RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples

Pan‑cancer analysis of oncogene SFXN1 to identify its prognostic and immunological roles in lung adenocarcinoma

As cancer incidence and mortality rates continue to rise, the urgency for research in this field has increased globally. Sideroflexin 1 (SFXN1), a pivotal member of the SFXN protein family, serves a crucial role in transporting serine to mitochondria and participates in one‑carbon metabolism, thereby influencing cell proliferation and differentiation. While SFXN1 is linked to lung cancer and glioma, its role in other malignancies remains largely unexplored. Utilizing The Cancer Genome Atlas, Human Protein Atlas, Gene Expression Profiling Interactive Analysis and University of Alabama at Birmingham Cancer Data Analysis Portal databases, the present study investigated the expression patterns, prognostic implications and association with immune cell infiltration of SFXN1. The present findings revealed that SFXN1 was differentially expressed across various tumor types, and exhibited significant associations with clinicopathological features and patient prognosis. Through immune infiltration analysis, a significant correlation between SFXN1 and T cells, B cells and immune checkpoint genes was established in numerous tumor types. Notably, loss‑of‑function experiments demonstrated that silencing of SFXN1 decreased cell proliferation, migration and invasion, while simultaneously increasing apoptosis in lung adenocarcinoma cells. Collectively, these findings suggested that SFXN1 expression could potentially serve as a biomarker for tumor diagnosis and prognosis, also emerging as a novel therapeutic target in cancer immunotherapy. The present study highlights the critical role of SFXN1 in cancer biology and paves the way for future translational efforts aimed at leveraging its potential in clinical oncology.

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Effect of Different Staining Methods on Brain Cryosections

Staining frozen sections is often required to distinguish cell types for spatial transcriptomic studies of the brain. The impact of the staining methods on the RNA integrity of the cells becomes one of the limitations of spatial transcriptome technology with microdissection. However, there is a lack of systematic comparisons of different staining modalities for the pretreatment of frozen sections of brain tissue as well as their effects on transcriptome sequencing results. In this study, four different staining methods were analyzed for their effect on RNA integrity in frozen sections of brain tissue. Subsequently, differences in RNA quality in frozen sections under different staining conditions and their impact on transcriptome sequencing results were assessed by RNA-seq. As one of the most commonly used methods for staining pathological sections, HE staining seriously affects the RNA quality of frozen sections of brain tissue. In contrast, the homemade cresyl violet staining method developed in this study has the advantages of short staining time, low cost, and less RNA degradation. The homemade cresyl violet staining proposed in this study can be applied instead of HE staining as an advance staining step for transcriptome studies in frozen sections of brain tissue. In the future, this staining method may be suitable for wide application in brain-related studies of frozen tissue sections. Moreover, it is expected to become a routine step for staining cells before sampling in brain science.

Human transcriptome array analysis and diffusion tensor imaging in attention-deficit/hyperactivity disorder

The mRNA markers identified using microarray assay and diffusion tensor magnetic resonance imaging (DTI) were applied to elucidate the pathophysiology of attention-deficit hyperactivity disorder (ADHD). First, we obtained total RNA from leukocytes from three children with ADHD and three healthy controls for analysis with microarray assays. Subsequently, we applied real-time quantitative polymerase chain reaction (qRT‒PCR) assays to validate the differential expression of 7 genes (COX7B, CYCS, TFAM, UTP14A, ZNF280C, IFT57 and NDUFB5) between 130 ADHD patients and 70 controls, and we built an ADHD prediction model based on the ΔCt values of aforementioned seven genes (AUROC = 0.98). Finally, in a validation group (28 patients with ADHD and 27 healthy controls), mRNA expression of the above seven genes also significantly differentiated ADHD patients from controls (AUROC value = 0.91). The DTI analysis showed increased fractional anisotropy (FA) of the forceps minor, superior corona radiata, posterior corona radiata and anterior corona radiata in ADHD patients. Moreover, the FA of the right superior corona radiata tract was positively correlated with ΔCt levels of the COX7B gene and the IFT57 gene. The results shed a new light on a genetic profile of ADHD that may help in deciphering the white matter microstructural features in disease pathogenesis.

Geriatric Surgery Produces a Hypoactive Molecular Phenotype in the Monocyte Immune Gene Transcriptome

Surgery is a major challenge for the immune system, but little is known about the immune response of geriatric patients to surgery. We therefore investigated the impact of surgery on the molecular signature of circulating CD14+ monocytes, cells implicated in clinical recovery from surgery, in older patients. We enrolled older patients having elective joint replacement (N = 19) or spine (N = 16) surgery and investigated pre- to postoperative expression changes in 784 immune-related genes in monocytes. Joint replacement altered the expression of 489 genes (adjusted p < 0.05), of which 38 had a |logFC| > 1. Spine surgery changed the expression of 209 genes (adjusted p < 0.05), of which 27 had a |logFC| > 1. In both, the majority of genes with a |logFC| > 1 change were downregulated. In the combined group (N = 35), 471 transcripts were differentially expressed (adjusted p < 0.05) after surgery; 29 had a |logFC| > 1 and 72% of these were downregulated. Notably, 21 transcripts were common across procedures. Thus, elective surgery in older patients produces myriad changes in the immune gene transcriptome of monocytes, with many suggesting development of an immunocompromised/hypoactive phenotype. Because monocytes are strongly implicated in the quality of surgical recovery, this signature provides insight into the cellular and molecular mechanisms of the immune response to surgery and warrants further study as a potential biomarker for predicting poor outcomes in older surgical patients.

Transcriptome alterations in peripheral blood B cells of patients with multiple sclerosis receiving immune reconstitution therapy

BACKGROUND

Multiple sclerosis (MS) is a chronic, inflammatory and neurodegenerative disease that leads to irreversible damage to the brain and spinal cord. The goal of so-called "immune reconstitution therapies" (IRTs) is to achieve long-term disease remission by eliminating a pathogenic immune repertoire through intense short-term immune cell depletion. B cells are major targets for effective immunotherapy in MS.

OBJECTIVES

The aim of this study was to analyze the gene expression pattern of B cells before and during IRT (i.e., before B-cell depletion and after B-cell repopulation) to better understand the therapeutic effects and to identify biomarker candidates of the clinical response to therapy.

METHODS

B cells were obtained from blood samples of patients with relapsing-remitting MS (n = 50), patients with primary progressive MS (n = 13) as well as healthy controls (n = 28). The patients with relapsing MS received either monthly infusions of natalizumab (n = 29) or a pulsed IRT with alemtuzumab (n = 15) or cladribine (n = 6). B-cell subpopulation frequencies were determined by flow cytometry, and transcriptome profiling was performed using Clariom D arrays. Differentially expressed genes (DEGs) between the patient groups and controls were examined with regard to their functions and interactions. We also tested for differences in gene expression between patients with and without relapse following alemtuzumab administration.

RESULTS

Patients treated with alemtuzumab or cladribine showed on average a > 20% lower proportion of memory B cells as compared to before IRT. This was paralleled by profound transcriptome shifts, with > 6000 significant DEGs after adjustment for multiple comparisons. The top DEGs were found to regulate apoptosis, cell adhesion and RNA processing, and the most highly connected nodes in the network of encoded proteins were ESR2, PHB and RC3H1. Higher mRNA levels of BCL2, IL13RA1 and SLC38A11 were seen in patients with relapse despite IRT, though these differences did not pass the false discovery rate correction.

CONCLUSIONS

We show that B cells circulating in the blood of patients with MS undergoing IRT present a distinct gene expression signature, and we delineated the associated biological processes and gene interactions. Moreover, we identified genes whose expression may be an indicator of relapse risk, but further studies are needed to verify their potential value as biomarkers.

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Exploration of the Immunotyping Landscape and Immune Infiltration-Related Prognostic Markers in Ovarian Cancer Patients

Background

Increasing evidence indicates that immune cell infiltration (ICI) affects the prognosis of multiple cancers. This study aims to explore the immunotypes and ICI-related biomarkers in ovarian cancer.

