Identification of crucial genes in abdominal aortic aneurysm by WGCNA

Identification of lncRNAs in peripheral blood mononuclear cells associated with sepsis immunosuppression based on weighted gene co-expression network analysis

BACKGROUND

Sepsis-induced immunosuppression involves complex molecular mechanisms, including dysregulated long noncoding RNAs (lncRNAs), which remain poorly understood.

OBJECTIVE

We aimed to identify immunosuppression-related lncRNAs and their functional pathways in sepsis. Methods: Using weighted gene coexpression network analysis (WGCNA), we analyzed lncRNA profiles from peripheral blood mononuclear cells (PBMCs) of three sepsis patients and three healthy controls. Key modules linked to immunosuppression were validated via RT-PCR and external datasets. Pathway enrichment and protein interaction networks were employed to prioritize mechanisms.

RESULTS

A sepsis-associated module containing 4,193 lncRNAs revealed three immunosuppression-related pathways: Th17 cell differentiation, cytokine-cytokine receptor interactions, and cancer-related proteoglycan signaling. Protein-protein interaction networks identified three central genes (SLFN12, ICOS, IKZF2) and their linked lncRNAs (ENSG00000267074, lnc-ICOSLG-1, lnc-IKZF2-7), all significantly downregulated in sepsis patients.

CONCLUSION

 Our findings highlight novel lncRNA-regulated pathways in sepsis-induced immunosuppression, providing potential targets for improved diagnosis and therapy.

Establishment and validation of an immune-related genes diagnostic model and experimental validation of diagnostic biomarkers for autoimmune thyroiditis based on RNA-seq

BACKGROUND

Autoimmune thyroiditis (AIT), a common autoimmune disease, is a complex disease with an increasing incidence and an unknown pathogenesis that awaits the refinement of diagnostic methods and identification of diagnostic biomarkers to improve screening to identify patients at high risk of AIT earlier and provide the potential effective therapeutic drugs.

PATIENTS AND METHODS

All samples for this study were from a cross-sectional survey, which was conducted among adults in two regions of Anhui Province, China. Ten representative samples (nAIT = 5, nControl = 5) were selected for RNA sequencing to build a training-set in order to identify immune-related differentially expressed genes (IRDEGs), and least absolute shrinkage and selection operator (LASSO) regression analysis was subsequently adopted to screen key IRDEGs and construct a diagnostic model. Then, a test-set was created by downloading AIT transcriptome datasets from the Gene Expression Omnibus (GEO) database. The diagnostic model was systematically evaluated in the training-set and test-set by principal component analysis (PCA) analysis, receiver operating characteristic (ROC) curve and immune infiltration analysis. To identify diagnostic biomarkers, quantitative reverse transcription PCR (RT-qPCR) was used to measure the expression levels of the diagnostic genes in 80 samples. The diagnostic and therapeutic values of the diagnostic genes for AIT were investigated using gene set variation analysis (GSVA), ROC curve, logistic regression analysis and drug docking.

RESULTS

The diagnostic model included three diagnostic genes (FGFR2, CCR1, IL1B). All ROC curves (AUC > 0.7) results suggested that the diagnostic model and the diagnostic genes had reliable predictive power. The results of logistic regression analysis showed that the three diagnostic genes were significant for AIT. The results of GSVA and immunoinfiltration analysis demonstrated that the diagnostic genes have significant negative or positive regulatory effect in immune mechanisms of AIT and the diagnostic model implements immune-related prediction algorithms. Finally, the small molecular compounds (Acetaminophen and Albuterol) were screened as the potential therapeutic drugs for AIT.

CONCLUSION

Using machine learning and bioinformatics techniques, this study developed and validated an AIT diagnostic model, explored the diagnostic model's prediction mechanism, verified three potential diagnostic biomarkers by experiments and predicted two small molecule therapeutic drugs.

Association between exposure to perfluoroalkyl and polyfluoroalkyl substances with estimated glomerular filtration rate: Mediating role of serum albumin

BACKGROUND

Previous studies have demonstrated perfluoroalkyl and polyfluoroalkyl substances (PFAS) impact renal function, with albumin playing dominant role in their transport and accumulation. However, the mediating role of albumin in PFAS-induced renal impairment and the identification of sensitive populations remain uninvestigated.

METHODS

This study included 9328 individuals from NHANES 1999-2018 with data on serum PFAS, creatinine, albumin, and covariates. The estimated glomerular filtration rate (eGFR) was calculated using standardized creatinine. Associations between perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), perfluorohexane sulfonate (PFHxS), and perfluorononanoic acid (PFNA) with eGFR and the risk of decreased renal function (eGFR < 90 vs. eGFR ≥ 90) using linear and logistic regression, weighted quantile sum (WQS) regression, Bayesian kernel machine regression (BKMR), and restricted cubic spline (RCS) analyses. Subgroup analyses identified sensitive populations. Mediation analysis was performed to examine the mediating role of albumin. Comparative toxicology databases identified relevant genes for mechanistic exploration.

RESULTS

Ln-transformed PFOA (β = -1.91, 95 % CI: -2.82 to -1), PFOS (β = -1.48, 95 % CI: -2.19 to -0.78) and PFHxS (β = -0.94, 95 % CI: -1.65 to -0.23) were negatively correlated with eGFR. PFOA (aOR = 1.21, 95 % CI: 1.1-1.32), PFOS (aOR = 1.2, 95 % CI: 1.12-1.29), and PFHxS (aOR = 1.13, 95 % CI: 1.05-1.21) were positively correlated with the risk of decreased renal function. Subgroup analyses indicated that individuals ≤ 45 years, females and other races were more sensitive. Albumin mediated 18.2 %, 16.4 %, 29.8 %, and 18.7 % of the negative effects of PFOA, PFOS, PFHxS, and PFNA on eGFR, respectively. Functional enrichment analysis suggested PFAS impair renal function by affecting lipid metabolism and increasing oxidative stress.

CONCLUSIONS

PFAS exposure is negatively associated with eGFR and positively associated with the risk of decreased renal function, with albumin playing a partial mediating role.

ATG4B as a novel biomarker for abdominal aortic aneurysm: integrated evaluation through experimental and bioinformatics analyses

Autophagy related gene 4B (ATG4B) plays a central role in autophagy machinery, but its clinical relevance to AAA remains unknown. In this study, 205 AAA patients and 205 age- and sex-matched controls were included to detect the serum ATG4B levels. Meanwhile, abdominal aortic specimens from 24 AAA patients and 6 human organ donors were collected to evaluate the mRNA and in situ protein expression of ATG4B. We observed significantly higher ATG4B mRNA and protein expression levels in AAA group compared to those in control group, with a positive correlation between mRNA levels and serum/in situ protein levels (serum, r = 0.518, P = 0.010; in situ, r = 0.453, P = 0.026). Serum ATG4B exhibited the diagnostic potential for AAA (AUC = 0.702, sensitivity = 75.6%) and intraluminal thrombus recognition (AUC = 0.602, sensitivity = 67.9%). Logistic regression revealed a significant association between elevated serum ATG4B and an increased risk of AAA and intraluminal thrombus formation. Deceased patients displayed higher baseline serum ATG4B levels, which could predict postoperative mortality (HR = 1.028, 95%CI = 1.007-1.049, P = 0.009, AUC = 0.612, sensitivity = 84.6%). The bioinformatics analysis suggested that ATG4B may modulate cellular autophagy and influence pathways associated with inflammation, lipid metabolism, or apoptosis, thereby contributing to the occurrence and development of AAA. The drug-gene interaction network identified 13 potential therapeutic drugs targeting ATG4B. In conclusion, ATG4B may serve as a promising biomarker for the diagnosis and prognostic assessment of AAA patients and play a key role in the pathogenesis of AAA.

