Revealing the diagnostic value and immune infiltration of senescence-related genes in endometriosis: a combined single-cell and machine learning analysis

Introduction: Endometriosis is a prevalent and recurrent medical condition associated with symptoms such as pelvic discomfort, dysmenorrhea, and reproductive challenges. Furthermore, it has the potential to progress into a malignant state, significantly impacting the quality of life for affected individuals. Despite its significance, there is currently a lack of precise and non-invasive diagnostic techniques for this condition. Methods: In this study, we leveraged microarray datasets and employed a multifaceted approach. We conducted differential gene analysis, implemented weighted gene co-expression network analysis (WGCNA), and utilized machine learning algorithms, including random forest, support vector machine, and LASSO analysis, to comprehensively explore senescence-related genes (SRGs) associated with endometriosis. Discussion: Our comprehensive analysis, which also encompassed profiling of immune cell infiltration and single-cell analysis, highlights the therapeutic potential of this gene assemblage as promising targets for alleviating endometriosis. Furthermore, the integration of these biomarkers into diagnostic protocols promises to enhance diagnostic precision, offering a more effective diagnostic journey for future endometriosis patients in clinical settings. Results: Our meticulous investigation led to the identification of a cluster of genes, namely BAK1, LMNA, and FLT1, which emerged as potential discerning biomarkers for endometriosis. These biomarkers were subsequently utilized to construct an artificial neural network classifier model and were graphically represented in the form of a Nomogram.

[Efficacy, safety, and mechanism of Huangkui Capsules in treating chronic kidney disease: Meta-analysis and integrative bioinformatics]

Meta-analysis and integrative bioinformatics were employed to comprehensively study the efficacy, safety, and mechanism of Huangkui Capsules in treating chronic kidney disease(CKD). CNKI, Wanfang, VIP, SinoMed, Cochrane Library, PubMed, EMbase, and Web of Science were searched for randomized controlled trial(RCT) of Huangkui Capsules for CKD from inception to January 3, 2023. The outcome indicators included urine protein, serum creatinine(Scr), and blood urea nitrogen(BUN) levels, and Cochrane Handbook 5.1 and RevMan 5.3 were employed to perform the Meta-analysis of the included RCT. The active ingredients of Huangkui Capsules were retrieved from CNKI, and the targets of CKD from GeneCards, OMIM, and TTD. Cytoscape 3.8.0 was used to build a "component-disease" network and a protein-protein interaction(PPI) network for the screening of core components and targets. Next, a differential analysis of the core targets of Huangkui Capsules for treating CKD was conducted with the clinical samples from GEO to identify the differentially expressed core targets, and correlation analysis and immune cell infiltration analysis were then performed for these targets. A total of 13 RCTs were included for the Meta-analysis, involving 2 372 patients(1 185 in the observation group and 1 187 in the control group). Meta-analysis showed that the Huangkui Capsules group and the losartan potassium group had no significant differences in reducing the urinary protein levels after 12(MD=19.60, 95%CI[-58.66, 97.86], P=0.62) and 24 weeks(MD=-66.00, 95%CI[-264.10, 132.11], P=0.51) of treatment. Huangkui Capsules in combination with conventional treatment was superior to conventional treatment alone(MD=-0.55, 95%CI[-0.86,-0.23], P=0.000 6). Huangkui Capsules combined with conventional treatment was superior to conventional treatment alone in recovering Scr(MD=-9.21, 95%CI[-15.85,-2.58], P=0.006) and BUN(MD=-1.02, 95%CI[-1.83,-0.21], P=0.01). Five patients showed clear adverse reactions, with abdominal or gastrointestinal discomfort. Huangkui Capsules had 43 active ingredients and 393 targets, and the core ingredients were myricetin, quercetin, gossypin, elaidic acid, dihydromyricetin, isochlorogenic acid B, and caffeic acid. CKD and Huangkui Capsules shared 247 common targets, including 25 core targets. The GEO differential analysis predicted 18 differentially expressed core targets, which were mainly positively correlated with immune cell expression and involved in immune inflammation, oxidative stress, pyroptosis, lipid metabolism, sex hormone metabolism, and cell repair. Conclusively, Huangkui Capsules combined with conventional treatment significantly reduced urine protein, Scr, and BUN. Huangkui Capsules alone and losartan potassium had no significant difference in reducing urine protein. This efficacy of Huangkui Capsules may be associated with the multi-component, multi-target, and multi-pathway responses to immune inflammation and oxidative stress. The included RCT had small sample sizes and general quality. More clinical trial protocols with large sample sizes and rigorous design and in line with international norms are needed to improve the evidence quality, and the results of bioinformatics analysis remain to be confirmed by further studies.