Methods

The ICI levels were quantified with the CIBERSORT and ESTIMATE algorithms. The unsupervised consensus clustering method determined immunotypes based on the ICI profiles. Characteristic genes were identified with the Boruta algorithm. Then, the ICI score, a novel prognostic marker, was generated with the principal component analysis of the characteristic genes. The relationships between the ICI scores and clinical features were revealed. Further, an ICI signature was integrated after the univariate Cox, lasso, and stepwise regression analyses. The accuracy and robustness of the model were tested by three independent cohorts. The roles of the model in the immunophenoscores (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and immunotherapy responses were also explored. Finally, risk genes (GBP1P1, TGFBI, PLA2G2D) and immune cell marker genes (CD11B, NOS2, CD206, CD8A) were tested by qRT-PCR in clinical tissues.

Results

Three immunotypes were identified, and ICI scores were generated based on the 75 characteristic genes. CD8 TCR pathways, chemokine-related pathways, and lymphocyte activation were critical to immunophenotyping. Higher ICI scores contributed to better prognoses. An independent prognostic factor, a three-gene signature, was integrated to calculate patients’ risk scores. Higher TIDE scores, lower ICI scores, lower IPS, lower immunotherapy responses, and worse prognoses were revealed in high-risk patients. Macrophage polarization and CD8 T cell infiltration were indicated to play potentially important roles in the development of ovarian cancer in the clinical validation cohort.

Conclusions

Our study characterized the immunotyping landscape and provided novel immune infiltration-related prognostic markers in ovarian cancer.

Comprehensive analysis of gene expression profiles to identify differential prognostic factors of primary and metastatic breast cancer

Breast cancer accounts for nearly half of all cancer-related deaths in women worldwide. However, the molecular mechanisms that lead to tumour development and progression remain poorly understood and there is a need to identify candidate genes associated with primary and metastatic breast cancer progression and prognosis. In this study, candidate genes associated with prognosis of primary and metastatic breast cancer were explored through a novel bioinformatics approach. Primary and metastatic breast cancer tissues and adjacent normal breast tissues were evaluated to identify biomarkers characteristic of primary and metastatic breast cancer. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) dataset (ID: HS-01619) was downloaded using the mRNASeq platform. Genevestigator 8.3.2 was used to analyse TCGA-BRCA gene expression profiles between the sample groups and identify the differentially-expressed genes (DEGs) in each group. For each group, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were used to determine the function of DEGs. Networks of protein-protein interactions were constructed to identify the top hub genes with the highest degree of interaction. Additionally, the top hub genes were validated based on overall survival and immunohistochemistry using The Human Protein Atlas. Of the top 20 hub genes identified, four (KRT14, KIT, RAD51, and TTK) were considered as prognostic risk factors based on overall survival. KRT14 and KIT expression levels were upregulated while those of RAD51 and TTK were downregulated in patients with breast cancer. The four proposed candidate hub genes might aid in further understanding the molecular changes that distinguish primary breast tumours from metastatic tumours as well as help in developing novel therapeutics. Furthermore, they may serve as effective prognostic risk markers based on the strong correlation between their expression and patient overall survival.

Exploration of a Novel Prognostic Nomogram and Diagnostic Biomarkers Based on the Activity Variations of Hallmark Gene Sets in Hepatocellular Carcinoma

Background

The initiation and progression of tumors were due to variations of gene sets rather than individual genes. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma.

Methods

The activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package. Unsupervised clustering was conducted to determine subtypes with the ConsensusClusterPlus R package in the TCGA-LIHC (n = 329) and LIRI-JP (n = 232) cohorts. Differentially expressed genes among subtypes were identified as initial variables. Then, we used TCGA-LIHC as the training set and LIRI-JP as the validation set. A six-gene model calculating the risk scores of patients was integrated with the least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were performed to assess predictive performances. Multivariate Cox regression analyses were implemented to select independent prognostic factors, and a prognostic nomogram was integrated. Moreover, the diagnostic values of six genes were explored with the ROC curves and immunohistochemistry.

Results

Patients could be separated into two subtypes with different prognoses in both cohorts based on the identified differential hallmark pathways. Six prognostic genes (ASF1A, CENPA, LDHA, PSMB2, SRPRB, UCK2) were included in the risk score signature, which was demonstrated to be an independent prognostic factor. A nomogram including 540 patients was further integrated and well-calibrated. ROC analyses in the five cohorts and immunohistochemistry experiments in solid tissues indicated that CENPA and UCK2 exhibited high and robust diagnostic values.

Conclusions

Our study explored a promising prognostic nomogram and diagnostic biomarkers in hepatocellular carcinoma.

The Study of Alternative Splicing Events in Human Induced Pluripotent Stem Cells From a Down's Syndrome Patient

Down's syndrome (DS) is one of the most commonly known disorders with multiple congenital disabilities. Besides severe cognitive impairment and intellectual disability, individuals with DS also exhibit additional phenotypes of variable penetrance and severity, with one or more comorbid conditions, including Alzheimer's disease, congenital heart disease, or leukemia. Various vital genes and regulatory networks had been studied to reveal the pathogenesis of the disease. Nevertheless, very few studies have examined alternative splicing. Alternative splicing (AS) is a regulatory mechanism of gene expression when making one multi-exon protein-coding gene produce more than one unique mature mRNA. We employed the GeneChip Human Transcriptome Array 2.0 (HTA 2.0) for the global gene analysis with hiPSCs from DS and healthy individuals. Examining differentially expressed genes (DEGs) in these groups and focusing on specific transcripts with AS, 466 up-regulated and 722 down-regulated genes with AS events were identified. These genes were significantly enriched in biological processes, such as cell adhesion, cardiac muscle contraction, and immune response, through gene ontology (GO) analysis of DEGs. Candidate genes, such as FN1 were further explored for potentially playing a key role in DS. This study provides important insights into the potential role that AS plays in DS.

Status of diagnosis and treatment of esophageal cancer and non-coding RNA correlation research: a narrative review

OBJECTIVE

To describe and discuss the progression of the non-coding RNA as biomarkers in early esophageal cancer.

BACKGROUND

Esophageal cancer without obvious symptoms during early stages is one of the most common cancers, the current clinical treatments offer possibilities of a cure, but the survival rates and the prognoses remain poor, it is a serious threat to human life and health. Most patients are usually diagnosed during terminal stages due to low sensitivity of esophageal cancer's early detection techniques. With the development of molecular biology, an increasing number of non-coding RNAs are found to be associated with the occurrence, development, and prognosis of esophageal cancer. Some of these have begun to be used in clinics and laboratories for diagnosis, treatment, and prognosis, with the goal of reducing mortality.

METHODS

The information for this paper was collected from a variety of sources, including a search of the keynote's references, a search for texts in college libraries, and discussions with experts in the field of esophageal cancer clinical treatment.

CONCLUSIONS

Non-coding RNA does play a regulatory role in the development of esophageal cancer, which can predict the occurrence or prognosis of tumors, and become a new class of tumor markers and therapeutic targets in clinical applications. In this review, we survey the recent developments in the incidence, diagnosis, and treatment of esophageal cancer, especially with new research progresses on non-coding RNA biomarkers in detail, and discuss its potential clinical applications.

Combining Multiple RNA-Seq Data Analysis Algorithms Using Machine Learning Improves Differential Isoform Expression Analysis

RNA sequencing has become the standard technique for high resolution genome-wide monitoring of gene expression. As such, it often comprises the first step towards understanding complex molecular mechanisms driving various phenotypes, spanning organ development to disease genesis, monitoring and progression. An advantage of RNA sequencing is its ability to capture complex transcriptomic events such as alternative splicing which results in alternate isoform abundance. At the same time, this advantage remains algorithmically and computationally challenging, especially with the emergence of even higher resolution technologies such as single-cell RNA sequencing. Although several algorithms have been proposed for the effective detection of differential isoform expression from RNA-Seq data, no widely accepted golden standards have been established. This fact is further compounded by the significant differences in the output of different algorithms when applied on the same data. In addition, many of the proposed algorithms remain scarce and poorly maintained. Driven by these challenges, we developed a novel integrative approach that effectively combines the most widely used algorithms for differential transcript and isoform analysis using state-of-the-art machine learning techniques. We demonstrate its usability by applying it on simulated data based on several organisms, and using several performance metrics; we conclude that our strategy outperforms the application of the individual algorithms. Finally, our approach is implemented as an R Shiny application, with the underlying data analysis pipelines also available as docker containers.