Bioinformatics data combined with single-cell analysis reveals patterns of immunoinflammatory infiltration and cell death in melanoma

BACKGRUOND

Melanoma is a common cancer in dermatology, but its molecular mechanisms remain poorly explained.

AIM

Utilizing single-cell analytics and bioinformatics, the work sought to discover the immunological infiltration and cellular molecular mechanisms of melanoma.

METHODS

Melanoma genes databases were downloaded from GeneCards, and gene expression profiles were chosen from the Gene Expression Omnibus (GSE244889). Establishing and analyzing protein-protein interaction networks for functional enrichment made use of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. The process assesses the immunological cell infiltration variations between normal and malignant samples by Immune Cell AI software program. Different cell type differences were clarified by cell quality control, filtration, removal of batch effects and cell clustering analysis using single cell analysis techniques.

RESULTS

Using a variety of machine learning techniques, 20 differentially expressed hub genes were found; among these, TP53, HSP90AB1, HSPA4, RHOA, CCND1, CYCS, PPARG, NFKBIA, CAV1, ANXA5, ENO1, ITGAM, YWHAZ, RELA, SOD1, and VDAC1 were found to be significantly significant. The results of enrichment analysis demonstrated that immune response and inflammatory response were strongly associated with melanoma. Animal mitophagy, ferroptosis, the PI3K-Akt signaling pathway, and the HIF-1 signaling pathway were the primary signaling pathways implicated. Cells of immunity, T-cells, lymphocytes, B-cells, NK-cells, monocytes, and macrophages were shown to be significantly infiltrated in melanoma patients, according to analysis. Single cell analysis also demonstrated that ferroptosis is a significant mechanism of cell death that contributes to the advancement of melanoma and that macrophages are important in the disease.

CONCLUSION

In summary, different immune cell infiltrations-particularly macrophages-have a significant impact on the onset and course of melanoma, and our findings may help direct future investigations into melanoma macrophages.

Single-Cell RNA Sequencing, Cell Communication, and Network Pharmacology Reveal the Potential Mechanism of Senecio scandens Buch.-Ham in Hepatocellular Carcinoma Inhibition

BACKGROUND

Hepatocellular carcinoma (HCC), a prevalent form of primary liver malignancy, arises from liver-specific hepatocytes. Senecio scandens Buch.-Ham(Climbing senecio) is a bitter-tasting plant of the Compositae family with anti-tumor properties. This study aims to identify the molecular targets and pathways through which Climbing senecio regulates HCC.

METHODS

Active ingredients of Climbing senecio were collected from four online databases and mapped to relevant target databases to obtain predicted targets. After recognizing the key pathways through which Climbing senecio acts in HCC. Gene expression data from GSE54238 Underwent differential expression and weighted gene correlation network analyses to identify HCC-related genes. The "Climbing senecio-Hepatocellular Carcinoma Targets" network was constructed using Cytoscape 3.10.1 software, followed by topology analysis to identify core genes. The expression and distribution of key targets were evaluated, and the differential expression of each key target between normal and diseased samples was calculated. Moreover, single-cell data from the Gene Expression Omnibus (GSE202642) were used to assess the distribution of Climbing senecio's bioactive targets within major HCC clusters. An intersection analysis of these clusters with pharmacological targets and HCC-related genes identified Climbing senecio's primary targets for this disease. Cell communication, receiver operating characteristic (ROC)analysis, survival analysis, immune filtration analysis, and molecular docking studies were conducted for detailed characterization.

RESULTS

Eleven components of Climbing senecio were identified, along with 520 relevant targets, 300 differentially expressed genes, and 3765 co-expression module genes associated with HCC. AKR1B1, CA2, FOS, CXCL2, SRC, ABCC1, and PLIN1 were identified within the intersection of HCC-related genes and Climbing senecio targets. TGFβ, IL-1, VEGF, and CXCL were identified as significant factors in the onset and progression of HCC. These findings underscore the anti-HCC potential and mode of action of Climbing senecio, providing insights into multi-targeted treatment approaches for HCC.

CONCLUSIONS

This study revealed that Climbing senecio may target multiple pathways and genes in the process of regulating HCC and exert potential drug effects through a multi-target mechanism, which provides a new idea for the treatment of HCC. However, the research is predicated on network database analysis and bioinformatics, offering insights into HCC therapeutic potential while emphasizing the need for further validation.

Diagnostic value of uric acid to high-density lipoprotein cholesterol ratio in abdominal aortic aneurysms

BACKGROUND

Abdominal aortic aneurysm (AAA) is highly lethal upon onset of acute aortic diseases (AAD) or rupture. Dyslipidaemia and hyperuricaemia are important risk factors for the development of AAA and AAD as well as aortic disease-related death. The aim of this study was to explore whether uric acid (UA) to high-density lipoprotein cholesterol (HDL-C) ratio (UHR) can be used as an independent predictor of the presence of AAA or AAD.

METHODS

Three hundred subjects, including 100 AAA patients (AAA group), 100 AAD patients (AAD group) and 100 controls (CON group), were recruited in this study. UHR and other serum samples were obtained upon the patients' admission before any medical treatment. The optimal cut-off points of UHR were determined using receiver operating characteristic (ROC) curve analysis.

RESULTS

The UHR in AAA group was significantly higher than that in CON group, but there was no significant difference between AAD group and CON group. The optimal cut-off point of UHR for AAA was 7.78 (sensitivity 84.7%, specificity 62.4%, and AUC 0.811; p < 0.001), and UHR (OR: 1.122, 95%CI: 1.064-1.184; p < 0.001) was found to be an independent factor for predicting AAA after adjusting for traditional AAA risk factor.

CONCLUSION

UHR can be widely used in clinical practice as an auxiliary tool for screening AAA. The optimal cut-off point for UHR to AAA was determined for the first time in Chinese subjects.

Transcriptomic and Metabolomics Joint Analyses Reveal the Influence of Gene and Metabolite Expression in Blood on the Lactation Performance of Dual-Purpose Cattle (Bos taurus)

Blood is an important component for maintaining animal lives and synthesizing sugars, lipids, and proteins in organs. Revealing the relationship between genes and metabolite expression and milk somatic cell count (SCC), milk fat percentage, milk protein percentage, and lactose percentage in blood is helpful for understanding the molecular regulation mechanism of milk formation. Therefore, we separated the buffy coat and plasma from the blood of Xinjiang Brown cattle (XJBC) and Chinese Simmental cattle (CSC), which exhibit high and low SCC/milk fat percentage/milk protein percentage/lactose percentages, respectively. The expression of genes in blood and the metabolites in plasma was detected via RNA-Seq and LC-MS/MS, respectively. Based on the weighted gene coexpression network analysis (WGCNA) and functional enrichment analysis of differentially expressed genes (DEGs), we further found that the expression of genes in the blood mainly affected the SCC and milk fat percentage. Immune or inflammatory-response-related pathways were involved in the regulation of SCC, milk fat percentage, milk protein percentage, and lactose percentage. The joint analysis of the metabolome and transcriptome further indicated that, in blood, the metabolism pathways of purine, glutathione, glycerophospholipid, glycine, arginine, and proline are also associated with SCC, while lipid metabolism and amino-acid-related metabolism pathways are associated with milk fat percentage and milk protein percentage, respectively. Finally, related SCC, milk fat percentage, and milk protein percentage DEGs and DEMs were mainly identified in the blood.

Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms

Objective

Abdominal aortic aneurysm (AAA) is a life-threatening vascular condition. This study aimed to discover new indicators for the early detection of AAA and explore the possible involvement of immune cell activity in its development.

Methods

Sourced from the Gene Expression Omnibus, the AAA microarray datasets GSE47472 and GSE57691 were combined to generate the training set. Additionally, a separate dataset (GSE7084) was designated as the validation set. Enrichment analyses were carried out to explore the underlying biological mechanisms using Disease Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology. We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. Finally, the single sample gene set enrichment analysis algorithm was applied to probe the immune landscape in AAA and its connection to the selected feature genes.

Results

We discovered 72 differentially expressed genes (DEGs) when comparing healthy and AAA samples, including 36 upregulated and 36 downregulated genes. Functional enrichment analysis revealed that the DEGs associated with AAA are primarily involved in inflammatory regulation and immune response. By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. The diagnostic performance of all these genes was strong, as revealed by the ROC analysis. A significant increase in 15 immune cell types in AAA samples was observed, based on the analysis of immune cell infiltration. In addition, the 3 feature genes show a strong linkage with different types of immune cells.

Conclusion

Three feature genes (MRAP2, PPP1R14A, and PLN) related to the development of AAA were identified. These genes are linked to immune cell activity and the inflammatory microenvironment, providing potential biomarkers for early detection and a basis for further research into AAA progression.

DUSP6 protein action and related hub genes prevention of sepsis-induced lung injury were screened by WGCNA and Venn

During a sepsis infection, the lung is extremely susceptible to damage. A condition known as acute respiratory distress syndrome (ARDS) may develop in extreme circumstances. The primary objective of this research is to identify important genes that are related with both sepsis and lung injury. These genes have the potential to act as novel biomarkers in the investigation of sepsis-induced lung injury prevention strategies. It was possible to download from GEO data both the sepsis-related dataset (GSE64457) and the lung injury-related dataset (GSE40839). In the GSE64457 dataset, using the "limma" package in R revealed 429 differentially expressed genes (DEGs) with logFC values more than or equal to -1 and p values <0.05. There were 266 genes that were up-regulated and 163 genes that were down-regulated. Through the use of Gene Ontology (GO), it was discovered that the majority of the DEGs were associated with the inflammatory response (BP terms), a particular granule lumen (CC terms), and protein binding (MF terms). By doing a pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG), researchers were able to identify DEGs that were mostly associated with the NOD-like receptor signalling pathway, the TNF signalling pathway, and Epstein-Barr virus infection. Within the GSE40839 dataset, Weighted Gene Co-Expression Network Analysis (WGCNA) yielded a total of 7 modules, from which it was possible to screen out 2 critical modules and 693 key genes. The important genes and DEGs were both subjected to a Venn analysis. Finally, 14 genes that overlapped (ARL4A, LAIR1, MTHFD2, TSPAN13, DUSP6, PECR, CBS, TES, ASNS, SYNE1, FGF13, LCN2, KLF10, BCAT1) were closely associated to the incidence and development of sepsis-induced lung injury. This indicates that these genes are the essential genes to avoid the occurrence of sepsis-induced lung injury. This study provides novel strategies for preventing lung harm brought on by sepsis.

Identifying the signature of NAD+ metabolism-related genes for immunotherapy of gastric cancer

NAD (Nicotinamide Adenine Dinucleotide) -related metabolic reprogramming in tumor cells involves multiple vital cellular processes. However, the role of NAD metabolism in immunity and the prognosis of gastric cancer (GC) remains not elucidated. Here we identified and clustered 33 NAD + metabolism-related genes (NMRGs) based on 808 GC samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Survival analysis between different groups found a poor prognosis in the GC patients with high NMRGs expression. Gene SGCE, APOD, and PPP1R14A were identified and performed high expression in GC samples, while the qRT-PCR results further confirmed that their expression levels in GC cell lines were significantly higher than those from normal human gastric mucosa epithelial cells. Based on the single-cell analysis, Gene SGCE, APOD, and PPP1R14A can potentially be novel biomarkers of tumor-associated fibroblasts (CAFs). In parallel, the proliferation and migration of GC cells were significantly hampered following the knockdown of SGCE, APOD, and PPP1R14A, particularly APOD, we confirmed that APOD knockdown can inhibit β-catenin and N-cadherin expression, while promote E-cadherin expression. This study unveils a novel NMRGs-related gene signature, highlighting APOD as a prognostic biomarker linked to the tumor microenvironment. APOD drives GC cell proliferation and metastasis through the Wnt/β-catenin/EMT signaling pathway, establishing it as a promising therapeutic target for GC patients.

Identification of m6A modification patterns and RBM15 mediated macrophage phagocytosis in pancreatic cancer: An integrative analysis

Pancreatic cancer (PC) responds weakly to conventional immunotherapy. RNA N6-methyladenosine (m6A) modification has an essential role in the immune response, while its potential role in PC tumor microenvironment (TME) immune cell infiltration remains unknown. In this study, we thoroughly assessed the m6A modification patterns of 472 PC samples using 19 m6A regulators, and we systematically correlated these modification patterns with TME immune cell infiltration characteristics. We also created the m6Ascore and evaluated the m6A modification patterns of individual tumors, identified three different m6A modification patterns, and explored the role of the important m6A "writer" RBM15 in the regulation of macrophage function in PC. Two independent PC cohorts confirmed that patients with higher m6Ascore showed significant survival benefit. We verified that knockdown of RBM15 has the ability to inhibit PC growth and to promote macrophage infiltration and enhance phagocytosis of PC cells by macrophages. In conclusion, m6A modifications play a non-negligible role in the formation of TME diversity and complexity in PC. We reveal that inhibition of RBM15 suppresses PC development and modulates macrophage phagocytosis, and provide a more effective immunotherapeutic strategy for PC.

ERP29 regulates the proliferation of endometrial carcinoma via M6A modification

AIMS

Endoplasmic reticulum protein 29 (ERP29) is crucial for endoplasmic reticulum stress (ERS). M6A plays an important role in the progression of endometrial cancer (EC). The study investigated the role of ERS-related gene (ERP29) and m6A in EC.

MATERIALS AND METHODS

We screened ERS-related genes based on the GEO dataset, GSEA dataset and TCGA-UCEC database using WGCNA and two machine learning algorithms. The m6A-related GEO dataset was employed to identify the ERS-related hub genes with m6A. Expression of hub genes in different cell types were visualize through scRNA-seq data analyzing. Using qPCR, Western blot, and Immunohistochemical assays to detect the expression of ERP29, the effect of ERP29 on cancer cell proliferation was investigated through CCK8, EdU and clone formation experiments. M6A modifications were studied using m6A Dot blot and MeRIP-qPCR. Finally, we conducted rescue experiments.

