Gene Ranking Research Articles

Integrated data driven analysis identifies potential candidate genes associated with PCOS

Polycystic ovary syndrome (PCOS) is one of the most common anovulatory disorder observed in women presenting with infertility. Several high and low throughput studies on PCOS have led to accumulation of vast amount of information on PCOS. Despite the availability of several resources which index the advances in PCOS, information on its etiology still remains inadequate. Analysis of the existing information using an integrated evidence based approach may aid identification of novel potential candidate genes with a role in PCOS pathophysiology. This work focuses on integrating existing information on PCOS from literature and gene expression studies and evaluating the application of gene prioritization and network analysis to predict missing novel candidates. Further, it assesses the utility of evidence-based scoring to rank genes for their association with PCOS. The results of this study led to identification of ~2000 plausible candidate genes associated with PCOS. Insilico validation of these identified candidates confirmed the role of 938 genes in PCOS. Further, experimental validation was carried out for four of the potential candidate genes, a high-scoring (PROS1), two mid-scoring (C1QA and KNG1), and a low-scoring gene (VTN) involved in the complement and coagulation pathway by comparing protein levels in follicular fluid in women with PCOS and healthy controls. While the expression of PROS1, C1QA, and KNG1 was found to be significantly downregulated in women with PCOS, the expression of VTN was found to be unchanged in PCOS. The findings of this study reiterate the utility of employing insilico approaches to identify and prioritize the most promising candidate genes in diseases with a complex pathophysiology like PCOS. Further, the study also helps in gaining clearer insights into the molecular mechanisms associated with the manifestation of the PCOS phenotype by contributing to the existing repertoire of genes associated with PCOS.

FungiFun3: systemic gene set enrichment analysis for fungal species.

The ever-growing amount of genome-wide omics data paved the way for solving life science problems in a data-driven manner. Among others, enrichment analysis is part of the standard analysis arsenal to determine systemic signals in any given transcriptomic or proteomic data. Only a part of the members of the fungal kingdom, however, can be analyzed via public web applications, despite the global rise of fungal pathogens and their increasing resistance to antimycotics. We present FungiFun3, a major update of our user-friendly gene set enrichment web application dedicated to fungi. FungiFun3 was rebuilt from scratch to support a modern and easy-to-use web interface and supports >4-fold more fungal strains (n = 1287 in total) than its predecessor. In addition, it also allows ranked gene set enrichment analysis at the genomic scale. FungiFun3 thus serves as a starting hub for identifying molecular signals in omics datasets related to a vast amount of available fungal strains including human fungal pathogens of the WHO's priority list and far beyond. FungiFun3, including sample data and FAQ, is freely available at https://fungifun3.hki-jena.de/.

The green formulation of silver nanoparticles for the anticancer and lupus nephritis treatment applications and reducing the inflammatory cytokines in the rat periodontal model