Multi-omics of extracellular vesicles: An integrative representation of functional mediators and perspectives on lung disease study

Extracellular vesicles are secreted by almost all cell types. EVs include a broader component known as exosomes that participate in cell-cell and tissue-tissue communication via carrying diverse biological signals from one cell type or tissue to another. EVs play roles as communication messengers of the intercellular network to mediate different physiological activities or pathological changes. In particular, most EVs are natural carriers of functional cargo such as DNA, RNA, and proteins, and thus they are relevant to advancing personalized targeted therapies in clinical practice. For the application of EVs, novel bioinformatic models and methods based on high-throughput technologies and multi-omics data are required to provide a deeper understanding of their biological and biomedical characteristics. These include qualitative and quantitative representation for identifying cargo markers, local cellular communication inference for tracing the origin and production of EVs, and distant organ communication reconstruction for targeting the influential microenvironment and transferable activators. Thus, this perspective paper introduces EVs in the context of multi-omics and provides an integrative bioinformatic viewpoint of the state of current research on EVs and their applications.

Systematic Bioinformatic Analyses of Nutrigenomic Modifications by Polyphenols Associated with Cardiometabolic Health in Humans-Evidence from Targeted Nutrigenomic Studies

Cardiometabolic disorders are among the leading causes of mortality in the human population. Dietary polyphenols exert beneficial effects on cardiometabolic health in humans. Molecular mechanisms, however, are not completely understood. Aiming to conduct in-depth integrative bioinformatic analyses to elucidate molecular mechanisms underlying the protective effects of polyphenols on cardiometabolic health, we first conducted a systematic literature search to identify human intervention studies with polyphenols that demonstrate improvement of cardiometabolic risk factors in parallel with significant nutrigenomic effects. Applying the predefined inclusion criteria, we identified 58 differentially expressed genes at mRNA level and 5 miRNAs, analyzed in peripheral blood cells with RT-PCR methods. Subsequent integrative bioinformatic analyses demonstrated that polyphenols modulate genes that are mainly involved in the processes such as inflammation, lipid metabolism, and endothelial function. We also identified 37 transcription factors that are involved in the regulation of polyphenol modulated genes, including RELA/NFKB1, STAT1, JUN, or SIRT1. Integrative bioinformatic analysis of mRNA and miRNA-target pathways demonstrated several common enriched pathways that include MAPK signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, focal adhesion, or PPAR signaling pathway. These bioinformatic analyses represent a valuable source of information for the identification of molecular mechanisms underlying the beneficial health effects of polyphenols and potential target genes for future nutrigenetic studies.

High-Resolution Cartography of the Transcriptome and Methylome Landscapes of Diffuse Gliomas

Molecular mechanisms of lower-grade (II-III) diffuse gliomas (LGG) are still poorly understood, mainly because of their heterogeneity. They split into astrocytoma- (IDH-A) and oligodendroglioma-like (IDH-O) tumors both carrying mutations(s) at the isocitrate dehydrogenase (IDH) gene and into IDH wild type (IDH-wt) gliomas of glioblastoma resemblance. We generated detailed maps of the transcriptomes and DNA methylomes, revealing that cell functions divided into three major archetypic hallmarks: (i) increased proliferation in IDH-wt and, to a lesser degree, IDH-O; (ii) increased inflammation in IDH-A and IDH-wt; and (iii) the loss of synaptic transmission in all subtypes. Immunogenic properties of IDH-A are diverse, partly resembling signatures observed in grade IV mesenchymal glioblastomas or in grade I pilocytic astrocytomas. We analyzed details of coregulation between gene expression and DNA methylation and of the immunogenic micro-environment presumably driving tumor development and treatment resistance. Our transcriptome and methylome maps support personalized, case-by-case views to decipher the heterogeneity of glioma states in terms of data portraits. Thereby, molecular cartography provides a graphical coordinate system that links gene-level information with glioma subtypes, their phenotypes, and clinical context.