KEY FINDINGS

Ten ERS-related hub genes with m6A were identified. ERP29 is highly expressed in EC. ERP29 knockdown inhibits EC cell proliferation. METTL3 overexpression increases the ERP29 mRNA m6A and decreases the expression of ERP29. Cycloleucine (Cyc), a nucleic acid methylation inhibitor, treatment reduces ERP29 mRNA m6A and increases the expression of ERP29. Cyc rescue the low expression of ERP29 caused by overexpression of METTL3 through m6A. ERP29 knockdown rescued the increased proliferation of EC cells caused by low m6A.

SIGNIFICANCE

ERP29 is highly expressed in EC. m6A regulates ERP29 expression and affects the proliferation of endometrial cancer cells. This represents the premise for applying ERP29 and m6A modifications in diagnosing and treating EC.

Structure of POU2AF1 recombinant protein and it affects the progression and treatment of liver cancer based on WGCNA and molecular docking analysis

Hepatocellular carcinoma, also referred to as HCC, is the most frequent form of primary liver cancer. It is anticipated that the discovery of the molecular pathways related with HCC would open up new possibilities for the treatment of HCC.WGCNA (Weighted gene co-expression network analysis) and molecular docking analysis were used to study the structural characteristics of POU2AF1 recombinant protein and its interaction with related proteins. Normal samples were placed in one group, and tumor samples were placed in another group inside the GEO database. We continued our investigation of the DEGs by performing an enrichment analysis using GO and KEGG. The GSCA platform is utilized in the process of doing an analysis of the connection between gene expression and medication sensitivity. In the end, the core target and the active molecule were both given the green light for a molecular docking investigation. POU2AF1 is being considered as a possible therapeutic target for HCC, and the results of our work have presented novel concepts for the treatment of HCC.

Polycystic ovarian syndrome (PCOS) and recurrent spontaneous abortion (RSA) are associated with the PI3K-AKT pathway activation

Aims

We aimed to elucidate the mechanism leading to polycystic ovarian syndrome (PCOS) and recurrent spontaneous abortion (RSA).

Background

PCOS is an endocrine disorder. Patients with RSA also have a high incidence rate of PCOS, implying that PCOS and RSA may share the same pathological mechanism.

Objective

The single-cell RNA-seq datasets of PCOS (GSE168404 and GSE193123) and RSA GSE113790 and GSE178535) were downloaded from the Gene Expression Omnibus (GEO) database.

Methods

Datasets of PSCO and RSA patients were retrieved from the Gene Expression Omnibus (GEO) database. The "WGCNA" package was used to determine the module eigengenes associated with the PCOS and RSA phenotypes and the gene functions were analyzed using the "DAVID" database. The GSEA analysis was performed in "clusterProfiler" package, and key genes in the activated pathways were identified using the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Real-time quantitative PCR (RT-qPCR) was conducted to determine the mRNA level. Cell viability and apoptosis were measured by cell counting kit-8 (CCK-8) and flow cytometry, respectively.

Results

The modules related to PCOS and RSA were sectioned by weighted gene co-expression network analysis (WGCNA) and positive correlation modules of PCOS and RSA were all enriched in angiogenesis and Wnt pathways. The GSEA further revealed that these biological processes of angiogenesis, Wnt and regulation of cell cycle were significantly positively correlated with the PCOS and RSA phenotypes. The intersection of the positive correlation modules of PCOS and RSA contained 80 key genes, which were mainly enriched in kinase-related signal pathways and were significant high-expressed in the disease samples. Subsequently, visualization of these genes including PDGFC, GHR, PRLR and ITGA3 showed that these genes were associated with the PI3K-AKT signal pathway. Moreover, the experimental results showed that PRLR had a higher expression in KGN cells, and that knocking PRLR down suppressed cell viability and promoted apoptosis of KGN cells.

Conclusion

This study revealed the common pathological mechanisms between PCOS and RSA and explored the role of the PI3K-AKT signaling pathway in the two diseases, providing a new direction for the clinical treatment of PCOS and RSA.

Burden of aortic aneurysm in Iran from 1990 to 2019: an analysis based on global burden of disease study

Background

Limited studies have been conducted on the epidemiology of aortic aneurysms in Iran. In this study, the authors aimed to comprehensively evaluate the burden and epidemiology of aortic aneurysms across various age groups, sexes, and provinces in Iran from 1990 to 2019.

Materials and methods

The authors used global burden of disease (GBD) study data in the current study. The authors retrieved data on the burden of aortic aneurysms across sexes and age groups at national and subnational levels from 1990 to 2019. The authors extracted mortality, years of life lost (YLL), years lived with disability (YLD), and disability-adjusted life year (DALY), numbers, rates, and age-standardized rates. Additionally, the authors extracted the burden of AA attributable to its risk factors, such as lead exposure, high systolic blood pressure, high dietary intake of sodium, and smoking.

Results

National ASDR (age-standardized DALY rate) was also observed to be reduced from 1990 [22.20 (17.46-26.86)] to 2019 [19.97 (17.98-21.98)]. However, the inclinations were evaluated to be slighter than the world (%change ASDR=-19.5%). In 2019, three leading risk factors yielding death in AA patients were smoking [ASMR attributable=0.73 (0.67-0.80)], high sodium intake [ASMR attributable=0.11 (0.03-0.27)], and lead exposure [ASMR attributable=0.04 (0.02-0.07)].

Conclusion

Mortality and DALYs due to AA both decreased slightly during the last three decades. Considering the role of smoking in the burden of AA in Iran, interventions targeting smoking cessation among high-risk groups, such as males, may be helpful to reduce the burden of AA in Iran in the coming years.

Revealing the potential role of hub metabolism-related genes and their correlation with immune cells in acute ischemic stroke

OBJECTIVES

Acute ischemic stroke (AIS) is caused by cerebral ischemia due to thrombosis in the blood vessel. The purpose of this study is to identify key genes related to metabolism to aid in the mechanism research and management of AIS.

MATERIALS AND METHODS

Gene expression data were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis, Gene Ontology and kyoto encyclopedia of genes and genomes analysis were used to identify metabolism-related genes that may be involved in the regulation of AIS. A protein protein interaction network was mapped using Cytoscape based on the STRING database. Subsequently, hub metabolism-related genes were identified based on Cytoscape-CytoNCA and Cytoscape-MCODE plug-ins. Least absolute shrinkage and selection operator algorithm and differential expression analysis. In addition, drug prediction, molecular docking, ceRNA network construction, and correlation analysis with immune cell infiltration were performed to explore their potential molecular mechanisms of action in AIS. Finally, the expression of hub gene was verified by real-time PCR.

RESULTS

Metabolism-related genes FBL, HEATR1, HSPA8, MTMR4, NDUFC1, NDUFS8 and SNU13 were identified. The AUC values of FBL, HEATR1, HSPA8, MTMR4, NDUFS8 and SNU13 were all greater than 0.8, suggesting that they had good diagnostic accuracy. Correlation analysis found that their expression levels were also related to the infiltration levels of multiple immune cells, such as Activated.CD8.T.cell and Activated.dendritic.cell. It was found that only HSPA8 was successfully matched to drugs with literature support, and these drugs were acetaminophen, bupivacaine, dexamethasone, gentamicin, tretinoin and cisplatin. Moreover, it was also identified that the ENSG000000218510-hsa-miR-330-3p-HEATR1 axis may be involved in regulating AIS.