In this study, a novel bio-inspired synthesis for the preparation of Ag NPs by Calendula persica flower extract has been researched. Also, the effects of the recent composite on lupus nephritis by following the TGF-β1 signaling pathway in mice and gingival amounts of IL1-β and TNF-α in the animal periodontal model were determined. To assess the crystal nature, morphology and size of the prepared nanoparticles, X-ray diffraction (XRD), transmission electron microscopy (TEM), ultraviolet–visible (UV–Vis) and field emission-scanning electron microscopy (FE-SEM) analyses were applied in this study. The prepared Ag NPs was employed for its effectiveness against colon cancer cell line (C26). The bio-synthesized Ag NPs had also cytotoxic property versus colon cancer based on MTT protocol. Collectively, the Ag NPs fabricated by biological way has potential applications in inflammatory treatment. In lupus nephritis section, indexes in animal tissues and serum were identified using specific kits, while pathological alterations in the mice kidney were examined through H&E staining. Additionally, qPCR was employed to determine the expression related to TGF-β 1 in kidney, thereby providing further insight into the silver nanoparticles mechanism. The findings indicated that silver nanoparticles reduced the mRNA expression levels of TGF-β 1 and NF-κB in the kidneys, while simultaneously increasing the Mn-SOD and IκB-α expression. Histological examination of H&E-stained sections revealed that silver nanoparticles mitigated the morphological abnormalities of the glomeruli and the inflammatory infiltration associated with nephritis. Silver nanoparticles demonstrated an inhibitory effect on the dsDNA positive rate in animal suffering from nephritis. Silver nanoparticles were found to reduce the increase in urinary protein and the pro-inflammatory cytokines concentrations in both the kidney and serum. In the periodontal model study, 0-3 ligatures were applied to induce the inflammatory periodontitis in right mandibular first molar neck in rats. The levels of inflammatory cytokines (IL1-β, CINC-1, IL-10 and TNF-α) were determined by common immunological test, ELISA. The findings revealed that Ag NPs administration could decease the IL1-β and TNF-α production in the rat periodontal model gum tissue. In addition, the outcome of this research indicated that IL1-β and TNF-α levels raised in the rat periodontal model gum tissue compared to control group. Ag NPs increased the levels of superoxide dismutase, catalase, and plasma bone alkaline phosphatase and reduced the IL1-β, TNF-α, inducible nitric oxide synthase and RANK genes mRNA expression. The results indicate that pre/post-treatment with Ag NPs because of its direct activity on pro-inflammatory cytokines inhibition improved the inflammatory symptoms in the periodontitis and lupus nephritis animals.

STAREG: Statistical replicability analysis of high throughput experiments with applications to spatial transcriptomic studies.

Replicable signals from different yet conceptually related studies provide stronger scientific evidence and more powerful inference. We introduce STAREG, a statistical method for replicability analysis of high throughput experiments, and apply it to analyze spatial transcriptomic studies. STAREG uses summary statistics from multiple studies of high throughput experiments and models the the joint distribution of p-values accounting for the heterogeneity of different studies. It effectively controls the false discovery rate (FDR) and has higher power by information borrowing. Moreover, it provides different rankings of important genes. With the EM algorithm in combination with pool-adjacent-violator-algorithm (PAVA), STAREG is scalable to datasets with millions of genes without any tuning parameters. Analyzing two pairs of spatially resolved transcriptomic datasets, we are able to make biological discoveries that otherwise cannot be obtained by using existing methods.

LOCC: a novel visualization and scoring of cutoffs for continuous variables with hepatocellular carcinoma prognosis as an example

BackgroundThe interpretation of large datasets, such as The Cancer Genome Atlas (TCGA), for scientific and research purposes, remains challenging despite their public availability. In this study, we focused on identifying gene expression profiles most relevant to patient prognosis and aimed to develop a method and database to address this issue. To achieve this, we introduced Luo’s Optimization Categorization Curve (LOCC), an innovative tool for visualizing and scoring continuous variables against dichotomous outcomes. To demonstrate the efficacy of LOCC using real-world data, we analyzed gene expression profiles and patient data from TCGA hepatocellular carcinoma samples.ResultsTo showcase LOCC, we demonstrate an optimal cutoff for E2F1 expression in hepatocellular carcinoma, which was subsequently validated in an independent cohort. Compared to ROC curves and their AUC, LOCC offered a superior description of the predictive value of E2F1 expression across various cancer types. The LOCC score, comprised of factors representing significance, range, and impact of the biomarker, facilitated the ranking of all gene expression profiles in hepatocellular carcinoma, aiding in the evaluation and understanding of previously published prognostic gene signatures. We also demonstrate that LOCC does not have the same assumptions required of Cox proportional hazards modeling for accurate analysis. Repeated sampling demonstrated that LOCC scores outperformed ROC’s AUC in discriminating predictors from non-predictors. Additionally, gene set enrichment analysis revealed significant associations between certain genes and prognosis, such as E2F target genes and G2M checkpoint with poor prognosis, and bile acid metabolism and oxidative phosphorylation with good prognosis.ConclusionIn summary, we present LOCC as a novel visualization tool for the analysis of gene expression in cancer, particularly for understanding and selecting cutoffs. Our findings suggest that LOCC scores, which effectively rank genes based on their prognostic potential, represent a more suitable approach than ROC curves and Cox proportional hazard for prognostic modeling and understanding in cancer gene expression analysis. LOCC holds promise as an invaluable tool for advancing precision medicine and furthering biomarker research. Further research regarding multivariable integration and validation will help LOCC reach its full potential and establish its utility across diverse cancer types and clinical settings.Graphical abstract

A Quantitative Computational Framework for Allopolyploid Single-Cell Data Integration and Core Gene Ranking in Development.

