Principal component analysis of binary genomics data

Maternal ploidy shapes reproductive pathways in the triploid hybrid Chrosomus eos × eos-neogaeus

Elements transferred from a mother to her eggs may strongly influence the phenotype of her offspring. Such maternal effects depend on the genotype of the mother, and while multiple ploidy levels occur naturally in some vertebrate species, studies evaluating the impact of maternal ploidy on offspring are scarce. This paper aimed to test whether maternal ploidy is responsible for the two reproductive phenotypes observed in the triploid fish Chrosomus eos × eos-neogaeus. Indeed, these hybrids have two different maternal origins (diploid or triploid) and display two reproductive phenotypes, ameiotic and meiotic hybridogenesis, resulting in diploid and haploid eggs, respectively. To this end, we first conducted a genomic survey to identify epigenetic variations in triploid larvae reared under common garden conditions, concordantly with their maternal origin. The results revealed that the polymorphic epigenetic loci of the larvae clustered into two highly distinct groups consistently with the ploidy of their mother. Diagnostic epigenetic loci were then tested in triploid adult females whose reproductive pathways were already known, to infer their own maternal origin. Altogether, the results suggest that triploid larvae from diploid and triploid mothers will develop the ameiotic and meiotic hybridogenesis pathway, respectively. This confirms that the development of a given reproductive pathway in triploid females results from the ploidy of their mother. Overall, this study supports a strong maternal effect, introducing maternal ploidy and reproductive pathways as additional cause and effect of maternal effects, respectively.

Identification and Preliminary Clinical Validation of Key Extracellular Proteins as the Potential Biomarkers in Hashimoto's Thyroiditis by Comprehensive Analysis

Hashimoto's thyroiditis (HT) is an autoimmune disruption manifested by immune cell infiltration in thyroid tissue and the production of antibodies against thyroid-specific antigens, such as the thyroid peroxidase antibody (TPOAb) and thyroglobulin antibody (TGAb). TPOAb and TGAb are commonly used in clinical tests; however, handy indicators of the diagnosis and progression of HT are still scarce. Extracellular proteins are glycosylated and are likely to enter body fluids and become readily available and detectable biomarkers. Our research aimed to discover extracellular biomarkers and potential treatment targets associated with HT through integrated bioinformatics analysis and clinical sample validations. A total of 19 extracellular protein-differentially expressed genes (EP-DEGs) were screened by the GSE138198 dataset from the Gene Expression Omnibus (GEO) database and protein annotation databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the function and pathway of EP-DEGs. STRING, Cytoscape, MCODE, and Cytohubba were used to construct a protein-protein interaction (PPI) network and screen key EP-DEGs. Six key EP-DEGs (CCL5, GZMK, CXCL9, CXCL10, CXCL11, and CXCL13) were further validated in the GSE29315 dataset and the diagnostic curves were evaluated, which all showed high diagnostic accuracy (AUC > 0.95) for HT. Immune profiling revealed the correlation of the six key EP-DEGs and the pivotal immune cells in HT, such as CD8+ T cells, dendritic cells, and Th2 cells. Further, we also confirmed the key EP-DEGs in clinical thyroid samples. Our study may provide bioinformatics and clinical evidence for revealing the pathogenesis of HT and improving the potential diagnosis biomarkers and therapeutic strategies for HT.

The Role of NCS1 in Immunotherapy and Prognosis of Human Cancer

The Neural Calcium Sensor1 (NCS1) is a crucial protein that binds to Ca2+ and is believed to play a role in regulating tumor invasion and cell proliferation. However, the role of NCS1 in immune infiltration and cancer prognosis is still unknown. Our study aimed to explore the expression profile, immune infiltration pattern, prognostic value, biological function, and potential compounds targeting NCS1 using public databases. High expression of NCS1 was detected by immune histochemical staining in LIHC (Liver hepatocellular carcinoma), BRCA (Breast invasive carcinoma), KIRC (Kidney renal clear cell carcinoma), and SKCM (Skin Cutaneous Melanoma). The expression of NCS1 in cancer was determined by TCGA (The Cancer Genome Atlas Program), GTEx (The Genotype-Tissue Expression), the Kaplan-Meier plotter, GEO (Gene Expression Omnibus), GEPIA2.0 (Gene Expression Profiling Interactive Analysis 2.0), HPA (The Human Protein Atlas), UALCAN, TIMER2.0, TISIDB, Metascape, Drugbank, chEMBL, and ICSDB databases. NCS1 has genomic mutations as well as aberrant DNA methylation in multiple cancers compared to normal tissues. Also, NCS1 was significantly different in the immune microenvironment, tumor mutational burden (TMB), microsatellite instability (MSI), and immune infiltrate-associated cells in different cancers, which could be used for the typing of immune and molecular subtypes of cancer and the presence of immune checkpoint resistance in several cancers. Univariate regression analysis, multivariate regression analysis, and gene enrichment analysis to construct prognostic models revealed that NCS1 is involved in immune regulation and can be used as a prognostic biomarker for SKCM, LIHC, BRCA, COAD, and KIRC. These results provide clues from a bioinformatic perspective and highlight the importance of NCS1 in a variety of cancers.

