostic model was constructed. This provides a new promising direction to improve the diagnosis, prognosis, and treatment outcome and guide more effective immunotherapy strategies of HF.

328. MOLECULAR SUBTYPING OF ESOPHAGEAL ADENOCARCINOMA BY NON-NEGATIVE MATRIX FACTORIZATION OF LASER CAPTURE MICRODISSECTED RNA-SEQ SAMPLES

Abstract Previous attempts to construct prognostic molecular subtypes for esophageal adenocarcinoma (EAC) using gene expression profiling has met with little success when compared to adenocarcinomas from other disease sites. We hypothesized that this is in part due to the low tumour cellularity obtained from EAC biopsy and resection specimens, which obscures the tumour-intrinsic gene expression signals due to normal cell contamination. We systematically collected biopsies from pre-treatment, post-induction, and metastatic EAC tumors. Laser capture microdissection (LCM) was utilized to enrich for tumour cells for whole transcriptome sequencing (RNA-seq) (N = 54). We utilized non-negative matrix factorization (NMF) with 7 components to identify common gene expression programs across our samples. After applying NMF to our samples, we identified 4 tumour-intrinsic components and 3 tumour-extrinsic components (including immune infiltrate and normal esophagus). These components validated on TCGA EAC and esophageal squamous cell carcinoma RNA-seq, single cell RNA-seq from EAC organoids, and the cancer cell line encyclopedia. Moreover, we observed that these tumour-intrinsic gene expression programs were preserved in EAC samples metastatic to other organ sites such as liver and lymph nodes. We observed that most tumours expressed more than one tumour component and that approximately 25% of our samples had low tumour signals despite LCM enrichment. We used LCM to enrich for tumour signal in EAC gene expression profiling data and identified 4 tumour-intrinsic gene expression programs that validate across multiple datasets. We have currently conducting survival analysis on our data and TCGA data to further validate the prognostic potential of our tumour signatures.

Toxic effects of long-term pesticides exposure and key gene discovery

Objective: Bioinformatics methods were used to mine differentially expressed genes (DEGs) and enriched signal pathways induced by chronic pesticide exposure, and explore its potential pathogenic mechanisms and key genes. Methods: In July 2021, high-throughput gene expression profile data related to pesticide toxicity was downloaded from Gene Expression Omnibus (GEO) database to obtain DEGs. The samples were from American male farm workers who had been exposed to pesticides for a long time and other industry workers. The functional enrichment analysis of GO, KEGG and Geme Set Enrichment Analysis (GSEA) were performed by R package clusterProfiler. STRING, Cytoscape and other tools were applied to construct and visualize the protein interaction network. With the help of MCODE and Cytohubba plugins, gene function modules were obtained, and hub gene was screened out. Results: 189 DEGs were screened from GSE30335 dataset, including 101 up-regulated genes and 88 down-regulated genes. The results of GO, KEGG and GSEA were mainly enriched in biological functions such as regulation of neuron projection development, regulation of locomotion, ribosomal protein synthesis, and pathways related to complex nervous system diseases such as Parkinson's disease. And the comprehensive screening showed that KLF1 was the hub gene of pesticide exposure, with a fold change of 0.456 (t=-3.82, P=0.021) . Conclusion: Long term exposure to pesticides results in the differential expression of multiple genes in the exposed population, which may be involved in the pathological changes of nervous system by down regulating KLF1 and related biological pathways.

Improving diagnosis accuracy of non-small cell lung carcinoma on noisy data by adaptive group lasso regularized multinomial regression

Non-small cell lung carcinoma (NSCLC) accounts for 80–85% of all lung cancers, ranking first in the cause of death of malignant tumors. Early and accurate diagnosis of NSCLC is crucial for the follow-up treatment, which is helpful in improving the survival time and quality of life of patients. Machine learning methods based on gene expression profile data provide a new way for the early diagnosis of NSCLC. However, there are a few difficulties in practical application: data noise, overlapping gene grouping, and the importance evaluations for gene groups and individual genes. This paper aimed to deal with these problems by developing an adaptive group lasso regularized multinomial regression (AGLRMR). Robust principal component analysis (RPCA) was presented to separate clean data from noisy gene expression profile data of NSCLC. Weighted gene co-expression network analysis (WGCNA) was adopted to perform overlapping gene grouping. The importance evaluation criteria for individual genes and gene groups were proposed by fusing module membership, information theory, and symmetric uncertainty. Compared with RAMRSGL, AMRSOGL, MSGL, MRGL, and MR-lasso, AGLRMR improved the diagnosis accuracy of NSCLC by 1.2%, 2.3%, 2.2%, 2.5%, and 4.0% on NSCLC-1 dataset, and by 1.1%, 8.7%, 6.2%, 7.1%, and 16.3% on NSCLC-2 dataset, respectively. The operation speed of AGLRMR is consistent with AMRSOGL and slower than the other four methods. Biological analysis showed that the genes selected by AGLRMR were highly correlated with NSCLC.