Polyploidization drives regulatory and phenotypic innovation. How the merger of different genomes contributes to polyploid development is a fundamental issue in evolutionary developmental biology and breeding research. Clarifying this issue is challenging because of genome complexity and the difficulty in tracking stochastic subgenome divergence during development. Recent single-cell sequencing techniques enabled probing subgenome-divergent regulation in the context of cellular differentiation. However, analyzing single-cell data suffers from high error rates due to high dimensionality, noise, and sparsity, and the errors stack up in polyploid analysis due to the increased dimensionality of comparisons between subgenomes of each cell, hindering deeper mechanistic understandings. In this study, we develop a quantitative computational framework, called "pseudo-genome divergence quantification" (pgDQ), for quantifying and tracking subgenome divergence directly at the cellular level. Further comparing with cellular differentiation trajectories derived from single-cell RNA sequencing data allows for an examination of the relationship between subgenome divergence and the progression of development. pgDQ produces robust results and is insensitive to data dropout and noise, avoiding high error rates due to multiple comparisons of genes, cells, and subgenomes. A statistical diagnostic approach is proposed to identify genes that are central to subgenome divergence during development, which facilitates the integration of different data modalities, enabling the identification of factors and pathways that mediate subgenome-divergent activity during development. Case studies have demonstrated that applying pgDQ to single-cell and bulk tissue transcriptomic data promotes a systematic and deeper understanding of how dynamic subgenome divergence contributes to developmental trajectories in polyploid evolution.

CpG Methylation of Receptor Activator NF-κB (RANK) Gene Promoter Region Delineates Senescence-Related Decrease of RANK Gene Expression.

While the rapid decrease in estrogen is well known as the main cause of postmenopausal osteoporosis in women, the precise pathogenesis of senile osteoporosis in the elderly regardless of gender is largely unknown. The age-related epigenetic regulation of receptor activator NF-κB (RANK) gene expression was investigated with the use of a high-passaged mouse osteoclast progenitor cell line, RAW264.7, as an in vitro model of aging. In the RAW264.7 cells after repeated passages, receptor RANK expression was downregulated, resulting in decreased soluble RANK ligand (sRANKL)-induced osteoclastogenesis, expression of tartrate-resistant acid phosphatase-5b (TRAcP) and cathepsin K (CTSK). Methylation-specific PCR and bisulfite mapping revealed hypermethylation of CpG-loci located in the RANK gene promoter in multiple-passaged cells. ICON probe-mediated in situ assessment of methylated-cytosine at the CpG loci revealed an increase in the percentage of methylated RAW264.7 cells in the RANK gene in a passage-dependent manner. Conversely, upon treatment with demethylating agent 5-aza-2-deoxycytidine (5-aza-dC), high-passaged RAW264.7 cells displayed restored expression of the RANK gene, osteoclastogenesis, TRAcP and CTSK. Ex vivo cultures of splenic macrophages from young (10.5 W) and aged (12 M) mice also showed that CpG methylation was predominant in the aged animals, resulting in reduced RANK expression and osteoclastogenesis. Reduced RANK expression by age-related accumulation of DNA methylation, albeit in a limited population of osteoclast precursor cells, might be, at least in part, indicative of low-turnover bone characteristic of senile osteoporosis.