Gene expression of intracortical bone demonstrates loading-induced increases in Wnt1 and Ngf and inhibition of bone remodeling processes

Osteocytes are the primary mechanosensitive cells in bone. However, their location in mineralized matrix has limited the in vivo study of osteocytic genes induced by mechanical loading. Laser Capture Microdissection (LCM) allows isolation of intracortical bone (Intra-CB), enriched for osteocytes, from bone tissue for gene expression analysis. We used microarray to analyze gene expression from mouse tibial Intra-CB dissected using LCM 4 h after a single loading bout or after 5 days of loading. Osteocyte enrichment was supported by greater expression of Sost, Dmp1, Dkk1, and Mepe in Intra-CB regions vs. Mixed regions containing periosteum and muscle (fold-change (FC) = 3.4, 2.2, 5.1, 3.0, respectively). Over 150 differentially expressed genes (DEGs) due to loading (loaded vs. contralateral control) in Intra-CB were found on Day 1 and Day 5, but only 10 genes were differentially expressed on both days, including Ngf (Day 1 FC = 13.5, Day 5 FC = 11.1) and Wnt1 (Day 1 FC = 1.5, Day 5 FC = 5.1). The expression of Ngf and Wnt1 within Intra-CB was confirmed by in situ hybridization, and a significant increase in number of Wnt1 mRNA molecules occurred on day 1. We also found changes in extracellular matrix remodeling with Timp1 (FC = 3.1) increased on day 1 and MMP13 (FC = 0.3) decreased on day 5. Supporting this result, IHC for osteocytic MMP13 demonstrated a marginal decrease due to loading on day 5. Gene Ontology (GO) biological processes for loading DEGs indicated regulation of vasculature, neuronal and immune processes while cell-type specific gene lists suggested regulation of osteoclast, osteoblast, and endothelial related genes. In summary, microarray analysis of microdissected Intra-CB revealed differential regulation of Ngf, Wnt1, and MMP13 due to loading in osteocytes.

Aggressive early-stage lung adenocarcinoma is characterized by epithelial cell plasticity with acquirement of stem-like traits and immune evasion phenotype

Lung adenocarcinoma (LUAD) is the main non-small-cell lung cancer diagnosed in ~40-50% of all lung cancer cases. Despite the improvements in early detection and personalized medicine, even a sizable fraction of patients with early-stage LUAD would experience disease relapses and adverse prognosis. Previous reports indicated the existence of LUAD molecular subtypes characterized by specific gene expression and mutational profiles, and correlating with prognosis. However, the biological and molecular features of such subtypes have not been further explored. Consequently, the mechanisms driving the emergence of aggressive LUAD remained unclear. Here, we adopted a multi-tiered approach ranging from molecular to functional characterization of LUAD and used it on multiple cohorts of patients (for a total of 1227 patients) and LUAD cell lines. We investigated the tumor transcriptome and the mutational and immune gene expression profiles, and we used LUAD cell lines for cancer cell phenotypic screening. We found that loss of lung cell lineage and gain of stem cell-like characteristics, along with mutator and immune evasion phenotypes, explain the aggressive behavior of a specific subset of lung adenocarcinoma that we called C1-LUAD, including early-stage disease. This subset can be identified using a 10-gene prognostic signature. Poor prognosis patients appear to have this specific molecular lung adenocarcinoma subtype which is characterized by peculiar molecular and biological features. Our data support the hypothesis that transformed lung stem/progenitor cells and/or reprogrammed epithelial cells with CSC characteristics are hallmarks of this aggressive disease. Such discoveries suggest alternative, more aggressive, therapeutic strategies for early-stage C1-LUAD.

Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth

Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r = 0.83) and, using data collected before 37 weeks of gestation, also predicts the delivery date in both normal pregnancies (r = 0.86) and those with spontaneous preterm birth (r = 0.75). Based on samples collected before 33 weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC = 0.76 versus AUROC = 0.6 at 27-33 weeks of gestation).

Robustness of differential gene expression analysis of RNA-seq

RNA-sequencing (RNA-seq) is a relatively new technology that lacks standardisation. RNA-seq can be used for Differential Gene Expression (DGE) analysis, however, no consensus exists as to which methodology ensures robust and reproducible results. Indeed, it is broadly acknowledged that DGE methods provide disparate results. Despite obstacles, RNA-seq assays are in advanced development for clinical use but further optimisation will be needed. Herein, five DGE models (DESeq2, voom + limma, edgeR, EBSeq, NOISeq) for gene-level detection were investigated for robustness to sequencing alterations using a controlled analysis of fixed count matrices. Two breast cancer datasets were analysed with full and reduced sample sizes. DGE model robustness was compared between filtering regimes and for different expression levels (high, low) using unbiased metrics. Test sensitivity estimated as relative False Discovery Rate (FDR), concordance between model outputs and comparisons of a ’population’ of slopes of relative FDRs across different library sizes, generated using linear regressions, were examined. Patterns of relative DGE model robustness proved dataset-agnostic and reliable for drawing conclusions when sample sizes were sufficiently large. Overall, the non-parametric method NOISeq was the most robust followed by edgeR, voom, EBSeq and DESeq2. Our rigorous appraisal provides information for method selection for molecular diagnostics. Metrics may prove useful towards improving the standardisation of RNA-seq for precision medicine.

GEOlimma: differential expression analysis and feature selection using pre-existing microarray data

Background

Differential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes.

Results

In this study, we propose a novel differential expression and feature selection method—GEOlimma—which combines pre-existing microarray data from the Gene Expression Omnibus (GEO) with the widely-applied Limma method for differential expression analysis. We first quantify differential gene expression across 2481 pairwise comparisons from 602 curated GEO Datasets, and we convert differential expression frequencies to DE prior probabilities. Genes with high DE prior probabilities show enrichment in cell growth and death, signal transduction, and cancer-related biological pathways, while genes with low prior probabilities were enriched in sensory system pathways. We then applied GEOlimma to four differential expression comparisons within two human disease datasets and performed differential expression, feature selection, and supervised classification analyses. Our results suggest that use of GEOlimma provides greater experimental power to detect DE genes compared to Limma, due to its increased effective sample size. Furthermore, in a supervised classification analysis using GEOlimma as a feature selection method, we observed similar or better classification performance than Limma given small, noisy subsets of an asthma dataset.

Conclusions

Our results demonstrate that GEOlimma is a more effective method for differential gene expression and feature selection analyses compared to the standard Limma method. Due to its focus on gene-level differential expression, GEOlimma also has the potential to be applied to other high-throughput biological datasets.

Effects of brain tissue section processing and storage time on gene expression

The pre-processing of samples is important factors that affect the results of the RNA-sequencing (RNA-seq) data. However, the effects of frozen sections storage conditions on the integrity of RNA and sequencing results haven't been reported. The study of frozen section protection schemes can provide reliable experimental results for single-cell and spatial transcriptome sequencing. In this study, RNA was isolated to be studied for RNA from brain section at different temperatures (RT: room temperature, -20 °C) and storage time (0 h, 2 h, 4 h, 8 h, 12 h, 16 h, 24 h, 7day, 3week, 6month). The stability of reference genes was validated using reverse transcription quantitative real-time polymerase chain reaction (qRT-PCR). The results showed that the storage at room temperature significantly affected RNA integrity number (RIN), and the RIN value was lower with the prolongation of storage, while the storage at -20 °C exerted less effect on the RIN value. Cresyl violet staining for brain tissue sections had little effect on RNA integrity. 1925, 899 and 3390 differential expression genes (DEGs) were screened at 2 h, 4 h and 8 h at room temperature, respectively. A total of 892, 478 and 619 genes were shown to be differentially expressed at -20 °C for 7d, 3w and 6 m, respectively. Among them, the expression of glycoprotein m6a (Gpm6a), calmodulin 1 (Calm1), calmodulin 1 (Calm2), thymosin, beta 4, X chromosome (Tmsb4x), ribosomal protein S21 (Rps21) and so on were correlated with RNA quality. According to the expression stability of 4 reference genes (Actb: beta-actin; Gapdh: glyceraldehyde-3-phosphate dehydrogenase; 18S: 18S ribosomal; Hprt1: hypoxanthine phosphoribosyltransferase 1), 18S is the most stable reference gene in the brain. In conclusion, the storage temperature and time of frozen sections have significant effects on RNA integrity and sequencing results. But there are still some genes that are stable and not affected by worsening of overall RNA integrity ie the decrease of RIN value. In addition, 1% cresyl violet staining can protect RNA.

