Potential of Immune-Related Genes as Biomarkers for Diagnosis and Subtype Classification of Preeclampsia

Comparison of immune-related gene signatures and immune infiltration features in early- and late-onset preeclampsia

BACKGROUND

Preeclampsia, a severe pregnancy syndrome, is widely accepted divided into early- and late-onset preeclampsia (EOPE and LOPE) based on the onset time of preeclampsia, with distinct pathophysiological origins. However, the molecular mechanism especially immune-related mechanisms for EOPE and LOPE is currently obscure. In the present study, we focused on placental immune alterations between EOPE and LOPE and search for immune-related biomarkers that could potentially serve as potential therapeutic targets through bioinformatic analysis.

METHODS

The gene expression profiling data was obtained from the Gene Expression Omnibus database. ESTIMATE algorithm and Gene Set Enrichment Analysis were employed to evaluate the immune status. The intersection of differentially expressed genes in GSE74341 series and immune-related genes set screened differentially expressed immune-related genes. Protein-protein interaction network and random forest were used to identify hub genes with a validation by a quantitative real-time PCR. Kyoto Encyclopedia of Genes and Genomes pathways, Gene Ontology and gene set variation analysis were utilized to conduct biological function and pathway enrichment analyses. Single-sample gene set enrichment analysis and CIBERSORTx tools were employed to calculate the immune cell infiltration score. Correlation analyses were evaluated by Pearson correlation analysis. Hub genes-miRNA network was performed by the NetworkAnalyst online tool.

RESULTS

Immune score and stromal score were all lower in EOPE samples. The immune system-related gene set was significantly downregulated in EOPE compared to LOPE samples. Four hub differentially expressed immune-related genes (IL15, GZMB, IL1B and CXCL12) were identified based on a protein-protein interaction network and random forest. Quantitative real-time polymerase chain reaction validated the lower expression levels of four hub genes in EOPE compared to LOPE samples. Immune cell infiltration analysis found that innate and adaptive immune cells were apparent lacking in EOPE samples compared to LOPE samples. Cytokine-cytokine receptor, para-inflammation, major histocompatibility complex class I and T cell co-stimulation pathways were significantly deficient and highly correlated with hub genes. We constructed a hub genes-miRNA regulatory network, revealing the correlation between hub genes and hsa-miR-374a-5p, hsa-miR-203a-3p, hsa-miR-128-3p, hsa-miR-155-3p, hsa-miR-129-2-3p and hsa-miR-7-5p.

CONCLUSIONS

The innate and adaptive immune systems were severely impaired in placentas of EOPE. Four immune-related genes (IL15, GZMB, IL1B and CXCL12) were closely correlated with immune-related pathogenesis of EOPE. The result of our study may provide a new basis for discriminating between EOPE and LOPE and acknowledging the role of the immune landscape in the eventual interference and tailored treatment of EOPE.

Demystifying the impact of prenatal tobacco exposure on the placental immune microenvironment: Avoiding the tragedy of mending the fold after death