Normalization of gene counts affects principal components-based exploratory analysis of RNA-sequencing data

Normalization of gene expression count data is an essential step of in the analysis of RNA-sequencing data. Its statistical analysis has been mostly addressed in the context of differential expression analysis, that is in the univariate setting. However, relationships among genes and samples are better explored and quantified using multivariate exploratory data analysis tools like Principal Component Analysis (PCA). In this study we investigate how normalization impacts the PCA model and its interpretation, considering twelve different widely used normalization methods that were applied on simulated and experimental data. Correlation patterns in the normalized data were explored using both summary statistics and Covariance Simultaneous Component Analysis. The impact of normalization on the PCA solution was assessed by exploring the model complexity, the quality of sample clustering in the low-dimensional PCA space and gene ranking in the model fit to normalized data. PCA models upon normalization were interpreted in the context gene enrichment pathway analysis. We found that although PCA score plots are often similar independently form the normalization used, biological interpretation of the models can depend heavily on the normalization method applied.

GRAMP: A gene ranking and model prioritisation framework for building consensus genetic networks

Despite significant recent advancements in computational and statistical methods, different methods have specific strengths and weaknesses in the accurate reconstruction of gene regulatory networks (GRNs), making it difficult to determine the best method for each specific problem. To overcome these challenges, ensemble approaches, which combine the strengths of individual inference methods, are valuable. However, existing ensemble methods for GRN inference lack a sophisticated network aggregation method and generally rely solely on ranking approaches. These ensemble methods have no reliable mechanisms to identify highly performing inference methods specific to a given problem. They therefore tend to aggregate weak methods, diminishing the overall accuracy of the approach. Thus, developing a reliable mechanism to identify the most effective methods for specific problems and prioritize them in consensus network building is important. This paper presents a novel ensemble approach for reconstructing GRNs by integrating previously developed diverse GRN inference approaches. A novel network aggregation method called GRAMP, Gene Ranking And Model Prioritisation framework was developed, taking into consideration both local and global gene ranking and the performance of different inference approaches on a specific network. The proposed ensemble approach demonstrated performance superior to those of other state-of-the-art methods, as evidenced by results from simulated datasets and a real-world gene expression dataset.

Development of a robust and generalizable algorithm "gQuant" for accurate normalizer gene selection in qRT-PCR analysis

The emergent role of nucleic acid-based biomarkers—microRNAs(miRNAs), long non-coding RNAs(lncRNAs), and messenger RNAs(mRNAs), is becoming increasingly prominent in disease diagnostics and risk assessment. qRT-PCR is the primary analytical method for quantitative measurement of biomarkers. Yet, the relative infancy of non-coding RNAs recognition as biomarkers poses a challenge due to the absence of a consensus on a universally accepted normalizer gene, an absolute requirement for accurate quantification. Current tools normalizer selection are fraught with statistical limitations and suboptimal graphical user interface for data visualisation. These deficiencies underscore the necessity for a balanced tool tailored to handle qRT-PCR datasets. Addressing the identified challenges, we have developed 'gQuant' tool crafted to address these limitations. We employed voting classifiers that combine predictions from multiple statistical methods. Tool's efficacy was validated through different available and in house data derived from urinary exosomal miRNAs datasets. Comparative analysis with existing tools revealed that their integrated methodologies could skew the ranking of normalizer genes, whereas 'gQuant' consistently yielded rankings characterised by lower standard-deviation, reduced covariance, and enhanced kernel density estimation values. Given 'gQuant's' promising performance, normalizer gene identification will be greatly improved, improving precision of gene expression quantification in a variety of research scenarios. The gQuant tool developed for this study is available for public use and can be accessed at [https://github.com/ABHAYHBB/gQuant-Tool]."

Positive-Unlabelled learning for identifying new candidate Dietary Restriction-related genes among ageing-related genes