Tools for time-course simulation in systems biology: a brief overview

Dynamic modeling of biological systems is essential for understanding all properties of a given organism as it allows us to look not only at the static picture of an organism but also at its behavior under various conditions. With the increasing amount of experimental data, the number of tools that enable dynamic analysis also grows. However, various tools are based on different approaches, use different types of data and offer different functions for analyses; so it can be difficult to choose the most suitable tool for a selected type of model. Here, we bring a brief overview containing descriptions of 50 tools for the reconstruction of biological models, their time-course simulation and dynamic analysis. We examined each tool using test data and divided them based on the qualitative and quantitative nature of the mathematical apparatus they use.

Methods for isolation and transcriptional profiling of individual cells from the human heart

BACKGROUND

Global transcriptional profiling of individual cells represents a powerful approach to systematically survey contributions from cell-specific molecular phenotypes to human disease states but requires tissue-specific protocols. Here we sought to comprehensively evaluate protocols for single cell isolation and transcriptional profiling from heart tissue, focusing particularly on frozen tissue which is necessary for study of human hearts at scale.

METHODS AND RESULTS

Using flow cytometry and high-content screening, we found that enzymatic dissociation of fresh murine heart tissue resulted in a sufficient yield of intact cells while for frozen murine or human heart resulted in low-quality cell suspensions across a range of protocols. These findings were consistent across enzymatic digestion protocols and whether samples were snap-frozen or treated with RNA-stabilizing agents before freezing. In contrast, we show that isolation of cardiac nuclei from frozen hearts results in a high yield of intact nuclei, and leverage expression arrays to show that nuclear transcriptomes reliably represent the cytoplasmic and whole-cell transcriptomes of the major cardiac cell types. Furthermore, coupling of nuclear isolation to PCM1-gated flow cytometry facilitated specific cardiomyocyte depletion, expanding resolution of the cardiac transcriptome beyond bulk tissue transcriptomes which were most strongly correlated with PCM1+ transcriptomes (r = 0.8). We applied these methods to generate a transcriptional catalogue of human cardiac cells by droplet-based RNA-sequencing of 8,460 nuclei from which cellular identities were inferred. Reproducibility of identified clusters was confirmed in an independent biopsy (4,760 additional PCM1- nuclei) from the same human heart.

CONCLUSION

Our results confirm the validity of single-nucleus but not single-cell isolation for transcriptional profiling of individual cells from frozen heart tissue, and establishes PCM1-gating as an efficient tool for cardiomyocyte depletion. In addition, our results provide a perspective of cell types inferred from single-nucleus transcriptomes that are present in an adult human heart.

Integration of whole-exome and anchored PCR-based next generation sequencing significantly increases detection of actionable alterations in precision oncology

BACKGROUND

Frequency of clinically relevant mutations in solid tumors by targeted and whole-exome sequencing is ∼30%. Transcriptome analysis complements detection of actionable gene fusions in advanced cancer patients. Goal of this study was to determine the added value of anchored multiplex PCR (AMP)-based next-generation sequencing (NGS) assay to identify further potential drug targets, when coupled with whole-exome sequencing (WES).

METHODS

Selected series of fifty-six samples from 55 patients enrolled in our precision medicine study were interrogated by WES and AMP-based NGS. RNA-seq was performed in 19 cases. Clinically relevant and actionable alterations detected by three methods were integrated and analyzed.

RESULTS

AMP-based NGS detected 48 fusions in 31 samples (55.4%); 31.25% (15/48) were classified as targetable based on published literature. WES revealed 29 samples (51.8%) harbored targetable alterations. TMB-high and MSI-high status were observed in 12.7% and 1.8% of cases. RNA-seq from 19 samples identified 8 targetable fusions (42.1%), also captured by AMP-based NGS. When number of actionable fusions detected by AMP-based NGS were added to WES targetable alterations, 66.1% of samples had potential drug targets. When both WES and RNA-seq were analyzed, 57.8% of samples had targetable alterations.

CONCLUSIONS

This study highlights importance of an integrative genomic approach for precision oncology, including use of different NGS platforms with complementary features. Integrating RNA data (whole transcriptome or AMP-based NGS) significantly enhances detection of potential targets in cancer patients. In absence of fresh frozen tissue, AMP-based NGS is a robust method to detect actionable fusions using low-input RNA from archival tissue.

Strategies and technologies for exploring long noncoding RNAs in heart failure

Long non-coding RNA (lncRNA) was once considered to be the "noise" of genome transcription without biological function. However, increasing evidence shows that lncRNA is dynamically expressed in developmental stage or disease status, playing a regulatory role in the process of gene expression and translation. In recent years, lncRNA is considered to be a core node of functional regulatory networks that controls cardiac and also involves in multiple process of heart failure such as myocardial hypertrophy, fibrosis, angiogenesis, etc., which would be a therapeutic target for diseases. In fact, it is the development of technology that has improved our understanding of lncRNAs and broadened our perspective on heart failure. From transcriptional "noise" to star molecule, progress of lncRNAs can't be achieved without the combination of multidisciplinary technologies, especially the emergence of high-throughput approach. Thus, here, we review the strategies and technologies available for the exploration lncRNAs and try to yield insights into the prospect of lncRNAs in clinical diagnosis and treatment in heart failure.

Probing infectious disease by single-cell RNA sequencing: Progresses and perspectives

The increasing application of single-cell RNA sequencing (scRNA-seq) technology in life science and biomedical research has significantly increased our understanding of the cellular heterogeneities in immunology, oncology and developmental biology. This review will summarize the development of various scRNA-seq technologies; primarily discussing the application of scRNA-seq on infectious diseases, and exploring the current development, challenges, and potential applications of scRNA-seq technology in the future.

Gene expression profiling identifies pathways involved in seed maturation of Jatropha curcas

Background

Jatropha curcas, a tropical shrub, is a promising biofuel crop, which produces seeds with high content of oil and protein. To better understand the maturation process of J. curcas seeds and to improve its agronomic performance, a two-step approach was performed in six different maturation stages of seeds: 1) generation of the entire transcriptome of J. curcas seeds using 454-Roche sequencing of a cDNA library, 2) comparison of transcriptional expression levels using a custom Agilent 8x60K oligonucleotide microarray.

Results

A total of 793,875 high-quality reads were assembled into 19,382 unique full-length contigs, of which 13,507 could be annotated with Gene Ontology (GO) terms. Microarray data analysis identified 9111 probes (out of 57,842 probes), which were differentially expressed between the six maturation stages. The expression results were validated for 75 selected transcripts based on expression levels, predicted function, pathway, and length.

Result from cluster analyses showed that transcripts associated with fatty acid, flavonoid, and phenylpropanoid biosynthesis were over-represented in the early stages, while those of lipid storage were over-represented in the late stages. Expression analyses of different maturation stages of J. curcas seed showed that most changes in transcript abundance occurred between the two last stages, suggesting that the timing of metabolic pathways during seed maturation in J. curcas occurs in late stages. The co-expression results showed that the hubs (CB5-D, CDR1, TT8, DFR, HVA22) with the highest number of edges, associated with fatty acid and flavonoid biosynthesis, are showing a decrease in their expression during seed maturation. Furthermore, seed development and hormone pathways are significantly well connected.