Identification and Validation of Nutrient State-Dependent Serum Protein Mediators of Human CD4+ T Cell Responsiveness

Intermittent fasting and fasting mimetic diets ameliorate inflammation. Similarly, serum extracted from fasted healthy and asthmatic subjects' blunt inflammation in vitro, implicating serum components in this immunomodulation. To identify the proteins orchestrating these effects, SOMAScan technology was employed to evaluate serum protein levels in healthy subjects following an overnight, 24-h fast and 3 h after refeeding. Partial least square discriminant analysis identified several serum proteins as potential candidates to confer feeding status immunomodulation. The characterization of recombinant IGFBP1 (elevated following 24 h of fasting) and PYY (elevated following refeeding) in primary human CD4⁺ T cells found that they blunted and induced immune activation, respectively. Furthermore, integrated univariate serum protein analysis compared to RNA-seq analysis from peripheral blood mononuclear cells identified the induction of IL1RL1 and MFGE8 levels in refeeding compared to the 24-h fasting in the same study. Subsequent quantitation of these candidate proteins in lean versus obese individuals identified an inverse regulation of serum levels in the fasted subjects compared to the obese subjects. In parallel, IL1RL1 and MFGE8 supplementation promoted increased CD4⁺ T responsiveness to T cell receptor activation. Together, these data show that caloric load-linked conditions evoke serological protein changes, which in turn confer biological effects on circulating CD4⁺ T cell immune responsiveness.

MMRFBiolinks: an R-package for integrating and analyzing MMRF-CoMMpass data

In order to understand the mechanisms underlying the onset and the drug responses in multiple myeloma (MM), the second most frequent hematological cancer, the use of appropriate bioinformatic tools for integrative analysis of publicly available genomic data is required. We present MMRFBiolinks, a new R package for integrating and analyzing datasets from the Multiple Myeloma Research Foundation (MMRF) CoMMpass (Clinical Outcomes in MM to Personal Assessment of Genetic Profile) study, available at MMRF Researcher Gateway (MMRF-RG), and from the National Cancer Institute Genomic Data Commons (NCI-GDC) Data Portal. The package provides several methods for integrative analysis (array-array intensity correlation, Kaplan-Meier survival analysis) and visualization (response to treatments plot) of MMRF data, for performing an easily comprehensible analysis workflow. MMRFBiolinks extends the TCGABiolinks package by providing 13 new functions to analyze MMRF-CoMMpass data: six dealing with MMRF-RG data and seven with NCI-GDC data. As validation of the tool, we present two cases studies for searching, downloading and analyzing MMRF data. The former presents a workflow for identifying genes involved in survival depending on treatment. The latter presents an analysis workflow for analyzing the Best Overall (BO) response through correlation plots between the BO Response with respect to treatments, time, duration of treatment and annotated variants, as well as through Kaplan-Meier survival curves. The case studies demonstrate how MMRFBiolinks is able of overcoming the limitations of the analysis tools available at NCI-GDC and MMRF-RG, facilitating and making more comprehensive the retrieval, downloading and analysis of MMRF data.

Integrative bioinformatics analysis of prognostic alternative splicing signatures in gastric cancer

Background

The potential prognostic value of alternative splicing (AS) variants and regulatory splicing factors in gastric carcinogenesis is unclear. We aimed to exploit the aberrant AS signatures and splicing factors involved in gastric cancer (GC) and to determine their prognostic predictive values.

Methods

We performed detailed data acquisition using the Cancer Genome Atlas project and profiled genome-wide AS signatures in a cohort of 190 patients with stomach adenocarcinoma (STAD). Prognostic prediction models and splicing correlation networks were assessed using an integrative bioinformatics analysis approach.