Comparative transcriptomic analysis identifies KcMYB1 as a R2R3-MYB anthocyanin activator in Kadsura coccinea

Fruit color, as an important appearance attribute, is crucial for attracting consumers. However, the underlying mechanism regulating mature fruit color formation in Kadsura coccinea remains unclear. Here, a comprehensive metabolomics and transcriptomics analysis was performed to investigate the molecular mechanisms of anthocyanin accumulation between two K. coccinea cultivars with different mature fruit colors—‘Dahong No. 1′ (red) and ‘Jinhu’ (yellow). Targeted metabolomic analysis revealed high anthocyanin levels, most of which were cyanidin and delphinidin derivatives, in ‘Dahong No. 1′ mature fruit peel. The SNP analysis indicated that the two different cultivars had similar genetic background. Moreover, comparative transcriptomic analysis demonstrated that differentially expressed genes (DEGs) were related to flavonoid biosynthesis and metabolic process in the two K. coccinea cultivars. Gene expression profiling data showed that the structural and regulatory genes associated with anthocyanin biosynthesis were significantly upregulated in ‘Dahong No. 1′ mature fruit peel, which was verified by quantitative real-time polymerase chain reaction (qRT-PCR). Notably, the key anthocyanin activator KcMYB1 was identified, which was significantly upregulated in ‘Dahong No. 1′ compared with ‘Jinhu’. We further confirmed that KcMYB1 actively regulated the accumulation of anthocyanin by ectopic expression in vivo. Furthermore, allelic constitution of KcMYB1 in K. coccinea were investigated. The present study can provide insights for understanding the regulatory mechanisms of anthocyanin differential accumulation in the mature fruits of K. coccinea.

Dysregulation and imbalance of innate and adaptive immunity are involved in the cardiomyopathy progression.

BackgroundCardiomyopathy is known to be a heterogeneous disease with numerous etiologies. They all have varying degrees and types of myocardial pathological changes, resulting in impaired contractility, ventricle relaxation, and heart failure. The purpose of this study was to determine the pathogenesis, immune-related pathways and important biomarkers engaged in the progression of cardiomyopathy from various etiologies.MethodsWe downloaded the gene microarray data from the Gene Expression Omnibus (GEO). The hub genes between cardiomyopathy and non-cardiomyopathy control groups were identified using differential expression analysis, least absolute shrinkage and selection operator (LASSO) regression and weighted gene co-expression network analysis (WGCNA). To assess the diagnostic precision of hub genes, receiver-operating characteristic (ROC) curves as well as the area under the ROC curve (AUC) were utilized. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis and Gene Ontology (GO) analysis were conducted on the obtained differential genes. Finally, single-sample GSEA (ssGSEA) and Gene Set Enrichment Analysis (GSEA) were utilized to analyze the infiltration level of 28 immune cells and their relationship with hub genes based on gene expression profile data and all differential gene files.ResultsA total of 82 differentially expressed genes (DEGs) were screened after the training datasets were merged and intersected. The WGCNA analysis clustered the expression profile data into four co-expression modules, The turquoise module exhibited the strongest relationship with clinical traits, and nine candidate key genes were obtained from the module. Then we intersected DEGs with nine candidate genes. LASSO regression analysis identified the last three hub genes as promising biomarkers to distinguish the cardiomyopathy group from the non-cardiomyopathy control group. ROC curve analysis in the validation dataset revealed the sensitivity and accuracy of three hub genes as marker genes. The majority of the functional enrichment analysis results were concentrated on immunological and inflammatory pathways. Immune infiltration analysis revealed a significant correlation between regulatory T cells, type I helper T cells, macrophages, myeloid-derived suppressor cells, natural killer cells, activated dendritic cells and the abundance of immune infiltration in hub genes.ConclusionThe hub genes (CD14, CCL2, and SERPINA3) can be used as markers to distinguish cardiomyopathy from non-cardiomyopathy individuals. Among them, SERPINA3 has the best diagnostic performance. T cell immunity (adaptive immune response) is closely linked to cardiomyopathy progression. Hub genes may protect the myocardium from injury through myeloid-derived suppressor cells, regulatory T cells, helper T cells, monocytes/macrophages, natural killer cells and activated dendritic cells. The innate immune response is crucial to this process. Dysregulation and imbalance of innate immune cells or activation of adaptive immune responses are involved in cardiomyopathy disease progression in patients.