Dietary Restriction (DR) is one of the most popular anti-ageing interventions; recently, Machine Learning (ML) has been explored to identify potential DR-related genes among ageing-related genes, aiming to minimize costly wet lab experiments needed to expand our knowledge on DR. However, to train a model from positive (DR-related) and negative (non-DR-related) examples, the existing ML approach naively labels genes without known DR relation as negative examples, assuming that lack of DR-related annotation for a gene represents evidence of absence of DR-relatedness, rather than absence of evidence. This hinders the reliability of the negative examples (non-DR-related genes) and the method’s ability to identify novel DR-related genes. This work introduces a novel gene prioritization method based on the two-step Positive-Unlabelled (PU) Learning paradigm: using a similarity-based, KNN-inspired approach, our method first selects reliable negative examples among the genes without known DR associations. Then, these reliable negatives and all known positives are used to train a classifier that effectively differentiates DR-related and non-DR-related genes, which is finally employed to generate a more reliable ranking of promising genes for novel DR-relatedness. Our method significantly outperforms (p<0.05) the existing state-of-the-art approach in three predictive accuracy metrics with up to ∼40% lower computational cost in the best case, and we identify 4 new promising DR-related genes (PRKAB1, PRKAB2, IRS2, PRKAG1), all with evidence from the existing literature supporting their potential DR-related role.

Six drivers of aging identified among genes differentially expressed with age.

Many studies have compared gene expression in young and old samples to gain insights on aging, the primary risk factor for most major chronic diseases. However, these studies only describe associations, failing to distinguish drivers of aging from compensatory geroprotective responses and incidental downstream effects. Here, we introduce a workflow to characterize the causal effects of differentially expressed genes on lifespan. First, we performed a meta-analysis of 25 gene expression datasets comprising samples of various tissues from healthy, untreated adult mammals (humans, dogs, and rodents) at two distinct ages. We ranked each gene according to the number of distinct datasets in which the gene was differentially expressed with age in a consistent direction. The top age-upregulated genes were TMEM176A, EFEMP1, CP, and HLA-A; the top age-downregulated genes were CA4, SIAH, SPARC, and UQCR10. Second, the effects of the top ranked genes on lifespan were measured by applying post-developmental RNA interference of the corresponding ortholog in the nematode C. elegans (two trials, with roughly 100 animals per genotype per trial). Out of 10 age-upregulated and 9 age-downregulated genes that were tested, two age-upregulated genes (csp-3/CASP1 and spch-2/RSRC1) and four age-downregulated genes (C42C1.8/DIRC2, ost-1/SPARC, fzy-1/CDC20, and cah-3/CA4) produced significant and reproducible lifespan extension. Notably, the data do not suggest that the direction of differential expression with age is predictive of the effect on lifespan. Our study provides novel insight into the relationship between differential gene expression and aging phenotypes, pilots an unbiased workflow that can be easily repeated and expanded, and pinpoints six genes with evolutionarily conserved, causal roles in the aging process for further study.

Mendelian randomization analysis identified potential genes pleiotropically associated with gout.

This study aims to prioritize genes potentially involved in multifactorial or causal relationships with gout. Using the Summary Data-based Mendelian Randomization (SMR) approach, this research analyzed expression quantitative trait loci (eQTL) data from blood and renal tissues and genome-wide association study (GWAS) data related to gout. It sought to identify genetic loci potentially involved in gout. Heterogeneity testing was conducted with the HEIDI test, and results were adjusted for the False Discovery Rate (FDR). Blood cis-eQTL data were sourced from the eQTLGen Consortium's summary-level data, and renal tissue data came from the V8 release of the GTEx eQTL summary data. Gout GWAS data was sourced from the FinnGen Documentation of the R10 release. SMR analysis identified 14 gene probes in the eQTLGen blood summary-level data significantly associated with gout. The top five ranked genes are: ENSG00000169231 (labeled THBS3, PSMR = 4.16 × 10-13), ENSG00000231064 (labeled THBS3-AS1, PSMR = 1.88 × 10-8), ENSG00000163463 (labeled KRTCAP2, PSMR = 3.88 × 10-6), ENSG00000172977 (labeled KAT5, PSMR = 1.70 × 10-5), and ENSG00000161395 (labeled PGAP3, PSMR = 3.24 × 10-5). Notably, increased expression of KRTCAP2 and PGAP3 is associated with an increased risk of gout, whereas increased expression of THBS3, THBS3-AS1, and KAT5 is associated with a reduced gout risk. No significant gene associations with gout were observed in renal tissue, likely due to the limited sample size of kidney tissue. Our findings have highlighted several genes potentially involved in the pathogenesis of gout. These results offer valuable insights into the mechanisms of gout and identify potential therapeutic targets for its treatment.