Results

We detected 1,308 overall survival (OS)-related AS signatures in 993 genes, most of which were favorable prognostic factors. Six splicing factors have been suggested to be dysregulated in GC, i.e., DHX15, PPP4R2, PRPF38B, RBM9, RBM15, and ILF3. Another notable finding was that most favorable prognosis AS events were positively correlated with expression of splicing factors, while a majority of poor survival prognostic AS genes were negatively associated with the expression of splicing factors.

Conclusions

To our knowledge, the current study provided the first comprehensive profiling of global modifications in the RNA splicing to identify survival associated AS signatures of GC specific genes. Our findings contribute to a better understanding of aberrant AS signatures and splicing factors in STAD, which can potentially be used as prognostic biomarkers and therapeutic targets for GC.

Differentially expressed full-length, fusion and novel isoforms transcripts-based signature of well-differentiated keratinized oral squamous cell carcinoma

Highly keratinized oral squamous cell carcinoma (OSCC) exhibits an improved response to treatment and prognosis compared with weakly keratinized OSCC. Therefore, we aimed to develop gene transcript signature and to identify novel full-length isoforms, fusion transcript and non-coding RNA to differentiate well-differentiated (WD) with Moderately Differentiated (MD)/Poorly Differentiated (PD)/WD-lymphadenopathy OSCC through, HTA, Isoform sequencing, and NanoString. Additionally, specific copy number gain and loss were also identify in WD keratinized OSCC through Oncoscan array and validated through Real-time PCR in histopathologically characterized FFPE-WD keratinized OSCC. Three-hundred-thirty-eight (338) differentially expressed full-length (FL) transcript isoforms (317 upregulated and 21 down-regulated in OSCC) were identified through Isoform Sequencing using the PacBio platform. Thirty-four (34) highly upregulated differentially expressed transcripts from IsoSeq data were also correlated with HTA2.0 and validated in 42 OSCC samples. We were able to identify 18 differentially expressed transcripts, 12 fusion transcripts, and two long noncoding RNAs. These transcripts were involved in increased cell proliferation, dysregulated metabolic reprogramming, oxidative stress, and immune system markers with enhanced immune rearrangements, suggesting a cancerous nature. However, an increase in proteasomal activity and hemidesmosome proteins suggested an improved prognosis and tumor cell stability in keratinized OSCC and helped to characterize WD with MD/PD/WD with lymphadenopathy OSCC. Additionally, novel isoforms of IL37, NAA10, UCHL3, SPAG7, and RAB24 were identified while in silico functionally validated SPAG7 represented the premalignant phenotype of keratinized (K4) OSCC. Most importantly we found copy number gain and overexpression of EGFR suggest that TKIs may also be used as therapeutics in WD-OSCCs.

Integrative Bioinformatic Analyses of Global Transcriptome Data Decipher Novel Molecular Insights into Cardiac Anti-Fibrotic Therapies

Integrative bioinformatics is an emerging field in the big data era, offering a steadily increasing number of algorithms and analysis tools. However, for researchers in experimental life sciences it is often difficult to follow and properly apply the bioinformatical methods in order to unravel the complexity and systemic effects of omics data. Here, we present an integrative bioinformatics pipeline to decipher crucial biological insights from global transcriptome profiling data to validate innovative therapeutics. It is available as a web application for an interactive and simplified analysis without the need for programming skills or deep bioinformatics background. The approach was applied to an ex vivo cardiac model treated with natural anti-fibrotic compounds and we obtained new mechanistic insights into their anti-fibrotic action and molecular interplay with miRNAs in cardiac fibrosis. Several gene pathways associated with proliferation, extracellular matrix processes and wound healing were altered, and we could identify micro (mi) RNA-21-5p and miRNA-223-3p as key molecular components related to the anti-fibrotic treatment. Importantly, our pipeline is not restricted to a specific cell type or disease and can be broadly applied to better understand the unprecedented level of complexity in big data research.