Ibrutinib and rituximab plus cyclophosphamide, doxorubicin, vincristine and prednisone in patients with previously untreated non-germinal centre B-cell-like diffuse large B-cell lymphoma: A Chinese subgroup analysis of the phase III PHOENIX trial.

In this post hoc subgroup analysis of 200 patients enrolled in China from the phase III PHOENIX trial (N=838, NCT01855750), addition of ibrutinib to rituximab plus cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) did not improve event-free survival (EFS) versus placebo+R-CHOP in the intent-to-treat (ITT; n=200, hazard ratio [HR]=0.83, 95% confidence interval [CI]: 0·509-1.349; p=0.4495) or activated B-cell-like (ABC; n=141 [based on available gene-expression profiling data], HR=0.86, 95% CI: 0.467-1.570; p=0.6160) subpopulations. However, ibrutinib+R-CHOP improved EFS (HR=0·50, 95% CI: 0.251-1.003) and progression-free survival (PFS; HR=0.48, 95% CI: 0.228-1.009) versus placebo+R-CHOP in patients aged<60 but not ≥60years. Grade ≥3 serious treatment-emergent adverse events occurred more with ibrutinib+R-CHOP (45·6% vs. 31·3%). The percentage of patients receiving ≥6 cycles of R-CHOP was similar across treatment arms in those<60years. A numerical trend was seen towards improved EFS and PFS with ibrutinib+R-CHOP versus placebo+R-CHOP in patients with MYC-high/BCL2-high co-expression. In this slightly younger Chinese subgroup, ibrutinib+R-CHOP did not improve EFS in the ITT and ABC subpopulations but improved outcomes with manageable safety in patients<60years, consistent with overall PHOENIX study outcomes.

Qu-Du-San-Jie decoction induces growth inhibition and vascular normalization in NF2-associated vestibular schwannoma.

Background: Neurofibromatosis type 2 (NF2) is a rare genetic syndrome that predisposes individuals to develop bilateral vestibular schwannomas (VSs) causing a high risk of life-threatening neurological complications. Traditional treatment options for NF2-associated VS usually cause neurological damage, and to date, there are no FDA-approved pharmacotherapies for NF2. The aim of this study was to evaluate the antitumor efficacy of Qu-Du-San-Jie (QDSJ) decoction, a traditional Chinese medicine formula, on NF2-associated VS and to investigate the potential underlying mechanisms. Methods: Ultra high-performance liquid chromatography-mass spectroscopy (UHPLC-MS) analysis was performed to identify the components of QDSJ and their targets. To determine the relationships between the putative targets of QDSJ and the differential genes of NF2-associated VS, the drug-disease crossover genes were screened using the UHPLC-MS data combined with our previous gene expression profiling data. The differentially expressed genes were imported into the STRING database to generate a PPI network. Differentially expressed gene targets and pathways were identified using GO and KEGG pathway enrichment analyses. The in vitro and in vivo drug efficacy of QDSJ decoction was tested using a patient-derived schwannoma cell line and a patient-derived xenograft mouse model, respectively. H&E staining, immunochemistry, and immunofluorescence staining were used to evaluate the cell proliferation and tumor vessels. Results: A total of 133 compounds were identified in QDSJ decoction using UHPLC-MS analysis. Network pharmacology showed that the regulation of necroptosis, apoptosis, cell cycle, angiogenesis, adherens junction, and neuroactive ligand-receptor interaction could be associated with the efficacy of QDSJ in treating NF2-associated VS. Treatment with QDSJ induced necrotic cell death and apoptosis of schwannoma cells in vitro and suppressed the tumor growth in vivo. Histopathological analysis revealed areas of cell necrosis and enlarged tumor blood vessels in the QDSJ-treated tumors. The numbers of cells positive for Cyclin D1 and Ki-67 were significantly reduced in QDSJ-treated tumors compared to control tumors. Immunofluorescence staining of CD31 and αSMA showed a decreased number and density of tumor vessels and normalized vessel structure in QDSJ-treated tumors. Conclusion: Our study demonstrates that QDSJ decoction shows significant antitumor activity against NF2-associated schwannoma and is a possible candidate for future clinical trials.