CONCLUSIONS

The identification of FBL, HEATR1, HSPA8, MTMR4, NDUFC1, NDUFS8 and SNU13 provides a new research direction for exploring the molecular mechanisms of AIS, which can help in clinical management and diagnosis.

Identification of signature genes and immune infiltration analysis in thyroid cancer based on PANoptosis related genes

Background

Thyroid cancer is the most common malignancy of the endocrine system. PANoptosis is a specific form of inflammatory cell death. It mainly includes pyroptosis, apoptosis and necrotic apoptosis. There is increasing evidence that PANoptosis plays a crucial role in tumour development. However, no pathogenic mechanism associated with PANoptosis in thyroid cancer has been identified.

Methods

Based on the currently identified PANoptosis genes, a dataset of thyroid cancer patients from the GEO database was analysed. To screen the common differentially expressed genes of thyroid cancer and PANoptosis. To analyse the functional characteristics of PANoptosis-related genes (PRGs) and screen key expression pathways. The prognostic model was established by LASSO regression and key genes were identified. The association between hub genes and immune cells was evaluated based on the CIBERSORT algorithm. Predictive models were validated by validation datasets, immunohistochemistry as well as drug-gene interactions were explored.

Results

The results showed that eight key genes (NUAK2, TNFRSF10B, TNFRSF10C, TNFRSF12A, UNC5B, and PMAIP1) exhibited good diagnostic performance in differentiating between thyroid cancer patients and controls. These key genes were associated with macrophages, CD4+ T cells and neutrophils. In addition, PRGs were mainly enriched in the immunomodulatory pathway and TNF signalling pathway. The predictive performance of the model was confirmed in the validation dataset. The DGIdb database reveals 36 potential therapeutic target drugs for thyroid cancer.

Conclusion

Our study suggests that PANoptosis may be involved in immune dysregulation in thyroid cancer by regulating macrophages, CD4+ T cells and activated T and B cells and TNF signalling pathways. This study suggests potential targets and mechanisms for thyroid cancer development.

Transcriptome meta-analysis reveals the hair genetic rules in six animal breeds and genes associated with wool fineness

Wool plays an irreplaceable role in the lives of livestock and the textile industry. The variety of hair quality and shape leads to the diversity of its functions and applications, and the finer wool has a higher economic value. In this study, 10 coarse and 10 fine ordos fine wool sheep skin samples were collected for RNA-seq, and coarse and fine skin/hair follicle RNA-seq datasets of other five animal breeds were obtained from NCBI. Weighted gene co-expression network analysis showed that the common genes were clustered into eight modules. Similar gene expression patterns in sheep and rabbits with the same wool types, different gene expression patterns in animal species with different hair types, and brown modules were significantly correlated with species and breeds. GO and KEGG enrichment analyses showed that, most genes in the brown module associated with hair follicle development. Hence, gene expression patterns in skin tissues may determine hair morphology in animal. The analysis of differentially expressed genes revealed that 32 highly expressed candidate genes associated with the wool fineness of Ordos fine wool sheep. Among them, KAZALD1 (grey module), MYOC (brown module), C1QTNF6 (brown module), FOS (tan module), ITGAM, MX2, MX1, and IFI6 genes have been reported to be involved in the regulation of the hair follicle cycle or hair loss. Additionally, 12 genes, including KAZALD1, MYOC, C1QTNF6, and FOS, are differentially expressed across various animal breeds and species. The above results suggest that different sheep breeds share a similar molecular regulatory basis of wool fineness. Finally, the study provides a theoretical reference for molecular breeding of sheep breeds as well as for the investigation of the origin and evolution of animal hair.

Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets

BACKGROUND

Lupus nephritis (LN) is a complication of SLE characterised by immune dysfunction and oxidative stress (OS). Limited options exist for LN. We aimed to identify LN-related OS, highlighting the need for non-invasive diagnostic and therapeutic approaches.

METHODS

LN-differentially expressed genes (DEGs) were extracted from Gene Expression Omnibus datasets (GSE32591, GSE112943 and GSE104948) and Molecular Signatures Database for OS-associated DEGs (OSEGs). Functional enrichment analysis was performed for OSEGs related to LN. Weighted gene co-expression network analysis identified hub genes related to OS-LN. These hub OSEGs were refined as biomarker candidates via least absolute shrinkage and selection operator. The predictive value was validated using receiver operating characteristic (ROC) curves and nomogram for LN prognosis. We evaluated LN immune cell infiltration using single-sample gene set enrichment analysis and CIBERSORT. Additionally, gene set enrichment analysis explored the functional enrichment of hub OSEGs in LN.

RESULTS

The study identified four hub genes, namely STAT1, PRODH, TXN2 and SETX, associated with OS related to LN. These genes were validated for their diagnostic potential, and their involvement in LN pathogenesis was elucidated through ROC and nomogram. Additionally, alterations in immune cell composition in LN correlated with hub OSEG expression were observed. Immunohistochemical analysis reveals that the hub gene is most correlated with activated B cells and CD8 T cells. Finally, we uncovered that the enriched pathways of OSEGs were mainly involved in the PI3K-Akt pathway and the Janus kinase-signal transducer and activator of transcription pathway.

CONCLUSION

These findings contribute to advancing our understanding of the complex interplay between OS, immune dysregulation and molecular pathways in LN, laying a foundation for the identification of potential diagnostic biomarkers and therapeutic targets.

Weighted gene co-expression network analysis reveals immune evasion related genes in Echinococcus granulosus sensu stricto

Cystic echinococcosis (CE) is a zoonotic disease caused by the tapeworm Echinococcus granulosus sensu lato (s.l). In the intermediate host, this disease is characterized by the growth of cysts in viscera such as liver and lungs, inside of which the parasite develops to the next infective stage known as protoscoleces. There are records that the infected viscera affect the development and morphology of E. granulosus s.l. protoscolex in hosts such as buffalo or humans. However, the molecular mechanisms that drive these differences remains unknown. Weighted gene co-expression network analysis (WGCNA) using a set of RNAseq data obtained from E. granulosus sensu stricto (s.s.) protoscoleces found in liver and lung cysts reveals 34 modules in protoscoleces of liver origin, of which 12 have differential co-expression from protoscoleces of lung origin. Three of these twelve modules contain hub genes related to immune evasion: tegument antigen, tegumental protein, ubiquitin hydrolase isozyme L3, COP9 signalosome complex subunit 3, tetraspanin CD9 antigen, and the methyl-CpG-binding protein Mbd2. Also, two of the twelve modules contain only hypothetical proteins with unknown orthology, which means that there are a group of unknown function proteins co-expressed inside the protoscolex of liver CE cyst origin. This is the first evidence of gene expression differences in protoscoleces from CE cysts found in different viscera, with co-expression networks that are exclusive to protoscoleces from liver CE cyst samples. This should be considered in the control strategies of CE, as intermediate hosts can harbor CE cysts in liver, lungs, or both organs simultaneously.

Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge.