Conclusion

The obtained results revealed differentially expressed sequences (DESs) regulating important pathways related to seed maturation, which could contribute to the understanding of the complex regulatory network during seed maturation with the focus on lipid, flavonoid and phenylpropanoid biosynthesis. This study provides detailed information on transcriptional changes during J. curcas seed maturation and provides a starting point for a genomic survey of seed quality traits. The results highlighted specific genes and processes relevant to the molecular mechanisms involved in Jatropha seed maturation. These data can also be utilized regarding other Euphorbiaceae species.

PEBP4 gene expression in lung squamous cell carcinoma: A meta-analysis-based study of the molecular pathways involved

Previous studies have suggested increased activity of phosphatidylethanolamine binding protein 4 (PEBP4) may be associated with the prognosis of non-small cell lung cancer. However, to the best of our knowledge, no direct association between PEBP4 and lung squamous cell carcinoma (LSCC) has been reported. In the present study, a systematic review and meta-analysis was performed to examine the gene expression activity of PEBP4 in LSCC. A total of 10 out of 131 gene expression datasets from the Gene Expression Omnibus (GEO) were selected, including 574 samples (319 patients with LSCC and 255 healthy controls). Subsequently, multiple linear regression (MLR) was employed to study three potential influencing factors: Sample size, population region and study date. A literature-based pathway analysis was then conducted to examine the potential mechanisms through which PEBP4 may exert influence on LSCC. The results of a meta-analysis indicated that, in LSCC, PEBP4 exhibited significantly low expression levels (P<0.033), with mildly increased gene expression levels observed in three studies (log fold-change: 0.072–2.13). However, a significant between-study variance was observed from the heterogeneity analysis. MLR indicated that population region was a significant factor (P<0.0065), whereas sample size and study age were not (P>0.46). Eight functional pathways were subsequently identified, through which PEBP4 may influence the prognosis of LSCC and its response to treatment. The results of the present study suggested that the effects of PEBP4 on LSCC can be neglected in most cases of LSCC, where PEBP4 demonstrated decreased expression levels. However, in the case of PEBP4 overexpression, it may contribute to the progression of LSCC and lead to the development of drug resistance.

Non-Coding RNAs in Adrenocortical Cancer: From Pathogenesis to Diagnosis

Non-coding RNA molecules including microRNAs and long non-coding RNAs (lncRNA) have been implicated in the pathogenesis of several tumors and numerous data support their applicability in diagnosis as well. Despite recent advances, the pathogenesis of adrenocortical cancer still remains elusive and there are no reliable blood-borne markers of adrenocortical malignancy, either. Several findings show the potential applicability of microRNAs as biomarkers of malignancy and prognosis, and there are some data on lncRNA as well. In this review, we present a synopsis on the potential relevance of non-coding RNA molecules in adrenocortical pathogenesis and their applicability in diagnosis from tissue and blood.

Amniotic fluid cell-free transcriptome: a glimpse into fetal development and placental cellular dynamics during normal pregnancy

Background

The amniotic fluid (AF) cell-free transcriptome is modulated by physiologic and pathologic processes during pregnancy. AF gene expression changes with advancing gestation reflect fetal development and organ maturation; yet, defining normal expression and splicing patterns for biomarker discovery in obstetrics requires larger heterogeneous cohorts, evaluation of potential confounding factors, and novel analytical approaches.

Methods

Women with a normal pregnancy who had an AF sample collected during midtrimester (n = 30) or at term gestation (n = 68) were included. Expression profiling at exon level resolution was performed using Human Transcriptome Arrays. Differential expression was based on moderated t-test adjusted p < 0.05 and fold change > 1.25; for differential splicing, a splicing index > 2 and adjusted p < 0.05 were required. Functional profiling was used to interpret differentially expressed or spliced genes. The expression of tissue-specific and cell-type specific signatures defined by single-cell genomics was quantified and correlated with covariates. In-silico validation studies were performed using publicly available datasets.

Results

1) 64,071 genes were detected in AF, with 11% of the coding and 6% of the non-coding genes being differentially expressed between midtrimester and term gestation. Expression changes were highly correlated with those previously reported (R > 0.79, p < 0.001) and featured increased expression of genes specific to the trachea, salivary glands, and lung and decreased expression of genes specific to the cardiac myocytes, uterus, and fetal liver, among others. 2) Single-cell RNA-seq signatures of the cytotrophoblast, Hofbauer cells, erythrocytes, monocytes, T and B cells, among others, showed complex patterns of modulation with gestation (adjusted p < 0.05). 3) In 17% of the genes detected, we found differential splicing with advancing gestation in genes related to brain development processes and immunity pathways, including some that were missed based on differential expression analysis alone.

Conclusions

This represents the largest AF transcriptomics study in normal pregnancy, reporting for the first time that single-cell genomic signatures can be tracked in the AF and display complex patterns of expression during gestation. We also demonstrate a role for alternative splicing in tissue-identity acquisition, organ development, and immune processes. The results herein may have implications for the development of fetal testing to assess placental function and fetal organ maturity.

Notch3 promotes 3T3-L1 pre-adipocytes differentiation by up-regulating the expression of LARS to activate the mTOR pathway

Adipocytes constitute a major component of the tumour microenvironment. Numerous studies have shown that adipocytes promote aggressiveness and invasion by stimulating cancer cells proliferation and modulating their metabolism. Herein, we reported that Notch3 promotes mouse 3T3‐L1 pre‐adipocytes differentiation by performing the integrative transcriptome and TMT‐based proteomic analyses. The results revealed that aminoacyl‐tRNA_biosynthesis pathway was significantly influenced with Nocth3 change during 3T3‐L1 pre‐adipocytes differentiation, and the expression of LARS in this pathway was positively correlated with Notch3. Published studies have shown that LARS is a sensor of leucine that regulates the mTOR pathway activity, and the latter involves in adipogenesis. We therefore supposed that Notch3 might promote 3T3‐L1 pre‐adipocytes differentiation by up‐regulating LARS expression and activating mTOR pathway. CHIP and luciferase activity assay uncovered that Notch3 could transcriptionally regulate the expression of LARS gene. Oil Red staining identified a positive correlation between Notch3 expression and adipocytic differentiation. The activation of mTOR pathway caused by Notch3 overexpression could be attenuated by knocking down LARS expression. Altogether, our study revealed that Notch3 promotes adipocytic differentiation of 3T3‐L1 pre‐adipocytes cells by up‐regulating LARS expression and activating the mTOR pathway, which might be an emerging target for obesity treatment.

Evaluation of cross-platform and interlaboratory concordance via consensus modelling of genomic measurements

Motivation

A synoptic view of the human genome benefits chiefly from the application of nucleic acid sequencing and microarray technologies. These platforms allow interrogation of patterns such as gene expression and DNA methylation at the vast majority of canonical loci, allowing granular insights and opportunities for validation of original findings. However, problems arise when validating against a “gold standard” measurement, since this immediately biases all subsequent measurements towards that particular technology or protocol. Since all genomic measurements are estimates, in the absence of a ”gold standard” we instead empirically assess the measurement precision and sensitivity of a large suite of genomic technologies via a consensus modelling method called the row-linear model. This method is an application of the American Society for Testing and Materials Standard E691 for assessing interlaboratory precision and sources of variability across multiple testing sites. Both cross-platform and cross-locus comparisons can be made across all common loci, allowing identification of technology- and locus-specific tendencies.

Results

We assess technologies including the Infinium MethylationEPIC BeadChip, whole genome bisulfite sequencing (WGBS), two different RNA-Seq protocols (PolyA+ and Ribo-Zero) and five different gene expression array platforms. Each technology thus is characterised herein, relative to the consensus. We showcase a number of applications of the row-linear model, including correlation with known interfering traits. We demonstrate a clear effect of cross-hybridisation on the sensitivity of Infinium methylation arrays. Additionally, we perform a true interlaboratory test on a set of samples interrogated on the same platform across twenty-one separate testing laboratories.

Availability and implementation

A full implementation of the row-linear model, plus extra functions for visualisation, are found in the R package consensus at https://github.com/timpeters82/consensus.

Supplementary information

Supplementary data are available at Bioinformatics online.