Robust biomarker screening from gene expression data by stable machine learning-recursive feature elimination methods

Recently, identifying robust biomarkers or signatures from gene expression profiling data has attracted much attention in computational biomedicine. The successful discovery of biomarkers for complex diseases such as spontaneous preterm birth (SPTB) and high-grade serous ovarian cancer (HGSOC) will be beneficial to reduce the risk of preterm birth and ovarian cancer among women for early detection and intervention. In this paper, we propose a stable machine learning-recursive feature elimination (StabML-RFE for short) strategy for screening robust biomarkers from high-throughput gene expression data. We employ eight popular machine learning methods, namely AdaBoost (AB), Decision Tree (DT), Gradient Boosted Decision Trees (GBDT), Naive Bayes (NB), Neural Network (NNET), Random Forest (RF), Support Vector Machine (SVM) and XGBoost (XGB), to train on all feature genes of training data, apply recursive feature elimination (RFE) to remove the least important features sequentially, and obtain eight gene subsets with feature importance ranking. Then we select the top-ranking features in each ranked subset as the optimal feature subset. We establish a stability metric aggregated with classification performance on test data to assess the robustness of the eight different feature selection techniques. Finally, StabML-RFE chooses the high-frequent features in the subsets of the combination with maximum stability value as robust biomarkers. Particularly, we verify the screened biomarkers not only via internal validation, functional enrichment analysis and literature check, but also via external validation on two real-world SPTB and HGSOC datasets respectively. Obviously, the proposed StabML-RFE biomarker discovery pipeline easily serves as a model for identifying diagnostic biomarkers for other complex diseases from omics data. The source code and data can be found at https://github.com/zpliulab/StabML-RFE.

Transcriptomic Analysis Reveals Potential Gene Regulatory Networks Under Cold Stress of Loquat (Eriobotrya japonica Lindl.).

Loquat (Eriobotrya japonica Lindl. ) is one of the most economically important evergreen fruit crops in China, while it often suffered the injury of cold stress in winter and earlier spring, and the annual yield loss of loquat fruits caused by cold or freezing stress was immeasurable. However, knowledge about the physiological response and molecular mechanism under cold stress is still limited. To investigate the potential regulation mechanism pre- and post-cold stress in loquat and the changes in physiological indicators, a comparative transcriptome analysis was performed against a cold-resistant cv. “Huoju” and a cold-sensitive cv. “Ninghaibai”. The results of physiological indicators related to cold resistance indicated that rachis was most sensitive to cold stress and was considered as the representative organ to directly evaluate cold resistance of loquat based on subordinate function analysis. Here, we compared the transcriptome profiles of rachis pre- and under cold stress in “Huoju” and “Ninghaibai”. A total of 4,347 and 3,513 differentially expressed genes (DEGs) were detected in “Ninghaibai” and “Huoju”, among which 223 and 166 were newly identified genes, respectively, most of them were functionally enriched in plant hormone signal transduction (Huoju: 142; Ninghaibai: 200), and there were higher plant hormone content and related DEG expression levels in “Huoju” than that of “Ninghaibai”. Moreover, a total of 3,309 differentially expressed transcription factors (DETFs) were identified, and some DEGs and DETFs were screened to be subjected to co-expression network analysis based on the gene expression profile data. Some candidate DEGs, including UDP-glycosyltransferase (UGT), glycosyltransferase (GT), sugar phosphate/phosphate translocator (SPT), sugar transport protein (STP), proline-rich receptor-like protein kinase (PERK), and peroxidise (POD), were significantly affected by cold stress, and the expression level of these genes obtained from real-time quantitative RT-PCR was consistent with the pattern of transcriptome profile, which suggested that these genes might play the vital roles in cold resistance of loquat. Our results provide an invaluable resource for the identification of specific genes and TFs and help to clarify gene transcription during the cold stress response of loquat.