A study on the role of Taxifolin in inducing apoptosis of pancreatic cancer cells: screening results using weighted gene co-expression network analysis

Pancreatic adenocarcinoma (PAAD) is a frequent malignant tumor in the pancreas. The incomplete understanding of cancer etiology and pathogenesis, as well as the limitations in early detection and diagnostic methods, have created an urgent need for the discovery of new therapeutic targets and drugs to control this disease. As a result, the current therapeutic options are limited. In this study, the weighted gene co-expression network analysis (WGCNA) method was employed to identify key genes associated with the progression and prognosis of pancreatic adenocarcinoma (PAAD) patients in the Gene Expression Profiling Interactive Analysis (GEPIA) database. To identify small molecule drugs with potential in the treatment of pancreatic adenocarcinoma (PAAD), we compared key genes to the reference dataset in the CMAP database. First, we analyzed the antitumor properties of small molecule drugs using cell counting kit-8 (CCK-8), AO/EB and Transwell assays. Subsequently, we integrated network pharmacology with molecular docking to explore the potential mechanisms of the identified molecules' anti-tumor effects. Our findings indicated that the progression and prognosis of PAAD patients in pancreatic cancer were associated with 11 genes, namely, DKK1, S100A2, CDA, KRT6A, ITGA3, GPR87, IL20RB, ZBED2, PMEPA1, CST6, and MUC16. These genes were filtered based on their therapeutic potential through comparing them with the reference dataset in the CMAP database. Taxifolin, a natural small molecule drug with the potential for treating PAAD, was screened by comparing it with the reference dataset in the CMAP database. Cell-based experiments have validated the potential of Taxifolin to facilitate apoptosis in pancreatic cancer cells while restraining their invasion and metastasis. This outcome is believed to be achieved via the HIF-1 signaling pathway. In conclusion, this study provided a theoretical basis for screening genes related to the progression of pancreatic cancer and discovered potentially active small molecule drugs. The experimental results confirm that Taxifolin has the ability to promote apoptosis in pancreatic cancer cells.

Immunogenic profiling of metastatic uveal melanoma discerns a potential signature related to prognosis

BACKGROUND

Uveal melanoma (UM) is an aggressive intraocular malignant tumor. The present study aimed to identify the key genes associated with UM metastasis and established a gene signature to analyze the relationship between the signature and prognosis and immune cell infiltration. Later, a predictive model combined with clinical variables was developed and validated.

METHODS

Two UM gene expression profile chip datasets were downloaded from TCGA and GEO databases. Immune-related genes (IRGs) were obtained from IMPORT database. First, these mRNAs were intersected with IRGs, and weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression of genes primarily associated with metastasis of UM. Univariate Cox regression analysis screened the genes related to prognosis. LASSO-Cox established a risk score to distinguish high-risk group and low-risk group. Then the GSEA enrichment pathway and immune cell infiltration of the two groups were compared. And combined with clinical variables, a predictive model was constructed. The time-dependent receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve were used to verify the stability and accuracy of the final predictive model, and a nomogram was then drawn.

RESULTS

The MEblack, MEpurple, and MEblue modules were significantly associated with the metastasis of UM patients (P value < 0.001, = 0.001, = 0.022, respectively). Four genes (UBXN2B, OTUD3, KAT8, LAMTOR2) were obtained by Pearson correlation analysis, weighted gene correlation network analysis (WGCNA), univariate Cox, and LASSO-Cox. And a novel prognostic risk score was established. Immune-related prognostic signature can well classify UM patients into high-risk and low-risk groups. Kaplan-Meier curve showed that the OS of high-risk patients was worse than that of low-risk patients. In addition, the risk score played an important role in evaluating the signaling pathway and immune cell infiltration of UM patients in high-risk and low-risk groups. Both the training set and validation set of the model showed good predictive accuracy in the degree of differentiation and calibration (e.g., 1-year overall survival: AUC = 0.930 (0.857-1.003)). Finally, a nomogram was established to serve in clinical practice.

SIGNIFICANCE

UM key gene signature and prognosis predictive model might provide insights for further investigation of the pathogenesis and development of UM at the molecular level, and provide theoretical basis for determining new prognostic markers of UM and immunotherapy.

Identification of CD8+ T cell-related biomarkers and immune infiltration characteristic of rheumatoid arthritis

Rheumatoid arthritis (RA) is an autoimmune rheumatic disease, which do not respond well to current treatment partially. Therefore, further in-depth elucidation of the molecular mechanism and pathogenesis of RA is urgently needed for the diagnosis, personalized therapy and drug development. Herein, we collected 111 RA samples from Gene Expression Omnibus (GEO) database, and conducted differentially expressed genes and GESA analysis. Abnormal activation and imbalance of immune cells in RA were observed. WGCNA was utilized to explore the gene modules and CD8+ T cell-related genes (CRGs) were chosen for KEGG and GO analysis. Besides, to explore biomarkers of RA in depth, machine learning algorithms and bioinformatics analysis were used, and we identified GDF15, IGLC1, and IGHM as diagnostic markers of RA, which was confirmed by clinical samples. Next, ssGSEA algorithms were adopted to investigate the differences in immune infiltration of 23 immune cell subsets between RA and healthy control group. Finally, optimal classification analysis based on consensus clustering combined with ssGSEA algorithms were conducted. GDF15 was revealed that to be positively correlated with mast cells and type 2 T helper cells, but negatively correlated with most other immune cells. On the other hand, IGHM and IGLC1 were negatively correlated with CD56dim natural killer cells, while positively associated with other immune cells. Finally, RA samples in subtype A exhibited a higher immune infiltration status. This study could provide guidance for individualized treatment of RA patients and provide new targets for drug design.

The association between atherosclerosis and nonalcoholic fatty liver disease

Atherosclerosis (AS) is closely related to nonalcoholic fatty liver disease (NAFLD), which promotes and exacerbates the development of AS. However, it is uncertain how the precise underlying mechanism occurs. Here, we attempted to further explore the association underlying atherosclerosis and nonalcoholic fatty liver disease through integrated bioinformatics analysis. Microarray data for atherosclerosis and nonalcoholic fatty liver disease were retrieved from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to identify the genes related to atherosclerosis and nonalcoholic fatty liver disease showing co-expression. Additionally, the common gene targets associated with atherosclerosis and nonalcoholic fatty liver disease were also analyzed and screened using data from 3 public databases [comparative toxicogenomics database (CTD), DISEASES, and GeneCards]. The Gene Ontology (GO) enrichment analysis and the Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis were performed using Metascape R, respectively. The protein-protein interaction networks (PPI) network was constructed using Cytoscape. According to the results of an analysis of common genes, matrix metalloproteinase 9 (MMP9) is co-expressed up-regulated in AS and NAFLD and is enriched in inflammatory and immune-related collaterals. Consequently, MMP9 may work together through immunity and inflammation to treat AS and NAFLD and may be a potential therapeutic target in the future. The findings of this study provide new insights into the shared association between AS and NAFLD. MMP9 is co-expressed up-regulated in AS and NAFLD, which be able to reveal the presence of co-expressed genes in atherosclerosis and NAFLD.