Technical differences between sequencing and microarray platforms impact transcriptomic subtyping of colorectal cancer

Gene expression profiling has increasing relevance in the molecular screening of patients with colorectal cancer (CRC). We investigated potential platform-specific effects on transcriptomic subtyping according to established frameworks by comparisons of expression profiles from RNA sequencing and exon-resolution microarrays in 126 primary microsatellite stable CRCs. There was a strong platform correspondence in global gene expression levels, albeit with systematic technical bias likely attributed to few sequencing reads covering short (<2000 nucleotides) and/or lowly expressed genes (<1 FPKM), as well as over-saturation of highly expressed genes on microarrays. Classification concordances according to both the consensus molecular subtypes and CRC intrinsic subtypes (CRIS) were also strong, but with disproportionate subtype distributions between platforms caused by frequent disagreements in adherence to sample classification thresholds. Subtypes defined largely by genes expressed at low levels, including the CRIS-D subtype and the estimated level of tumor-infiltrating cytotoxic lymphocytes, had a weaker correspondence in classification metrics between platforms. In conclusion, even subtle differences between platforms suggest that clinical translation of transcriptomic CRC subtyping frameworks is dependent on assay standardization, and systematic technical biases reinforce the need for careful selection of classifier genes.

Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients

BACKGROUND

The amount of publicly available cancer-related "omics" data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis and suitable treatment options. However, the integration of different datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels.

METHODS

Here we present a method that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large discovery datasets. The approach is based on independent component analysis (ICA) - an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes transcriptomics datasets into expression profiles with minimal mutual dependency.

RESULTS

By applying the method to a small cohort of primary melanoma and control samples combined with a large discovery melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. We showed the potential of the method to predict cancer subtypes and estimate the activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation. ICA-based risk score was proposed and its connection to patient survival was validated with an independent cohort of patients. Additionally, through integration of components identified for mRNA and miRNA data, the proposed method helped deducing biological functions of miRNAs, which would otherwise not be possible.

CONCLUSIONS

We present a method that can be used to map new transcriptomic data from cancer patient samples onto large discovery datasets. The method corrects technical biases, helps characterizing activity of biological processes or cell types in the new samples and provides the prognosis of patient survival.

Cross-disease analysis identified novel common genes for both lung adenocarcinoma and lung squamous cell carcinoma

Lung squamous cell carcinoma (LSCC) exhibits a number of similarities with lung adenocarcinoma (LA) in terms of copy number alterations. However, compared with LA, the range of genetic alterations in LSCC is less understood. In the present study, a large-scale literature-based search of LA-associated genes and LSCC-associated genes was performed to identify the genetic basis in common with these two diseases. For each of the LA-associated genes, a mega-analysis was performed to test its expression variations in LSCC using 11 RNA expression datasets, with significant genes identified using statistical analysis. Subsequently, a functional pathway analysis was performed to identify a possible association between any of the significant genes identified from the mega-analysis and LSCC, followed by a co-expression analysis. A multiple linear regression (MLR) model was employed to investigate the possible influence of sample size, country of origin and study date on gene expression in patients with LSCC. Disease-gene association data analysis identified 1,178 genes involved in LA, 334 in LSCC, with a significant overlap of 187 genes (P<1.02×⁻¹⁶¹). Mega-analysis revealed that three LA-associated genes, such as solute carrier family 2 member 1 (SLC2A1), endothelial PAS domain protein 1 (EPAS1) and cyclin-dependent kinase 4 (CDK4), were significantly associated with LSCC (P<1.60×10⁻⁸), with multiple potential pathways identified by functional pathway analysis, which were further validated by co-expression analysis. The present MLR analysis suggested that the country of origin was a significant factor for the levels of expression of all three genes in patients with LSCC (P<4.0×10⁻³). Collectively, the present results suggested that genes associated with LA should be further investigated for their association with LSCC. In addition, SLC2A1, EPAS1 and CDK4 may be novel risk genes associated with LA and LSCC.

Investigation of muscle transcriptomes using gradient boosting machine learning identifies molecular predictors of feed efficiency in growing pigs

Background

Improving feed efficiency (FE) is a major challenge in pig production. This complex trait is characterized by a high variability. Therefore, the identification of predictors of FE may be a relevant strategy to reduce phenotyping efforts in breeding and selection programs. The aim of this study was to investigate the suitability of expressed muscle genes in prediction of FE traits in growing pigs. The approach considered different transcriptomics experiments to cover a large range of FE values and identify reliable predictors.

Results

Microarrays data were obtained from longissimus muscles of two lines divergently selected for residual feed intake (RFI). Pigs (n = 71) from three experiments belonged to generations 6 to 8 of selection, were fed either a diet with a standard composition or a diet rich in fiber and lipids, received feed ad libitum or at restricted level, and weighed between 80 and 115 kg at slaughter. For each pig, breeding value for RFI was estimated (RFI-BV), and feed conversion ratio (FCR) and energy-based feed conversion ratio (FCRe) were calculated during the test periods. Gradient boosting algorithms were used on the merged muscle transcriptomes to identify very important predictors of FE traits. About 20,405 annotated molecular probes were commonly expressed in longissimus muscle across experiments. Six to 267 expressed muscle genes covering a variety of biological processes were found as important predictors for RFI-BV (R² = 0.63–0.65), FCR (R² = 0.61–0.70) and FCRe (R² = 0.49–0.52). The error of prediction was less than 8% for FCR. Altogether, 56 predictors were common to RFI-BV and FCR. Expression levels of 24 target genes were further measured by qPCR. Linear regression confirmed the good accuracy of combining mRNA levels of these genes to fit FE traits (RFI-BV: R² = 0.73, FRC: R² = 0.76; FCRe: R² = 0.75). Stepwise regression procedure highlighted 10 genes (FKBP5, MUM1, AKAP12, FYN, TMED3, PHKB, TGF, SOCS6, ILR4, and FRAS1) in a linear combination predicting FCR and FCRe. In addition, FKBP5 and expression levels of five other genes (IGF2, SERINC3, CSRNP3, EZR and RPL16) significantly contributed to RFI-BV.

Conclusion

It was possible to identify few genes expressed in muscle that might be reliable predictors of feed efficiency.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-6010-9) contains supplementary material, which is available to authorized users.

2,3,7,8-Tetrachlorodibenzo-p-dioxin modifies alternative splicing in mouse liver

Alternative splicing is a co-transcriptional mechanism that generates protein diversity by including or excluding exons in different combinations, thereby expanding the diversity of protein isoforms of a single gene. Abnormalities in this process can result in deleterious effects to human health, and several xenobiotics are known to interfere with splicing regulation through multiple mechanisms. These changes could lead to human diseases such as cancer, neurological disorders, autoimmune diseases, and developmental disorders. 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is an environmental contaminant generated as a byproduct of various industrial activities. Exposure to this dioxin has been linked to a wide range of pathologies through the alteration of multiple cellular processes. However, the effects of TCDD exposure on alternative splicing have not yet been studied. Here, we investigated whether a single po. dose of 5 μg/kg or 500 μg/kg TCDD influence hepatic alternative splicing in adult male C57BL/6Kou mouse. We identified several genes whose alternative splicing of precursor messenger RNAs was modified following TCDD exposure. In particular, we demonstrated that alternative splicing of Cyp1a1, Ahrr, and Actn1 was significantly altered after TCDD treatment. These findings show that the exposure to TCDD has an impact on alternative-splicing, and suggest a new avenue for understanding TCDD-mediated toxicity and pathogenesis.