Transcriptome profile of spleen tissues from locally-adapted Kenyan pigs (Sus scrofa) experimentally infected with three varying doses of a highly virulent African swine fever virus genotype IX isolate: Ken12/busia.1 (ken-1033)

BackgroundAfrican swine fever (ASF) is a lethal hemorrhagic disease affecting domestic pigs resulting in up to 100% mortality rates caused by the ASF virus (ASFV). The locally-adapted pigs in South-western Kenya have been reported to be resilient to disease and harsh climatic conditions and tolerate ASF; however, the mechanisms by which this tolerance is sustained remain largely unknown. We evaluated the gene expression patterns in spleen tissues of these locally-adapted pigs in response to varying infective doses of ASFV to elucidate the virus-host interaction dynamics.MethodsLocally adapted pigs (n = 14) were experimentally infected with a high dose (1x106HAD50), medium dose (1x104HAD50), and low dose (1x102HAD50) of the highly virulent genotype IX ASFV Ken12/busia.1 (Ken-1033) isolate diluted in PBS and followed through the course of infection for 29 days. The in vivo pig host and ASFV pathogen gene expression in spleen tissues from 10 pigs (including three from each infective group and one uninfected control) were analyzed in a dual-RNASeq fashion. We compared gene expression between three varying doses in the host and pathogen by contrasting experiment groups against the naïve control.ResultsA total of 4954 differentially expressed genes (DEGs) were detected after ASFV Ken12/1 infection, including 3055, 1771, and 128 DEGs in the high, medium, and low doses, respectively. Gene ontology and KEGG pathway analysis showed that the DEGs were enriched for genes involved in the innate immune response, inflammatory response, autophagy, and apoptosis in lethal dose groups. The surviving low dose group suppressed genes in pathways of physiopathological importance. We found a strong association between severe ASF pathogenesis in the high and medium dose groups with upregulation of proinflammatory cytokines and immunomodulation of cytokine expression possibly induced by overproduction of prostaglandin E synthase (4-fold; p < 0.05) or through downregulation of expression of M1-activating receptors, signal transductors, and transcription factors. The host-pathogen interaction resulted in induction of expression of immune-suppressive cytokines (IL-27), inactivation of autophagy and apoptosis through up-regulation of NUPR1 [5.7-fold (high dose) and 5.1-fold (medium dose) [p < 0.05] and IL7R expression. We detected repression of genes involved in MHC class II antigen processing and presentation, such as cathepsins, SLA-DQB1, SLA-DOB, SLA-DMB, SLA-DRA, and SLA-DQA in the medium and high dose groups. Additionally, the host-pathogen interaction activated the CD8+ cytotoxicity and neutrophil machinery by increasing the expression of neutrophils/CD8+ T effector cell-recruiting chemokines (CCL2, CXCL2, CXCL10, CCL23, CCL4, CXCL8, and CXCL13) in the lethal high and medium dose groups. The recovered pigs infected with ASFV at a low dose significantly repressed the expression of CXCL10, averting induction of T lymphocyte apoptosis and FUNDC1 that suppressed neutrophilia.ConclusionsWe provide the first in vivo gene expression profile data from locally-adapted pigs from south-western Kenya following experimental infection with a highly virulent ASFV genotype IX isolate at varying doses that mimic acute and mild disease. Our study showed that the locally-adapted pigs induced the expression of genes associated with tolerance to infection and repression of genes involved in inflammation at varying levels depending upon the ASFV dose administered.

Identifying potential prognosis markers in hepatocellular carcinoma via integrated bioinformatics analysis and biological experiments.

Background: Hepatocellular carcinoma is one kind of clinical common malignant tumor with a poor prognosis, and its pathogenesis remains to be clarified urgently. This study was performed to elucidate key genes involving HCC by bioinformatics analysis and experimental evaluation. Methods: We identified common differentially expressed genes (DEGs) based on gene expression profile data of GSE60502 and GSE84402 from the Gene Expression Omnibus (GEO) database. Gene Ontology enrichment analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, REACTOME pathway enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were used to analyze functions of these genes. The protein-protein interaction (PPI) network was constructed using Cytoscape software based on the STRING database, and Molecular Complex Detection (MCODE) was used to pick out two significant modules. Hub genes, screened by the CytoHubba plug-in, were validated by Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas (HPA) database. Then, the correlation between hub genes expression and immune cell infiltration was evaluated by Tumor IMmune Estimation Resource (TIMER) database, and the prognostic values were analyzed by Kaplan-Meier plotter. Finally, biological experiments were performed to illustrate the functions of RRM2. Results: Through integrated bioinformatics analysis, we found that the upregulated DEGs were related to cell cycle and cell division, while the downregulated DEGs were associated with various metabolic processes and complement cascade. RRM2, MAD2L1, MELK, NCAPG, and ASPM, selected as hub genes, were all correlated with poor overall prognosis in HCC. The novel RRM2 inhibitor osalmid had anti-tumor activity, including inhibiting proliferation and migration, promoting cell apoptosis, blocking cell cycle, and inducing DNA damage of HCC cells. Conclusion: The critical pathways and hub genes in HCC progression were screened out, and targeting RRM2 contributed to developing new therapeutic strategies for HCC.