An Overview of Deep Generative Models in Functional and Evolutionary Genomics

Following the widespread use of deep learning for genomics, deep generative modeling is also becoming a viable methodology for the broad field. Deep generative models (DGMs) can learn the complex structure of genomic data and allow researchers to generate novel genomic instances that retain the real characteristics of the original dataset. Aside from data generation, DGMs can also be used for dimensionality reduction by mapping the data space to a latent space, as well as for prediction tasks via exploitation of this learned mapping or supervised/semi-supervised DGM designs. In this review, we briefly introduce generative modeling and two currently prevailing architectures, we present conceptual applications along with notable examples in functional and evolutionary genomics, and we provide our perspective on potential challenges and future directions.

Roxadustat alleviates the inflammatory status in patients receiving maintenance hemodialysis with erythropoiesis-stimulating agent resistance by increasing the short-chain fatty acids producing gut bacteria

BACKGROUND

Hypoxia-inducible factor-prolyl hydroxylase inhibitors (HIF-PHIs) have improved the treatment of renal anemia, especially in patients resistant to erythropoiesis-stimulating agents (ESAs). HIF facilitates maintain gut microbiota homeostasis, which plays an important role in inflammation and iron metabolism, which are in turn key factors affecting ESA resistance. The current study aimed to investigate the effects of roxadustat on inflammation and iron metabolism and on the gut microbiota in patients with ESA resistance.

METHODS

We conducted a self-controlled, single-center study including 30 patients with ESA resistance undergoing maintenance hemodialysis. All patients received roxadustat without iron agents for renal anemia. Hemoglobin and inflammatory factors were monitored. Fecal samples were collected before and after 3 months' administration and the gut microbiota were analyzed by 16S ribosomal RNA gene sequencing.

RESULTS

Hemoglobin levels increased after treatment with roxadustat for 3 months (P < 0.05). Gut microbiota diversity and abundance also changed, with increases in short-chain fatty acid (SCFA)-producing bacteria (Acidaminococcaceae, Butyricicoccus, Ruminococcus bicirculans, Ruminococcus bromii, Bifidobacterium dentium, Eubacterium hallii) (P < 0.05). Serum SCFA levels also increased (P < 0.05). Inflammatory factors, including interleukin (IL)-1, IL-6, tumor necrosis factor (TNF)-α, interferon-γ, and endotoxin gradually decreased (P < 0.05). Serum hepcidin, ferritin, and total and unsaturated iron-binding capacities decreased (P < 0.05), while soluble transferrin receptor levels increased at each time point (P < 0.05). There were no significant differences in serum iron and transferrin saturation at each time point. The abundance of Alistipes shahii was significantly negatively correlated with IL-6 and TNF-α (P < 0.05).

CONCLUSIONS

Roxadustat alleviated renal anemia in patients with ESA resistance by decreasing inflammatory factors and hepcidin levels and improving iron utilization. These effects were at least partly mediated by improved diversity and abundance of SCFA-producing gut bacteria, probably via activation of HIF.

Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy

The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.

MMP1 acts as a potential regulator of tumor progression and dedifferentiation in papillary thyroid cancer