Inference of essential genes in Brugia malayi and Onchocerca volvulus by machine learning and the implications for discovering new interventions

Detailed explorations of the model organisms Caenorhabditis elegans (elegant worm) and Drosophila melanogaster (vinegar fly) have substantially improved our knowledge and understanding of biological processes and pathways in metazoan organisms. Extensive functional genomic and multi-omic data sets have enabled the discovery and characterisation of ‘essential’ genes that are critical for the survival of these organisms. Recently, we showed that a machine learning (ML)-based pipeline could be utilised to predict essential genes in both C. elegans and D. melanogaster using features from DNA, RNA, protein and/or cellular data or associated information. As these distantly-related species are within the Ecdysozoa, we hypothesised that this approach could be suited for non-model organisms within the same group (phylum) of protostome animals. In the present investigation, we cross-predicted essential genes within the phylum Nematoda – between C. elegans and the parasitic filarial nematodes Brugia malayi and Onchocerca volvulus, and then ranked and prioritised these genes. Highly ranked genes were linked to key biological pathways or processes, such as ribosome biogenesis, translation and RNA processing, and were expressed at relatively high levels in the germline, gonad, hypodermis and/or nerves. The present in silico workflow is hoped to expedite the identification of drug targets in parasitic organisms for subsequent experimental validation in the laboratory.

GeneRaMeN enables integration, comparison, and meta-analysis of multiple ranked gene lists to identify consensus, unique, and correlated genes.

High-throughput experiments often produce ranked gene outputs, with forward genetic screening being a notable example. While there are various tools for analyzing individual datasets, those that perform comparative and meta-analytical examination of such ranked gene lists remain scarce. Here, we introduce Gene Rank Meta Analyzer (GeneRaMeN), an R Shiny tool utilizing rank statistics to facilitate the identification of consensus, unique, and correlated genes across multiple hit lists. We focused on two key topics to showcase GeneRaMeN: virus host factors and cancer dependencies. Using GeneRaMeN 'Rank Aggregation', we integrated 24 published and new flavivirus genetic screening datasets, including dengue, Japanese encephalitis, and Zika viruses. This meta-analysis yielded a consensus list of flavivirus host factors, elucidating the significant influence of cell line selection on screening outcomes. Similar analysis on 13 SARS-CoV-2 CRISPR screening datasets highlighted the pivotal role of meta-analysis in revealing redundant biological pathways exploited by the virus to enter human cells. Such redundancy was further underscored using GeneRaMeN's 'Rank Correlation', where a strong negative correlation was observed for host factors implicated in one entry pathway versus the alternate route. Utilizing GeneRaMeN's 'Rank Uniqueness', we analyzed human coronaviruses 229E, OC43, and SARS-CoV-2 datasets, identifying host factors uniquely associated with a defined subset of the screening datasets. Similar analyses were performed on over 1000 Cancer Dependency Map (DepMap) datasets spanning 19 human cancer types to reveal unique cancer vulnerabilities for each organ/tissue. GeneRaMeN, an efficient tool to integrate and maximize the usability of genetic screening datasets, is freely accessible via https://ysolab.shinyapps.io/GeneRaMeN.

Biological characteristics of tissue engineered-nerve grafts enhancing peripheral nerve regeneration