Prenatal tobacco exposure (PTE) correlates significantly with a surge in adverse pregnancy outcomes, yet its pathological mechanisms remain partially unexplored. This study aims to meticulously examine the repercussions of PTE on placental immune landscapes, employing a coordinated research methodology encompassing bioinformatics, machine learning and animal studies. Concurrently, it aims to screen biomarkers and potential compounds that could sensitively indicate and mitigate placental immune disorders. In the course of this research, two gene expression omnibus (GEO) microarrays, namely GSE27272 and GSE7434, were included. Gene set enrichment analysis (GSEA) and immune enrichment investigations on differentially expressed genes (DEGs) indicated that PTE might perturb numerous innate or adaptive immune-related biological processes. A cohort of 52 immune-associated DEGs was acquired by cross-referencing the DEGs with gene sets derived from the ImmPort database. A protein-protein interaction (PPI) network was subsequently established, from which 10 hub genes were extracted using the maximal clique centrality (MCC) algorithm (JUN, NPY, SST, FLT4, FGF13, HBEGF, NR0B2, AREG, NR1I2, SEMA5B). Moreover, we substantiated the elevated affinity of tobacco reproductive toxicants, specifically nicotine and nitrosamine, with hub genes through molecular docking (JUN, FGF13 and NR1I2). This suggested that these genes could potentially serve as crucial loci for tobacco's influence on the placental immune microenvironment. To further elucidate the immune microenvironment landscape, consistent clustering analysis was conducted, yielding three subtypes, where the abundance of follicular helper T cells (p < 0.05) in subtype A, M2 macrophages (p < 0.01), neutrophils (p < 0.05) in subtype B and CD8+ T cells (p < 0.05), resting NK cells (p < 0.05), M2 macrophages (p < 0.05) in subtype C were significantly different from the control group. Additionally, three pivotal modules, designated as red, blue and green, were identified, each bearing a close association with differentially infiltrated immunocytes, as discerned by the weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis was subsequently conducted on these modules. To further probe into the mechanisms by which immune-associated DEGs are implicated in intercellular communication, 20 genes serving as ligands or receptors and connected to differentially infiltrating immunocytes were isolated. Employing a variety of machine learning techniques, including one-way logistic regression, LASSO regression, random forest and artificial neural networks, we screened 11 signature genes from the intersection of immune-associated DEGs and secretory protein-encoding genes derived from the Human Protein Atlas. Notably, CCL18 and IFNA4 emerged as prospective peripheral blood markers capable of identifying PTE-induced immune disorders. These markers demonstrated impressive predictive power, as indicated by the area under the curve (AUC) of 0.713 (0.548-0.857) and 0.780 (0.618-0.914), respectively. Furthermore, we predicted 34 potential compounds, including cyclosporine, oestrogen and so on, which may engage with hub genes and attenuate immune disorders instigated by PTE. The diagnostic performance of these biomarkers, alongside the interventional effect of cyclosporine, was further corroborated in animal studies via ELISA, Western blot and immunofluorescence assays. In summary, this study identifies a disturbance in the placental immune landscape, a secondary effect of PTE, which may underlie multiple pregnancy complications. Importantly, our research contributes to the noninvasive and timely detection of PTE-induced placental immune disorders, while also offering innovative therapeutic strategies for their treatment.

Association of the LEP gene with immune infiltration as a diagnostic biomarker in preeclampsia

Objective: Preeclampsia (PE) is a serious condition in pregnant women and hence an important topic in obstetrics. The current research aimed to recognize the potential and significant immune-related diagnostic biomarkers for PE. Methods: From the Gene Expression Omnibus (GEO) data sets, three public gene expression profiles (GSE24129, GSE54618, and GSE60438) from the placental samples of PE and normotensive pregnancy were downloaded. Differentially expressed genes (DEGs) were selected and determined among 73 PE and 85 normotensive control pregnancy samples. The DEGs were used for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA). The candidate biomarkers were identified by the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analysis. The receiver operating characteristic curve (ROC) was applied to evaluate diagnostic ability. For further confirmation, the expression levels and diagnostic value of biomarkers in PE were verified in the GSE75010 data set (80 PE and 77 controls) and validated by qRT-RCR, Western blot, and immunohistochemistry (IHC). The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in PE. Results: In total, 15 DEGs were recognized. The GO and KEGG analyses revealed that the DEGs were enriched in the steroid metabolic process, receptor ligand activity, GnRH secretion, and neuroactive ligand-receptor interaction. The recognized DEGs were primarily implicated in cell-type benign neoplasm, kidney failure, infertility, and PE. Gene sets related to hormone activity, glycosylation, multicellular organism process, and response to BMP were activated in PE. The LEP gene was distinguished as a diagnostic biomarker of PE (AUC = 0.712) and further certified in the GSE75010 data set (AUC = 0.850). The high expression of LEP was associated with PE in clinical samples. In addition, the analysis of the immune microenvironment showed that gamma delta T cells, memory B cells, M0 macrophages, and regulatory T cells were positively correlated with LEP expression (P < 0.05). Conclusion: LEP expression can be considered to be a diagnostic biomarker of PE and can offer a novel perspective for future studies regarding the occurrence and molecular mechanisms of PE.