Papillary thyroid cancer (PTC) is one of the malignancies with an excellent prognosis. However, in PTC, progression or dedifferentiation into poorly differentiated thyroid cancer (PDTC) or anaplastic thyroid cancer (ATC) extremely jeopardizes patients’ prognosis. MMP1 is a zinc-dependent endopeptidase, and its role in PTC progression and dedifferentiation is unclear. In this study, transcriptome data of PDTC/ATC and PTC from the Gene Expression Omnibus and The Cancer Genome Atlas databases were utilized to perform an integrated analysis of MMP1 as a potential regulator of tumor progression and dedifferentiation in PTC. Both bulk and single-cell RNA-sequencing data confirmed the high expression of MMP1 in ATC tissues and cells, and further study verified that MMP1 possessed good diagnostic and prognostic value in PTC and PDTC/ATC. Up-regulated MMP1 was found to be positively related to more aggressive clinical characteristics, worse survival, extracellular matrix-related pathways, oncogenic immune microenvironment, more mutations, higher stemness, and more dedifferentiation of PTC. Meanwhile, in vitro experiments verified the high level of MMP1 in PDTC/ATC cell lines, and MMP1 knockdown and its inhibitor triolein could both inhibit the cell viability of PTC and PDTC/ATC. In conclusion, our findings suggest that MMP1 is a potential regulator of tumor progression and dedifferentiation in PTC, and might become a novel therapeutic target for PTC, especially for more aggressive PDTC and ATC.

Intelligent and novel multi-type cancer prediction model using optimized ensemble learning

Cancer is known to be highly severe disease and gets incurable even when the treatment has started at the time of diagnosis owing to the occurrence of cancer cells. Diverse machine learning approaches are implemented for predicting the cancer recurrence that needs to be evaluated for showing the appropriate approach for cancer prediction. This paper provides intelligent optimized ensemble learning for predicting multiple types of cancers. At first, the different types of cancer data are collected and performed the data cleansing. Then, the feature extraction is done using statistical features, 'Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA)'. With these features, a new Adaptive Condition Searched-Harris hawks Whale Optimization (ACS-HWO) is used for selecting the optimal features and transformed into weighted features with meta-heuristic update. The prediction is carried out by Optimized Ensemble-based Multi-disease Detection (OEMD) with Support Vector Machine (SVM), Autoencoder, Adaboost, 'Deep Neural Network (DNN), and Recurrent Neural Network (RNN)' with high ranking strategy. The same ACS-HWO is used for improvising the weighted feature selection and optimized ensemble learning. The comparative analysis over existing models shows that the suggested method can be highly applicable for the healthcare system to ensure the consistent prediction with the multi-type of cancers.

Predictive Biomarkers for Postmyocardial Infarction Heart Failure Using Machine Learning: A Secondary Analysis of a Cohort Study

Background

There are few biomarkers with an excellent predictive value for postacute myocardial infarction (MI) patients who developed heart failure (HF). This study aimed to screen candidate biomarkers to predict post-MI HF.

Methods

This is a secondary analysis of a single-center cohort study including nine post-MI HF patients and eight post-MI patients who remained HF-free over a 6-month follow-up. Transcriptional profiling was analyzed using the whole blood samples collected at admission, discharge, and 1-month follow-up. We screened differentially expressed genes and identified key modules using weighted gene coexpression network analysis. We confirmed the candidate biomarkers using the developed external datasets on post-MI HF. The receiver operating characteristic curves were created to evaluate the predictive value of these candidate biomarkers.

Results

A total of 6,778, 1,136, and 1,974 genes (dataset 1) were differently expressed at admission, discharge, and 1-month follow-up, respectively. The white and royal blue modules were most significantly correlated with post-MI HF (dataset 2). After overlapping dataset 1, dataset 2, and external datasets (dataset 3), we identified five candidate biomarkers, including FCGR2A, GSDMB, MIR330, MED1, and SQSTM1. When GSDMB and SQSTM1 were combined, the area under the curve achieved 1.00, 0.85, and 0.89 in admission, discharge, and 1-month follow-up, respectively.

Conclusions

This study demonstrates that FCGR2A, GSDMB, MIR330, MED1, and SQSTM1 are the candidate predictive biomarker genes for post-MI HF, and the combination of GSDMB and SQSTM1 has a high predictive value.

Epigenetic Biomarkers of Transition from Metabolically Healthy Obesity to Metabolically Unhealthy Obesity Phenotype: A Prospective Study