The Indispensable Roles of GMDS and GMDS-AS1 in the Advancement of Cancer: Fucosylation, Signal Pathway and Molecular Pathogenesis

Fucosylation is facilitated by converting GDP-mannose to GDP-4-keto-6-deoxymannose, which GDP-mannose 4,6-dehydratase, a crucial enzyme in the route, carries out. One of the most prevalent glycosylation alterations linked to cancer has reportedly been identified as fucosylation. There is mounting evidence that GMDS is intimately linked to the onset and spread of cancer. Furthermore, the significance of long-chain non-coding RNAs in the development and metastasis of cancer is becoming more well-recognized, and the regulatory mechanism of lncRNAs has emerged as a prominent area of study in the biological sciences. GMDS-AS1, an antisense RNA of GMDS, was discovered to have the potential to be an oncogene. We have acquired and analyzed relevant data to understand better how GMDS-AS1 and its lncRNA work physiologically and in tumorigenesis and progression. Additionally, we have looked into the possible effects of these molecules on cancer treatment approaches and patient outcomes. The physiological roles and putative processes of GMDS and lncRNA GMDS-AS1 throughout the development and progression of tumors have been assembled and examined. We also examined how these chemicals might affect patient prognosis and cancer therapy approaches. GMDS and GMDS-AS1 were determined to be research subjects by searching and gathering pertinent studies using the PubMed system. The analysis of these research articles demonstrated the close relationship between GMDS and GMDS-AS1 and tumorigenesis and the factors that influence them. GMDS plays a vital role in regulating fucosylation. The related antisense gene GMDS-AS1 affects the biological behaviors of cancer cells through multiple pathways, including the key processes of proliferation, migration, invasion, and apoptosis, providing potential biomarkers and therapeutic targets for cancer treatment and prognosis assessment.

Weighted Gene Co-Expression Network Analysis Based on Stimulation by Lipopolysaccharides and Polyinosinic:polycytidylic Acid Provides a Core Set of Genes for Understanding Hemolymph Immune Response Mechanisms of Amphioctopus fangsiao

The primary influencer of aquaculture quality in Amphioctopus fangsiao is pathogen infection. Both lipopolysaccharides (LPS) and polyinosinic:polycytidylic acid (Poly I:C) are recognized by the pattern recognition receptor (PRR) within immune cells, a system that frequently serves to emulate pathogen invasion. Hemolymph, which functions as a transport mechanism for immune cells, offers vital transcriptome information when A. fangsiao is exposed to pathogens, thereby contributing to our comprehension of the species' immune biological mechanisms. In this study, we conducted analyses of transcript profiles under the influence of LPS and Poly I:C within a 24 h period. Concurrently, we developed a Weighted Gene Co-expression Network Analysis (WGCNA) to identify key modules and genes. Further, we carried out Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to investigate the primary modular functions. Co-expression network analyses unveiled a series of immune response processes following pathogen stress, identifying several key modules and hub genes, including PKMYT1 and NAMPT. The invaluable genetic resources provided by our results aid our understanding of the immune response in A. fangsiao hemolymph and will further our exploration of the molecular mechanisms of pathogen infection in mollusks.

Impact of sarcosine on diabetic retinopathy: Findings based on weighted gene co-expression network analysis and machine learning techniques

AIM

To quantify the association between serum sarcosine and diabetic retinopathy (DR) using weighted gene co-expression network analysis (WGCNA).

METHODS

We measured serum metabolites in 69 pairs of type 2 diabetes (T2D) patients with and without DR matched by age, gender, body mass index（BMI and HbA1c, using a propensity score matching-based approach. To identify modules and metabolites linked to DR, pathway analysis was performed using WGCNA, the Kyoto Encyclopedia of Genes and Genomes and Small-Molecule Pathway Database. The association of sarcosine with DR was estimated by restricted cubic spline and conditional logistic regression models. Its joint effects with covariates on DR were also extensively examined.

RESULTS

With per interquartile range elevation of sarcosine, the adjusted odds ratio (AOR) of DR significantly decreased by 67% (AOR: 0.33, 95% confidence interval [CI]: 0.19-0.58). Similar results were also found in the tertile analysis. Compared with those in the first tertile of sarcosine, the AOR significantly decreased by 54% (AOR: 0.46, 95% CI: 0.18-1.17) and 78% (AOR: 0.22, 95% CI: 0.08-0.59) for subjects in the second and third tertiles, respectively. Compared with subjects with lower sarcosine and lower HDL-C levels, those with higher sarcosine and lower HDL-C levels had the lowest odds of DR (OR: 0.13, 95% CI: 0.04, 0.43).

CONCLUSIONS

Serum sarcosine was inversely related to DR, especially in T2D patients with insufficient HDL-C. This study provides insights on a possible novel target for DR precision prevention and control, as well as a better understanding of the DR mechanism.

Comprehensive bioinformatics analysis revealed potential key genes and pathways underlying abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is a permanent, asymptomatic segmental dilatation of the abdominal aorta, with a high mortality risk upon rupture. Identification of potential key genes and pathways may help to develop curative drugs for AAA. We conducted RNA-seq on abdominal aortic tissues from both AAA patients and normal individuals as a control group. Integrated bioinformatic analysis was subsequently performed to comprehensively reveal potential key genes and pathways. A total of 1148 differential expressed genes (DEGs) (631 up-regulated and 517 down-regulated) were identified in our study. Gene Ontology (GO) analysis revealed enrichment in terms related to extracellular matrix organization, while KEGG analysis indicated enrichment in hematopoietic cell lineage and ECM-receptor interaction. Protein-protein interaction (PPI) network analysis revealed several candidate key genes, and differential expression of 6 key genes (CXCL8, CCL2, PTGS2, SELL, CCR7, and CXCL1) was validated by Gene Expression Omnibus (GEO) datasets. Receiver operating characteristic curve (ROC) analysis demonstrated these genes' high discriminatory ability between AAA and normal tissues. Immunohistochemistry indicated that several key genes were highly expressed in AAA tissues. Single-cell RNA sequencing revealed differential distribution patterns of these identified key genes among various cell types. 26 potential drugs linked to our key genes were found through DGIdb. Overall, our study provides a comprehensive evaluation of potential key genes and pathways in AAA, which could pave the way for the development of curative pharmacological therapies.

COL3A1, COL5A1 and COL6A2 serve as potential molecular biomarkers for osteoarthritis based on weighted gene co‑expression network analysis bioinformatics analysis

Osteoarthritis (OA) is a non-inflammatory degenerative joint disease, characterized by joint pain and stiffness. The prevalence of OA increases with age. However, the relationship between biomarkers [collagen type III α1 (COL3A1), COL5A1, COL6A2, COL12A1] and OA remains unclear. The OA subchondral bone dataset GSE51588 was downloaded from the GEO database, and the differentially expressed genes (DEGs) were screened. Weighted gene co-expression network analysis was performed, and a protein-protein interaction network was constructed and further analyzed using Cytoscape and STRING. Functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and then Gene Set Enrichment Analysis (GSEA) was used to formulate the molecular functions and pathways based on the results of GO and KEGG analyses. Comparative Toxicogenomics Database and TargetScan were used to identify the hub-gene-related diseases and the microRNAs that regulated the central hub genes. Immunohistochemical staining was performed to confirm the expression of related proteins in OA and non-OA tissue samples. A total of 1,679 DEGs were identified. GO analysis showed that the DEGs were primarily enriched in the process of 'immune system', 'extracellular region', 'secretory granule', 'collagen-containing extracellular matrix', 'ECM-receptor, glycosaminoglycan binding' and 'systemic lupus erythematosus'. The results of GSEA were similar to those of GO and KEGG enrichment terms for DEGs. A total of 25 important modules were generated, and two core gene clusters and seven core genes were obtained (COL6A2, COL5A2, COL12A1, COL5A1, COL6A1, LUM and COL3A1). Core genes were expressed differentially between OA subchondral bone and normal tissue samples. The expression levels of COL3A1, COL5A1 and COL6A2 in OA subchondral bone tissue were higher compared with those in normal tissues, but COL12A1 expression was not significantly increased; all stained markers were highly expressed in surrounding tissues of immunohistochemical staining. In conclusion, COL3A1, COL5A1 and COL6A2 may be potential molecular biomarkers for OA.