Identification of aberrantly methylated differentially expressed genes in glioblastoma multiforme and their association with patient survival

Glioblastoma multiforme (GBM) is the most malignant primary tumour type of the central nervous system with limited therapeutic options and poor prognosis, and its pathogenic mechanisms have remained to be fully elucidated. Aberrant DNA methylation is involved in multiple biological processes and may contribute to the occurrence and development of GBM by affecting the expression of certain genes. However, the specific molecular mechanisms remain to be fully elucidated. The present study focused on uncovering differentially expressed genes with altered methylation status in GBM and aimed to discover novel biomarkers for the diagnosis and treatment of GBM. These genes were identified by combined analysis of multiple gene expression and methylation datasets from gene expression omnibus (GSE16011, GSE50161 and GSE 50923) to increase the reliability. In addition, The Cancer Genome Atlas (TCGA) dataset for GBM was used to test the stability of the results. Overall, 251 hypomethylated upregulated genes (Hypo-UGs) and 199 hypermethylated downregulated genes (Hyper-DGs) were identified in the present study. Functional enrichment analysis revealed that the Hypo-UGs are involved in the regulation of immune- and infection-associated signalling, while the Hyper-DGs are involved in the regulation of synaptic transmission. The three hub genes for Hyper-DGs (somatostatin, neuropeptide Y and adenylate cyclase 2) and five hub genes for Hypo-UGs [interleukin-8, matrix metalloproteinase (MMP)9, cyclin-dependent kinase 1, 2′-5′-oligoadenylate synthetase 1, C-X-C motif chemokine ligand 10 and MMP2] were identified by protein-protein interaction network analysis. Among the Hypo-UGs and Hyper-DGs, overexpression of C-type lectin domain containing 5A, epithelial membrane protein 3, solute carrier family 43 member 3, STEAP3 metalloreductase, tumour necrosis factor α-induced protein 6 and apolipoprotein B mRNA editing enzyme catalytic subunit 3G was significantly associated with poor prognosis in the TCGA and GSE16011 datasets (P<0.001). In conclusion, the present study uncovered numerous novel aberrantly methylated genes and pathways associated with GBM. Methylation-based markers, including the hub genes and prognostic genes identified, may potentially serve as markers for the diagnosis of GBM and targets for its treatment.

Machine learning approaches to predict lupus disease activity from gene expression data

The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learning approaches to integrate gene expression data from three SLE data sets and used it to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations were employed with various classification algorithms. Classifiers were evaluated by 10-fold cross-validation across three combined data sets or by training and testing in independent data sets, the latter of which amplified the effects of technical variation. A random forest classifier achieved a peak classification accuracy of 83 percent under 10-fold cross-validation, but its performance could be severely affected by technical variation among data sets. The use of gene modules rather than raw gene expression was more robust, achieving classification accuracies of approximately 70 percent regardless of how the training and testing sets were formed. Fine-tuning the algorithms and parameter sets may generate sufficient accuracy to be informative as a standalone estimate of disease activity.

Integrative transcriptomics, proteomics, and metabolomics data analysis exploring the injury mechanism of ricin on human lung epithelial cells

Ricin (RT) is a plant toxin belonging to the family of type II ribosome-inactivating protein with high bioterrorism potential. Aerosol RT exposure is the most lethal route, but its mechanism of injury needs further investigation. In the present study, we performed a comprehensive transcriptomics, proteomics and metabolomics analysis on the potential mechanism of injury caused by RT on human lung epithelial cells. In total, 5872 genes, 187 proteins, and 143 metabolites were shown to be significantly changed in human lung epithelial cells after RT treatment. Molecular function, pathway, and network analyses, the genes and proteins regulated in RT-treated cells were mainly attributed to fatty acid metabolism, arginine and proline metabolism and ubiquitin-mediated proteolysis pathway. Furthermore, a comprehensive analysis of transcripts, proteins, and metabolites was performed. The results revealed the correlated genes, proteins, and metabolic pathways regulated in metabolic pathways, amino acid metabolism, transcription and energy metabolism. These genes, proteins, and metabolites involved in these dis-regulated pathways may provide a more targeted and credible direction to study the mechanism of RT injury on human lung epithelial cells. This study provides large-scale omics data that can be used to develop a new strategy for the prevention, rapid diagnosis, and treatment of RT poisoning, especially of RT aerosol.

Circular RNAs and RNA Splice Variants as Biomarkers for Prognosis and Therapeutic Response in the Liquid Biopsies of Lung Cancer Patients

Lung cancer, including non-small cell lung carcinoma (NSCLC), is the most frequently diagnosed cancer. It is also the leading cause of cancer-related mortality worldwide because of its late diagnosis and its resistance to therapies. Therefore, the identification of biomarkers for early diagnosis, prognosis, and monitoring of therapeutic response is urgently needed. Liquid biopsies, especially blood, are considered as promising tools to detect and quantify circulating cancer biomarkers. Cell-free circulating tumor DNA has been extensively studied. Recently, the possibility to detect and quantify RNAs in tumor biopsies, notably circulating cell-free RNAs, has gained great attention. RNA alternative splicing contributes to the proteome diversity through the biogenesis of several mRNA splice variants from the same pre-mRNA. Circular RNA (circRNA) is a new class of RNAs resulting from pre-mRNA back splicing. Owing to the development of high-throughput transcriptomic analyses, numerous RNA splice variants and, more recently, circRNAs have been identified and found to be differentially expressed in tumor patients compared to healthy controls. The contribution of some of these RNA splice variants and circRNAs to tumor progression, dissemination, or drug response has been clearly demonstrated in preclinical models. In this review, we discuss the potential of circRNAs and mRNA splice variants as candidate biomarkers for the prognosis and the therapeutic response of NSCLC in liquid biopsies.

Identification of potential biomarkers related to glioma survival by gene expression profile analysis

BACKGROUND

Recent studies have proposed several gene signatures as biomarkers for different grades of gliomas from various perspectives. However, most of these genes can only be used appropriately for patients with specific grades of gliomas.

METHODS

In this study, we aimed to identify survival-relevant genes shared between glioblastoma multiforme (GBM) and lower-grade glioma (LGG), which could be used as potential biomarkers to classify patients into different risk groups. Cox proportional hazard regression model (Cox model) was used to extract relative genes, and effectiveness of genes was estimated against random forest regression. Finally, risk models were constructed with logistic regression.

RESULTS

We identified 104 key genes that were shared between GBM and LGG, which could be significantly correlated with patients' survival based on next-generation sequencing data obtained from The Cancer Genome Atlas for gene expression analysis. The effectiveness of these genes in the survival prediction of GBM and LGG was evaluated, and the average receiver operating characteristic curve (ROC) area under the curve values ranged from 0.7 to 0.8. Gene set enrichment analysis revealed that these genes were involved in eight significant pathways and 23 molecular functions. Moreover, the expressions of ten (CTSZ, EFEMP2, ITGA5, KDELR2, MDK, MICALL2, MAP 2 K3, PLAUR, SERPINE1, and SOCS3) of these genes were significantly higher in GBM than in LGG, and comparing their expression levels to those of the proposed control genes (TBP, IPO8, and SDHA) could have the potential capability to classify patients into high- and low- risk groups, which differ significantly in the overall survival. Signatures of candidate genes were validated, by multiple microarray datasets from Gene Expression Omnibus, to increase the robustness of using these potential prognostic factors. In both the GBM and LGG cohort study, most of the patients in the high-risk group had the IDH1 wild-type gene, and those in the low-risk group had IDH1 mutations. Moreover, most of the high-risk patients with LGG possessed a 1p/19q-noncodeletion.

CONCLUSION

In this study, we identified survival relevant genes which were shared between GBM and LGG, and those enabled to classify patients into high- and low-risk groups based on expression level analysis. Both the risk groups could be correlated with the well-known genetic variants, thus suggesting their potential prognostic value in clinical application.