Potential biomarkers for late-onset and term preeclampsia: A scoping review

Preeclampsia is a progressive, multisystem pregnancy disorder. According to the time of onset or delivery, preeclampsia has been subclassified into early-onset (&lt;34 weeks) and late-onset (≥34 weeks), or preterm (&lt;37 weeks) and term (≥37 weeks). Preterm preeclampsia can be effectively predicted at 11–13 weeks well before onset, and its incidence can be reduced by preventively using low-dose aspirin. However, late-onset and term preeclampsia are more prevalent than early forms and still lack effective predictive and preventive measures. This scoping review aims to systematically identify the evidence of predictive biomarkers reported in late-onset and term preeclampsia. This study was conducted based on the guidance of the Joanna Briggs Institute (JBI) methodology for scoping reviews. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews (PRISMA-ScR) was used to guide the study. The following databases were searched for related studies: PubMed, Web of Science, Scopus, and ProQuest. Search terms contain “preeclampsia,” “late-onset,” “term,” “biomarker,” or “marker,” and other synonyms combined as appropriate using the Boolean operators “AND” and “OR.” The search was restricted to articles published in English from 2012 to August 2022. Publications were selected if study participants were pregnant women and biomarkers were detected in maternal blood or urine samples before late-onset or term preeclampsia diagnosis. The search retrieved 4,257 records, of which 125 studies were included in the final assessment. The results demonstrate that no single molecular biomarker presents sufficient clinical sensitivity and specificity for screening late-onset and term preeclampsia. Multivariable models combining maternal risk factors with biochemical and/or biophysical markers generate higher detection rates, but they need more effective biomarkers and validation data for clinical utility. This review proposes that further research into novel biomarkers for late-onset and term preeclampsia is warranted and important to find strategies to predict this complication. Other critical factors to help identify candidate markers should be considered, such as a consensus on defining preeclampsia subtypes, optimal testing time, and sample types.

Identification and validation of an angiogenesis-related signature associated with preeclampsia by bioinformatic analysis

Preeclampsia (PE) is a pregnancy disorder with high morbidity and mortality rates for both mothers and newborns. This study explores potential diagnostic indicators of PE. We downloaded the messenger ribonucleic acid profiles of the GSE75010 dataset from the Gene Expression Omnibus database, and used placenta samples to carry out different analyses including differential expression, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses. Least absolute shrinkage and selection operator regression was constructed and the receiver operating characteristic curve was drawn to evaluate the accuracy of the model. An external validation was conducted to prove the stability of the risk model. We found 140 angiogenesis-related genes and identified 29 angiogenesis-related genes between the 2 groups, including 12 upregulated genes and 17 downregulated genes. In addition, we established a 12-gene risk signature, which has a high accuracy in predicting PE during pregnancy (area under curve = 0.90). The immune infiltration characteristics are differentially distributed in the 2 groups, which may be the cause of hypertension during pregnancy. The external validation with the GSE25906 dataset confirmed the high accuracy of our model (area under curve = 0.87). Our results outline the characteristics of a set of genes potentially involved in PE and its subgroups, contributing to a better understanding of the molecular mechanisms of PE.

Characterizing and correcting immune dysfunction in non-tuberculous mycobacterial disease

Non-tuberculous mycobacterial pulmonary disease (NTM-PD) is a chronic, progressive, and growing worldwide health burden associated with mounting morbidity, mortality, and economic costs. Improvements in NTM-PD management are urgently needed, which requires a better understanding of fundamental immunopathology. Here, we examine temporal dynamics of the immune compartment during NTM-PD caused by Mycobacterium avium complex (MAC) and Mycobactereoides abscessus complex (MABS). We show that active MAC infection is characterized by elevated T cell immunoglobulin and mucin-domain containing-3 expression across multiple T cell subsets. In contrast, active MABS infection was characterized by increased expression of cytotoxic T-lymphocyte-associated protein 4. Patients who failed therapy closely mirrored the healthy individual immune phenotype, with circulating immune network appearing to 'ignore' infection in the lung. Interestingly, immune biosignatures were identified that could inform disease stage and infecting species with high accuracy. Additionally, programmed cell death protein 1 blockade rescued antigen-specific IFN-γ secretion in all disease stages except persistent infection, suggesting the potential to redeploy checkpoint blockade inhibitors for NTM-PD. Collectively, our results provide new insight into species-specific 'immune chatter' occurring during NTM-PD and provide new targets, processes and pathways for diagnostics, prognostics, and treatments needed for this emerging and difficult to treat disease.

Development of a prediction model on preeclampsia using machine learning-based method: a retrospective cohort study in China

Objective: The aim of this study was to use machine learning methods to analyze all available clinical and laboratory data obtained during prenatal screening in early pregnancy to develop predictive models in preeclampsia (PE).