BackgroundA favorable regenerative microenvironment is essential for peripheral nerve regeneration. Neural tissue-specific extracellular matrix (ECM) is a natural material that helps direct cell behavior and promote axon regeneration. Both bone marrow-derived mesenchymal stem cells (BMSCs) and adipose-derived mesenchymal stem cells (ADSCs) transplantation are effective in repairing peripheral nerve injury (PNI). However, there is no study that characterizes the in vivo microenvironmental characteristics of these two MSCs for the early repair of PNI when combined with neural tissue-derived ECM materials, i.e., acellular nerve allograft (ANA).MethodsIn order to investigate biological characteristics, molecular mechanisms of early stage, and effectiveness of ADSCs- or BMSCs-injected into ANA for repairing PNI in vivo, a rat 10 mm long sciatic nerve defect model was used. We isolated primary BMSCs and ADSCs from bone marrow and adipose tissue, respectively. First, to investigate the in vivo response characteristics and underlying molecular mechanisms of ANA combined with BMSCs or ADSCs, eighty-four rats were randomly divided into three groups: ANA group, ANA+BMSC group, and ANA+ADSC group. We performed flow cytometry, RT-PCR, and immunofluorescence staining up to 4 weeks postoperatively. To further elucidate the underlying molecular mechanisms, changes in long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) were systematically investigated using whole transcriptome sequencing. We then constructed protein–protein interaction networks to find 10 top ranked hub genes among differentially expressed mRNAs. Second, in order to explore the effectiveness of BMSCs and ADSCs on neural tissue-derived ECM materials for repairing PNI, sixty-eight rats were randomized into four groups: ANA group, ANA+BMSC group, ANA+ADSC group, and AUTO group. In the ANA+BMSC and ANA+ADSC groups, ADSCs/BMSCs were equally injected along the long axis of the 10-mm ANA. Then, we performed histological and functional assessments up to 12 weeks postoperatively.ResultsThe results of flow cytometry and RT-PCR showed that ANA combined with BMSCs exhibited more significant immunomodulatory effects, as evidenced by the up-regulation of interleukin (IL)-10, down-regulation of IL-1β and tumor necrosis factor-alpha (TNF-α) expression, promotion of M1-type macrophage polarization to M2-type, and a significant increase in the number of regulatory T cells (Tregs). ANA combined with ADSCs exhibited more pronounced features of pro-myelination and angiogenesis, as evidenced by the up-regulation of myelin-associated protein gene (MBP and MPZ) and angiogenesis-related factors (TGF-β, VEGF). Moreover, differentially expressed genes from whole transcriptome sequencing results further indicated that ANA loaded with BMSCs exhibited notable immunomodulatory effects and ANA loaded with ADSCs was more associated with angiogenesis, axonal growth, and myelin formation. Notably, ANA infused with BMSCs or ADSCs enhanced peripheral nerve regeneration and motor function recovery with no statistically significant differences.ConclusionsThis study revealed that both ANA combined with BMSCs and ADSCs enhance peripheral nerve regeneration and motor function recovery, but their biological characteristics (mainly including immunomodulatory effects, pro-vascular regenerative effects, and pro-myelin regenerative effects) and underlying molecular mechanisms in the process of repairing PNI in vivo are different, providing new insights into MSC therapy for peripheral nerve injury and its clinical translation.

Rank Matrix Approach for Endometriosis: Integrating Data and Constructing Diagnostic Models

Background: Endometriosis is a debilitating gynecological disorder characterized by chronic pain, infertility, and the growth of endometrial tissue outside the uterus. Accurate and early detection of this condition is crucial for effective management and treatment. Methods: We developed a gene rank matrix-based model to integrate endometriosis cohorts across multiple platforms. After removing batch effects, we identified 83 genes associated with endometriosis and further refined a diagnostic model using 11 of these genes. The model was trained on two platforms and validated on two others using SVM, Random Forest, Logistic Regression, and gradient-boosting machine learning algorithms. Results: The integration via the gene rank matrix effectively mitigated batch effects. Utilizing a gradient boosting classifier with a subset of 11 genes, the model demonstrated commendable diagnostic efficacy, achieving an Area Under the Curve (AUC) of 0.77, an accuracy of 0.72, and an F1 score of 0.72 for the training dataset. When subjected to validation, the model maintained its performance, yielding an AUC of 0.769, an accuracy of 0.719, and an F1 score of 0.732. These 11 genes were found to be associated with immunosuppression. Conclusion: Our approach to integrating gene rank matrices effectively consolidates endometriosis data across diverse platforms. The diagnostic model, harnessing the predictive power of 11 specific genes, surpasses alternative models, thereby offering promising prospects for aiding clinical diagnosis of endometriosis. Further validation is imperative to elucidate the functional significance of these 11 genes. Our study underscores the potential of data integration coupled with machine learning techniques in advancing the diagnosis of intricate diseases, such as endometriosis.