Background: Identifying those parameters that could potentially predict the deterioration of metabolically healthy phenotype is a matter of debate. In this field, epigenetics, in particular DNA methylation deserves special attention. Results: The aim of the present study was to analyze the long-term evolution of methylation patterns in a subset of metabolically healthy subjects in order to search for epigenetic markers that could predict the progression to an unhealthy state. Twenty-six CpG sites were significantly differentially methylated, both at baseline and 11-year follow-up. These sites were related to 19 genes or pseudogenes; a more in-depth analysis of the methylation sites of these genes showed that CYP2E1 had 50% of the collected CpG sites differently methylated between stable metabolically healthy obesity (MHO) and unstable MHO, followed by HLA-DRB1 (33%), ZBTB45 (16%), HOOK3 (14%), PLCZ1 (14%), SLC1A1 (12%), MUC2 (12%), ZFPM2 (12.5%) and HLA-DQB2 (8%). Pathway analysis of the selected 26 CpG sites showed enrichment in pathways linked to th1 and th2 activation, antigen presentation, allograft rejection signals and metabolic processes. Higher methylation levels in the cg20707527 (ZFPM2) could have a protective effect against the progression to unstable MHO (OR: 0.21, 95%CI (0.067–0.667), p < 0.0001), whilst higher methylation levels in cg11445109 (CYP2E1) would increase the progression to MUO; OR: 2.72, 95%CI (1.094–6.796), p < 0.0014; respectively). Conclusions: DNA methylation status is associated with the stability/worsening of MHO phenotype. Two potential biomarkers of the transition to an unhealthy state were identified and deserve further investigation (cg20707527 and cg11445109). Moreover, the described differences in methylation could alter immune system-related pathways, highlighting these pathways as therapeutic targets to prevent metabolic deterioration in MHO patients.

Clinical pharmacogenomics in action: design, assessment and implementation of a novel pharmacogenetic panel supporting drug selection for diseases of the central nervous system (CNS)

Background

Pharmacogenomics describes the link between gene variations (polymorphisms) and drug responses. In view of the implementation of precision medicine in personalized healthcare, pharmacogenetic tests have recently been introduced in the clinical practice. However, the translational aspects of such tests have been limited due to the lack of robust population-based evidence.

Materials

In this paper we present a novel pharmacogenetic panel (iDNA Genomics-PGx–CNS or PGx–CNS), consisting of 24 single nucleotide polymorphisms (SNPs) on 13 genes involved in the signaling or/and the metabolism of 28 approved drugs currently administered to treat diseases of the Central Nervous System (CNS). We have tested the PGx–CNS panel on 501 patient-derived DNA samples from a southeastern European population and applied biostatistical analyses on the pharmacogenetic associations involving drug selection, dosing and the risk of adverse drug events (ADEs).

Results

Results reveal the occurrences of each SNP in the sample and a strong correlation with the European population. Nonlinear principal component analysis strongly indicates co-occurrences of certain variants. The metabolization efficiency (poor, intermediate, extensive, ultra-rapid) and the frequency of clinical useful pharmacogenetic, associations in the population (drug relevance), are also described, along with four exemplar clinical cases illustrating the strong potential of the PGx–CNS panel, as a companion diagnostic assay. It is noted that pharmacogenetic associations involving copy number variations (CNVs) or the HLA gene were not included in this analysis.

Conclusions

Overall, results illustrate that the PGx–CNS panel is a valuable tool supporting therapeutic medical decisions, urging its broad clinical implementation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02816-3.

EZH2-TROAP Pathway Promotes Prostate Cancer Progression Via TWIST Signals

Trophinin-associated protein (TROAP) has been shown to be overexpressed and promotes tumor progression in some tumors. We performed this study to assess the biological and clinical significance of TROAP in prostate cancer. We downloaded TROAP mRNA expression data from TCGA and GEO databases. We analyzed expressions of TROAP and other genes in prostate cancer tumors at different stages and assessed Gleason scores. We used Celigo image, Transwell, and rescue assays, and flow cytometry detection to assess growth, apoptosis, proliferation, migration, and invasion of the prostate cancer cells. We identified and validated up- and down-stream genes in the TROAP pathway. The mRNA data suggested that TROAP expression was markedly upregulated in prostate cancer compared with its expression in normal tissues, especially in cancers with high stages and Gleason scores. Moreover, a high TROAP expression was associated with poor patient survival. Results of our in vitro assay showed that TROAP knockdown inhibited DU145 and PC3 cell proliferation and viability via cell apoptosis and S phase cycle arrest. The Transwell assay showed that TROAP knockdown inhibited cell migration and invasion, probably through MMP-9 and E-Cadherin modulation. Overexpression of TWIST partially abrogated the inhibitory effects of TROAP knockdown on prostate cancer cells. Our integrative mechanism dissection revealed that TROAP is in a pathway downstream of EZH2 and that it activates the TWIST/c-Myc pathway to regulate prostate cancer progression. In all, we identified TROAP as a driver of prostate cancer development and progression, providing a novel target for prostate cancer treatments.