Identification of prognostic genes in oral squamous cell carcinoma microenvironment.

Numerous studies reveal the clinical significance of tumor microenvironment (TME) in multiple cancers. The association between TME in oral squamous cell carcinoma (OSCC) and clinical outcomes remains unsolved. This study aims to exhibit the TME of OSCC and identified the prognostic marker. Gene expression profile and clinical data OSCC patients were from the TCGA database. The validated stage data was from the Gene Expression Omnibus (GSE65858). Immune/stromal scores of each patient were calculated by ESTIMATE algorithm. Biological functional prediction was conducted. Prognostic genes identified by survival analysis. Nomogram and Receiver operating characteristic curves were employed to test the predicting power. TIMER database was applied to evaluated the immune infiltrates. Lower immune scores were observed in male patients (P= 0.0107) and different primary tumor sites of oral cavity with different stromal scores (P= 0.0328). The Differentially expressed genes (DEGs) were involved in immune related pathways. HGF gene (hepatocyte growth factor) was prognostic related and with a better prognostic performance when combined with clinical features (AUC=TCGA 0.638, AUC=GEO 0.714). HGF was significantly related with B cell, CD4ï⁢¼⁢T cell, CD8+T cell, macrophage, neutrophils, and dendritic cell infiltration. The current study analyzed the TME and presented immune related prognostic biomarkers for OSCC.

Gene Expression Signatures for the Accurate Diagnosis of Peripheral T-Cell Lymphoma Entities in the Routine Clinical Practice.

Peripheral T-cell lymphoma (PTCL) includes heterogeneous clinicopathologic entities with numerous diagnostic and treatment challenges. We previously defined robust transcriptomic signatures that distinguish common PTCL entities and identified two novel biologic and prognostic PTCL-not otherwise specified subtypes (PTCL-TBX21 and PTCL-GATA3). We aimed to consolidate a gene expression-based subclassification using formalin-fixed, paraffin-embedded (FFPE) tissues to improve the accuracy and precision in PTCL diagnosis. We assembled a well-characterized PTCL training cohort (n = 105) with gene expression profiling data to derive a diagnostic signature using fresh-frozen tissue on the HG-U133plus2.0 platform (Affymetrix, Inc, Santa Clara, CA) subsequently validated using matched FFPE tissues in a digital gene expression profiling platform (nCounter, NanoString Technologies, Inc, Seattle, WA). Statistical filtering approaches were applied to refine the transcriptomic signatures and then validated in another PTCL cohort (n = 140) with rigorous pathology review and ancillary assays. In the training cohort, the refined transcriptomic classifier in FFPE tissues showed high sensitivity (> 80%), specificity (> 95%), and accuracy (> 94%) for PTCL subclassification compared with the fresh-frozen-derived diagnostic model and showed high reproducibility between three independent laboratories. In the validation cohort, the transcriptional classifier matched the pathology diagnosis rendered by three expert hematopathologists in 85% (n = 119) of the cases, showed borderline association with the molecular signatures in 6% (n = 8), and disagreed in 8% (n = 11). The classifier improved the pathology diagnosis in two cases, validated by clinical findings. Of the 11 cases with disagreements, four had a molecular classification that may provide an improvement over pathology diagnosis on the basis of overall transcriptomic and morphological features. The molecular subclassification provided a comprehensive molecular characterization of PTCL subtypes, including viral etiologic factors and translocation partners. We developed a novel transcriptomic approach for PTCL subclassification that facilitates translation into clinical practice with higher precision and uniformity than conventional pathology diagnosis.