Functional exploration of copy number alterations in a Drosophila model of triple-negative breast cancer.

Accounting for 10-20% of breast cancer cases, triple-negative breast cancer (TNBC) is associated with a disproportionate number of breast cancer deaths. One challenge in studying TNBC is its genomic profile: with the exception of TP53 loss, most breast cancer tumors are characterized by a high number of copy number alterations (CNAs), making modeling the disease in whole animals challenging. We computationally analyzed 186 CNA regions previously identified in breast cancer tumors to rank genes within each region by likelihood of acting as a tumor driver. We then used a Drosophila p53-Myc TNBC model to identify 48 genes as functional drivers. To demonstrate the utility of this functional database, we established six 3-hit models; altering candidate genes led to increased aspects of transformation as well as resistance to the chemotherapeutic drug fluorouracil. Our work provides a functional database of CNA-associated TNBC drivers, and a template for an integrated computational/whole-animal approach to identify functional drivers of transformation and drug resistance within CNAs in other tumor types.

Heme oxygenase 1 linked to inactivation of subchondral osteoclasts in osteoarthritis.

Osteoarthritis (OA) is a chronic progressive osteoarthropathy in the elderly. Osteoclast activation plays a crucial role in the occurrence of subchondral bone loss in early OA. However, the specific mechanism of osteoclast differentiation in OA remains unclear. In our study, gene expression profiles related to OA disease progression and osteoclast activation were screened from the Gene Expression Omnibus (GEO) repository. GEO2R and Funrich analysis tools were employed to find differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses demonstrated that chemical carcinogenesis, reactive oxygen species (ROS), and response to oxidative stress were mainly involved in osteoclast differentiation in OA subchondral bone. Furthermore, fourteen DEGs that are associated with oxidative stress were identified. The first ranked differential gene, heme oxygenase 1 (HMOX1), was selected for further validation. Related results showed that osteoclast activation in the pathogenesis of OA subchondral bone is accompanied by the downregulation of HMOX1. Carnosol was revealed to inhibit osteoclastogenesis by targeting HMOX1 and upregulating the expression of antioxidant protein in vitro. Meanwhile, carnosol was found to alleviate the severity of OA by inhibiting the activation of subchondral osteoclasts in vivo. Our research indicated that the activation of osteoclasts due to subchondral bone redox dysplasia may serve as a significant pathway for the advancement of OA. Targeting HMOX1 in subchondral osteoclasts may offer novel insights for the treatment of early OA.

Classification of Breast Cancer Subtypes using Microarray RNA Expression Data

Breast cancer is a heterogeneous disease that involves molecular alteration, cellular alterations, and clinical outcome for which the classification of Breast cancer remains a challenge to diagnose. Current practice uses immunohistochemistry markers and clinical variables to classify Breast cancer, but this approach has limitations due to the inclusion of other tumour subtypes and healthy individuals. Machine learning approaches based on mRNA expression data offer new possibilities for researchers to investigate the potential of molecular biomarkers as one of the diagnostic characteristics. The purpose of this study is to evaluate features (genes) rank through feature selection method for Breast cancer diagnostic test. Three feature selection methods of IG, relief and mRMR were applied and subsets of top 100, 50, 25, 10, 5 and 3 were created. Each subset was tested with SVM, LR and RF classifiers and its performance was assessed using confusion matrix. The result of this study found that the feature selection of IG, reliefF and mRMR was able to achieve highest accuracy with SVM, LR and RF classifier. mRMR with RF classifier achieved highest accuracy with the least number of top rank genes with 25 genes. Hybrid feature selection approached (mRMR + SVM) improved accuracy of top 3 highest rank genes using SVM, LR and RF classifier. Future work should aim to use other feature selection methods and classifiers to explore the classification accuracy with the least features subset in multiclass cancer dataset.