Material and Methods: Data were collected by retrospective medical records review. This study used 5 machine learning algorithms to predict the PE: deep neural network (DNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), and random forest (RF). Our model incorporated 18 variables including maternal characteristics, medical history, prenatal laboratory results, and ultrasound results. The area under the receiver operating curve (AUROC), calibration and discrimination were evaluated by cross-validation.

Results: Compared with other prediction algorithms, the RF model showed the highest accuracy rate. The AUROC of RF model was 0.86 (95% CI 0.80–0.92), the accuracy was 0.74 (95% CI 0.74–0.75), the precision was 0.82 (95% CI 0.79–0.84), the recall rate was 0.42 (95% CI 0.41–0.44), and Brier score was 0.17 (95% CI 0.17–0.17).

Conclusion: The machine learning method in our study automatically identified a set of important predictive features, and produced high predictive performance on the risk of PE from the early pregnancy information.

Ethanol promoting the upregulation of C-X-C Motif Chemokine Ligand 1（CXCL1） and C-X-C Motif Chemokine Ligand 6（CXCL6） in models of early alcoholic liver disease

Alcoholic liver disease (ALD) denotes a series of liver diseases caused by ethanol. Recently, immune-related genes (IRGs) play increasingly crucial role in diseases. However, it’s unclear the role of IRGs in ALD. Bioinformatic analysis was used to discern the core immune-related differential genes (IRDGs) in the present study. Subsequently, Cell Counting Kit-8 say, oil red O staining, and triglyceride detection were employed to explore optimal experimental conditions of establishing hepatocellular models of early ALD. Ultimately, real-time reverse transcription-PCR and immunohistochemistry/immunocytochemistry methods were adopted to verify the expressions of mRNA and proteins of core IRDGs, respectively. C-X-C Motif Chemokine Ligand 1 (Cxcl1) and Cxcl6 were regarded as core IRDGs via integrated bioinformatics analysis. Besides, Lieber Decarli Ethanol feeding and 200 mM and 300 mM ethanol stimulating L02 cells for 36 h can both successfully hepatocellular model. In ethanol groups, the levels of CXCL1 and CXCL6 mRNA were significantly upregulated than pair-fed groups (P < 0.0001). Also, immunohistochemistry revealed that positive particles of CXCL1 and CXCL6 in mice model of early ALD were obviously more than control groups (P < 0.0001). Besides, in L02 hepatocytes stimulated by ethanol, CXCL1 and CXCL6 mRNA were over-expressed, compared with normal L02 cells (P < 0.0001). Meanwhile, immunocytochemistry indicated that CXCL1 and CXCL6 proteins in hepatocellular model of early ALD were higher than normal L02 hepatocytes stimulus (P < 0.0001). Ethanol promoted the upregulation of Cxcl1 and Cxcl6 mRNA and proteins in models of early ALD, denoting their potentiality of acting as biomarkers of ALD.

Identification of Immune-Related Biomarkers for Sciatica in Peripheral Blood

Objective: Sciatica pertains to neuropathic pain that has been associated with inflammatory response. We aimed to identify significant immune-related biomarkers for sciatica in peripheral blood. Methods: We utilized the GSE150408 expression profiling data from the Gene Expression Omnibus (GEO) database as the training dataset and extracted immune-related genes for further analysis. Differentially expressed immune-related genes (DEIRGs) between healthy controls and patients with sciatica were selected using the "limma" package and verified in clinical specimens by quantitative reverse transcription PCR (RT-qPCR). A diagnostic immune-related gene signature was established using the training model and random forest (RF), generalized linear model (GLM), and support vector machine (SVM) models. Sciatica patient subtypes were identified using the consensus clustering method. Results: Thirteen significant DEIRGs were acquired, of which five (CRP, EREG, FAM19A4, RLN1, and WFIKKN1) were selected to establish a diagnostic immune-related gene signature according to the most appropriate training model, namely, the RF model. A clinical application nomogram model was established based on the expression level of the five DEIRGs. The sciatica patients were divided into two subtypes (C1 and C2) according to the consensus clustering method. Conclusions: Our research established a diagnostic five immune-related gene signature to discriminate sciatica and identified two sciatica subtypes, which may be beneficial to the clinical diagnosis and treatment of sciatica.