Long noncoding RNA expression profile and functional analysis in psoriasis

Long noncoding RNAs (lncRNAs) serve important roles in the biology of autoimmune diseases and immune-associated disorders. To identify lncRNAs specifically associated with psoriasis, the expression of lncRNAs from biopsies obtained from patients with psoriasis were compared with samples obtained from healthy volunteers using a microarray. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to detect the expression of 10 identified dysregulated lncRNAs. Cis- and trans-regulated target genes of lncRNAs were predicted. The results of microarray analysis indicated that 2,194 lncRNAs and 1,725 mRNAs were significantly dysregulated. Gene Ontology and pathway analyses among the dysregulated genes were performed. Co-expression network analysis was also performed to study molecular interactions. Several identified pathways were associated with psoriasis. Among the 2,194 dysregulated lncRNAs, 1,549 of these had cis- or trans-regulated predicted target genes. Among the 1,725 dysregulated mRNAs, 289 of the cis-regulated target genes and 262 of the trans-regulated target genes may be regulated by the differentially expressed lncRNAs; 10 differentially expressed lncRNAs were randomly selected and then validated. Of these lncRNAs, 7 exhibited the same expression profile as determined via microarray analysis, of which 3 lncRNAs were upregulated and 4 lncRNAs were downregulated. To the best of our knowledge, the present study is the first in which a microarray has been used to investigate the expression profile of lncRNAs associated with psoriasis. Additionally, the expression levels of the 10 aforementioned lncRNAs associated with psoriasis were validated in the present study for the first time using RT-qPCR. The findings demonstrated that lncRNAs may contribute to the pathogenesis of psoriasis and suggested their potential diagnostic and therapeutic value. Furthermore, the findings of the present study suggest that the combined actions of several lncRNAs may contribute to the pathogenesis of psoriasis.

Genomic evidence of Y chromosome microchimerism in the endometrium during endometriosis and in cases of infertility

BACKGROUND

Previous studies, which were primarily based on the fluorescent in-situ hybridisation (FISH) technique, revealed conflicting evidence regarding male foetal microchimerism in endometriosis. FISH is a relatively less sensitive technique, as it is performed on a small portion of the sample. Additionally, the probes used in the previous studies specifically detected centromeric and telomeric regions of Y chromosome, which are gene-sparse heterochromatised regions. In the present study, a panel of molecular biology tools such as qPCR, expression microarray, RNA-seq and qRT-PCR were employed to examine the Y chromosome microchimerism in the endometrium using secretory phase samples from fertile and infertile patients with severe (stage IV) ovarian endometriosis (OE) and without endometriosis.

METHODS

Microarray expression analysis followed by validation using RNA-seq and qRT-PCR experiments at the RNA levels and further validation at the DNA level by qPCR of target inserts for selected targets in eutopic endometrium samples obtained from control (CON) and stage IV ovarian endometriosis (OE), either from fertile (FCON and FOE; n = 30/each) or infertile (ICON and IOE; n = 30/each) women, were performed.

RESULTS

Six coding (AMELY, PCDH11, SRY, TGIF2LY, TSPY3, and USP9Y) and 10 non-coding (TTTY2, TTTY4C, TTTY5, TTTYY6, TTTY8, TTTY10, TTTY14, TTTY21, TTTY22, and TTTY23) genes exhibited a bimodal pattern of expression characterised by low expression in samples from fertile patients and high expression in samples from infertile patients. Seven coding MSY-linked genes (BAGE, CD24, EIF1AY, NLGN4Y, PRKY, VCY and ZFY) exhibited differential regulation in microarray analysis, and this change was validated by RNA-seq or qRT-PCR. DNA inserts for 7 genes in various samples were validated by qPCR. The prevalence and concentration of PCR-positive target inserts for BAGE, PRKY, TTTY9A and ZFY displayed higher values in the fertile, control (FCON) patients compared with the fertile, endometriosis patients (FOE).

CONCLUSION

Several coding and non-coding MSY-linked genes displayed microchimerism as evidenced by the presence of their respective DNA inserts, along with their differential transcript expression, in the endometrium during endometriosis and in cases of infertility.

Splicing factor ESRP1 controls ER-positive breast cancer by altering metabolic pathways

The epithelial splicing regulatory proteins 1 and 2 (ESRP1 and ESRP2) control the epithelial-to-mesenchymal transition (EMT) splicing program in cancer. However, their role in breast cancer recurrence is unclear. In this study, we report that high levels of ESRP1, but not ESRP2, are associated with poor prognosis in estrogen receptor positive (ER+) breast tumors. Knockdown of ESRP1 in endocrine-resistant breast cancer models decreases growth significantly and alters the EMT splicing signature, which we confirm using TCGA SpliceSeq data of ER+ BRCA tumors. However, these changes are not accompanied by the development of a mesenchymal phenotype or a change in key EMT-transcription factors. In tamoxifen-resistant cells, knockdown of ESRP1 affects lipid metabolism and oxidoreductase processes, resulting in the decreased expression of fatty acid synthase (FASN), stearoyl-CoA desaturase 1 (SCD1), and phosphoglycerate dehydrogenase (PHGDH) at both the mRNA and protein levels. Furthermore, ESRP1 knockdown increases the basal respiration and spare respiration capacity. This study reports a novel role for ESRP1 that could form the basis for the prevention of tamoxifen resistance in ER+ breast cancer.

Challenges and Future Trends for Microarray Analysis

The current situation in microarray data analysis and prospects for the future are briefly discussed in this chapter, in which the competition between microarray technologies and high-throughput technologies is considered under a data analysis view. The up-to-date limitations of DNA microarrays are important to forecast challenges and future trends in microarray data analysis; these include data analysis techniques associated with an increasing sample sizes, new feature selection methods, deep learning techniques, covariate significance testing as well as false discovery rate methods, among other procedures for a better interpretability of the results.

Integrative expression analysis identifies a novel interplay between CFTR and linc-SUMF1-2 that involves CF-associated gene dysregulation

Cystic fibrosis transmembrane regulator (CFTR) is a cyclic AMP-dependent Cl^- channel, and its dysfunction, due to CFTR gene mutations, causes the lethal inherited disorder cystic fibrosis (CF). To date, widespread dysregulation of certain coding genes in CF airway epithelial cells is well studied and considered as the driver of pulmonary abnormality. However, the involvement of non-coding genes, novel classes of functional RNAs with little or no protein-coding capacity, in the regulation of CF-associated gene dysregulation is poorly understood. Here, we utilized integrative analyses of human transcriptome array (HTA) and characterized 99 coding and 91 non-coding RNAs that are dysregulated in CFTR-defective CF bronchial epithelial cell line CFBE41o-. Among these genes, the expression level of linc-SUMF1-2, an intergenic non-coding RNA (lincRNA) whose function is unknown, was inversely correlated with that of WT-CFTR and consistently higher in primary human CF airway epithelial cells (DHBE-CF). Further integrative analyses under linc-SUMF1-knockdown condition determined MXRA5, SEMA5A, CXCL10, AK022877, CTGF, MYC, AREG and LAMB3 as both CFTR- and linc-SUMF1-2-dependent dysregulated gene sets in CF airway epithelial cells. Overall, our analyses reveal linc-SUMF1-2 as a dysregulated non-coding gene in CF as well as CFTR-linc-SUMF1-2 axis as a novel regulatory pathway involved in CF-associated gene dysregulation.

Hub genes in a pan-cancer co-expression network show potential for predicting drug responses

Background: The topological analysis of networks extracted from different types of "omics" data is a useful strategy for characterizing biologically meaningful properties of the complex systems underlying these networks. In particular, the biological significance of highly connected genes in diverse molecular networks has been previously determined using data from several model organisms and phenotypes. Despite such insights, the predictive potential of candidate hubs in gene co-expression networks in the specific context of cancer-related drug experiments remains to be deeply investigated. The examination of such associations may offer opportunities for the accurate prediction of anticancer drug responses.  Methods: Here, we address this problem by: a) analyzing a co-expression network obtained from thousands of cancer cell lines, b) detecting significant network hubs, and c) assessing their capacity to predict drug sensitivity using data from thousands of drug experiments. We investigated the prediction capability of those genes using a multiple linear regression model, independent datasets, comparisons with other models and our own in vitro experiments. Results: These analyses led to the identification of 47 hub genes, which are implicated in a diverse range of cancer-relevant processes and pathways. Overall, encouraging agreements between predicted and observed drug sensitivities were observed in public datasets, as well as in our in vitro validations for four glioblastoma cell lines and four drugs. To facilitate further research, we share our hub-based drug sensitivity prediction model as an online tool. Conclusions: Our research shows that co-expression network hubs are biologically interesting and exhibit potential for predicting drug responses in vitro. These findings motivate further investigations about the relevance and application of our unbiased discovery approach in pre-clinical, translationally-oriented research.