Integrative analysis of gene expression profiles reveals distinct molecular characteristics in oral tongue squamous cell carcinoma

Oral tongue squamous cell carcinoma (OTSCC) is the most common type of oral cancer. Despite advances in knowledge regarding the genome-scale gene expression pattern of oral cancer, the molecular portrait of OTSCC biology has remained unclear over the last few decades. Furthermore, studies concerning OTSCC gene-expression profiles are limited or inconsistent owing to tissue heterogeneity in single-cohort studies. Consequently, the present study integrated the profile datasets of three cohorts in order to screen for differentially expressed genes (DEGs), and subsequently identified the potential candidate genes and pathways in OTSCC through gene enrichment analysis and protein-protein interaction (PPI) network construction. Using the selected Gene Expression Omnibus datasets GSE13601, GSE31056 and GSE78060, 206 DEGs (125 upregulated and 81 downregulated) were identified in OTSCC, principally associated with extracellular matrix (ECM) organization and the phosphoinositide 3-kinase/protein kinase B signaling pathway. Furthermore, 146/206 DEGs were filtered into the PPI network and 20 hub genes were sorted. Further results indicated that the two most significant modules filtered from the PPI network were associated with ECM organization and human papillomavirus infection, which are important factors affecting OTSCC pathology. Overall, a set of OTSCC-associated DEGs has been identified, including certain key candidate genes that may be of vital importance for diagnosis, therapy and prevention of this disease.

Combined SOM-portrayal of gene expression and DNA methylation landscapes disentangles modes of epigenetic regulation in glioblastoma

AIM

We present here a novel method that enables unraveling the interplay between gene expression and DNA methylation in complex diseases such as cancer.

MATERIALS & METHODS

The method is based on self-organizing maps and allows for analysis of data landscapes from 'governed by methylation' to 'governed by expression'.

RESULTS

We identified regulatory modules of coexpressed and comethylated genes in high-grade gliomas: two modes are governed by genes hypermethylated and underexpressed in IDH-mutated cases, while two other modes reflect immune and stromal signatures in the classical and mesenchymal subtypes. A fifth mode with proneural characteristics comprises genes of repressed and poised chromatin states active in healthy brain. Two additional modes enrich genes either in active or repressed chromatin states.

CONCLUSION

The method disentangles the interplay between gene expression and methylation. It has the potential to integrate also mutation and copy number data and to apply to large sample cohorts.

Molecular characterization of long-term survivors of glioblastoma using genome- and transcriptome-wide profiling

The prognosis of glioblastoma, the most malignant type of glioma, is still poor, with only a minority of patients showing long-term survival of more than three years after diagnosis. To elucidate the molecular aberrations in glioblastomas of long-term survivors, we performed genome- and/or transcriptome-wide molecular profiling of glioblastoma samples from 94 patients, including 28 long-term survivors with >36 months overall survival (OS), 20 short-term survivors with <12 months OS and 46 patients with intermediate OS. Integrative bioinformatic analyses were used to characterize molecular aberrations in the distinct survival groups considering established molecular markers such as isocitrate dehydrogenase 1 or 2 (IDH1/2) mutations, and O(6) -methylguanine DNA methyltransferase (MGMT) promoter methylation. Patients with long-term survival were younger and more often had IDH1/2-mutant and MGMT-methylated tumors. Gene expression profiling revealed over-representation of a distinct (proneural-like) expression signature in long-term survivors that was linked to IDH1/2 mutation. However, IDH1/2-wildtype glioblastomas from long-term survivors did not show distinct gene expression profiles and included proneural, classical and mesenchymal glioblastoma subtypes. Genomic imbalances also differed between IDH1/2-mutant and IDH1/2-wildtype tumors, but not between survival groups of IDH1/2-wildtype patients. Thus, our data support an important role for MGMT promoter methylation and IDH1/2 mutation in glioblastoma long-term survival and corroborate the association of IDH1/2 mutation with distinct genomic and transcriptional profiles. Importantly, however, IDH1/2-wildtype glioblastomas in our cohort of long-term survivors lacked distinctive DNA copy number changes and gene expression signatures, indicating that other factors might have been responsible for long survival in this particular subgroup of patients.