Identification of milk-related genes and regulatory networks in Bactrian camel either supplemented or under grazing

Using gene co-expression networks to understand dynamic characterizations in lactating animals becomes a common method. However, there are rarely reporters focusing on milk traits in Bactrian camel by high-throughput sequencing. We used RNA-seq to generate the camel transcriptome from the blood of 16 lactating Alxa Bactrian camel in different feeding groups. In total, we obtained 1185 milk-related genes correlated with milk yield, milk protein, milk fat, and milk lactose across the WGCNA analysis. Moreover, 364 milk-related genes were differentially expressed between supplementation and grazing feeding groups. The differential expression-camel milk-related genes CMRGs (DE-CMRGs) in supplement direct an intensive gene co-expression network to improve milk performance in lactating camels. This study provides a non-invasive method to identify the camel milk-related genes in camel blood for four primary milk traits and valuable theoretical basis and research ideas for the study of the milk performance regulation mechanism of camelid animals.

Identification and experimental validation of autophagy-related genes in abdominal aortic aneurysm

AIM

Autophagy plays essential roles in abdominal aortic aneurysm (AAA) development and progression. The objective of this study was to verify the autophagy-related genes (ARGs) underlying AAA empirically and using bioinformatics analysis.

METHODS

Two gene expression profile datasets GSE98278 and GSE57691 were downloaded from the Gene Expression Omnibus (GEO) database, and principal component analysis was performed. Following, the R software (version 4.0.0) was employed to analyze potentially differentially expressed genes related with AAA and autophagy. Subsequently, the candidate genes were screened using protein-protein interaction (PPI), gene ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Finally, quantitative real-time polymerase chain reaction (RT-qPCR) was performed to detect the RNA expression levels of the top five selected abnormal ARGs in clinical samples obtained from the normal and AAA patients.

RESULTS

According to the information contained (97 AAA patients and 10 healthy controls) in the two datasets, a total of 44 differentially expressed autophagy-related genes (6 up-regulated genes and 38 down-regulated genes) were screened. GO enrichment analysis of differentially expressed autophagy-related genes (DEARGs) demonstrated that some enrichment items were associated with inflammation, and PPI analysis indicated interaction between these genes. RT-qPCR results presented that the expression levels of IL6, PPARG, SOD1, and MAP1LC3B were in accordance with the bioinformatics prediction results acquired from the mRNA chip.

CONCLUSION

Bioinformatics analysis identified 44 potential autophagy-related differentially expressed genes in AAA. Further verification by RT- qPCR presented that IL6, PPARG, SOD1, and MAP1LC3B may affect the development of AAA by regulating autophagy. These findings might help explain the pathogenesis of AAA and be helpful in its diagnosis and treatment.

Assessment of lipid metabolism-disrupting effects of non-phthalate plasticizer diisobutyl adipate through in silico and in vitro approaches

Diisobutyl adipate (DIBA), as a novel non-phthalate plasticizer, is widely used in various products. However, little effort has been made to investigate whether DIBA might have adverse effects on human health. In this study, we integrated an in silico and in vitro strategy to assess the impact of DIBA on cellular homeostasis. Since numerous plasticizers could activate peroxisome proliferator-activated receptor γ (PPARγ) pathway to interrupt metabolism systems, we first utilized molecular docking to analyze interaction between DIBA and PPARγ. Results indicated that DIBA had strong affinity with the ligand-binding domain of PPARγ (PPARγ-LBD) at Histidine 499. Afterwards, we used cellular models to investigate in vitro effects of DIBA. Results demonstrated that DIBA exposure increased intracellular lipid content in murine and human hepatocytes, and altered transcriptional expression of genes related to PPARγ signaling and lipid metabolism pathways. At last, target genes regulated by DIBA were predicted and enriched for Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Protein-protein interaction (PPI) network and transcriptional factors (TFs)-genes network were established accordingly. Target genes were enriched in Phospholipase D signaling pathway, phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) and Epidermal growth factor receptor (EGFR) signaling pathway which were related to lipid metabolism. These findings suggested that DIBA exposure might disturb intracellular lipid metabolism homeostasis via targeting PPARγ. This study also demonstrated that this integrated in silico and in vitro methodology could be utilized as a high throughput, cost-saving and effective tool to assess the potential risk of various environmental chemicals on human health.

Exploration and Validation of Pancreatic Cancer Hub Genes Based on Weighted Gene Co-Expression Network Analysis and Immune Infiltration Score Analysis

Objective

To find pancreatic cancer (PC)-related hub genes based on weighted gene co-expression network analysis (WGCNA) construction and immune infiltration score analysis and validate them immunohistochemically by clinical cases, to generate new concepts or therapeutic targets for the early diagnosis and treatment of PC.

Material and Methods

In this study, WGCNA and immune infiltration score were utilized to identify the relevant core modules of PC and the hub genes within these core modules.

Results

Using WGCNA analysis, data from PC and normal pancreas integrated with TCGA and GTEX were analyzed and brown modules were chosen from the six modules. Five hub genes, including DPYD, FXYD6, MAP6, FAM110B, and ANK2, were discovered to have differential survival significance via validation tests utilizing survival analysis curves and the GEPIA database. The DPYD gene was the only gene associated with PC survival side effects. Validation of the Human Protein Atlas (HPA) database and immunohistochemical testing of clinical samples showed positive results for DPYD expression in PC.

Conclusion

In this study, we identified DPYD, FXYD6, MAP6, FAM110B, and ANK2, as immune-related candidate markers for PC. Only the DPYD gene had a negative impact on the survival of PC patients. Through validation of the HPA database and immunohistochemical testing of clinical cases, we believe that the DPYD gene brings novel ideas and therapeutic targets in the diagnosis and treatment of PC.

WGCNA and molecular docking identify hub genes for cardiac aging

Background

Cardiac aging and ageing-related cardiovascular diseases remain increase medical and social burden. Discovering the molecular mechanisms associated with cardiac aging is expected to provide new perspectives for delaying aging and related disease treatment.

Methods

The samples in GEO database were divided into older group and younger group based on age. Age-associated differentially expressed genes (DEGs) were identified by limma package. Gene modules significantly associated with age were mined using weighted gene co-expression network analysis (WGCNA). Protein-protein interaction networks (PPI) networks were developed using genes within modules, and topological analysis on the networks was performed to identify hub genes in cardiac aging. Pearson correlation was used to analyze the association among hub genes and immune and immune-related pathways. Molecular docking of hub genes and the anti-aging drug Sirolimus was performed to explore the potential role of hub genes in treating cardiac aging.

Results

We found a generally negative correlation between age and immunity, with a significant negative correlation between age and b_cell_receptor_signaling_pathway, fc_gamma_r_mediated_phagocytosis, chemokine signaling pathway, t-cell receptor signaling pathway, toll_like_receptor_signaling_pathway, and jak_stat_signaling_pathway, respectively. Finally, 10 cardiac aging-related hub genes including LCP2, PTPRC, RAC2, CD48, CD68, CCR2, CCL2, IL10, CCL5 and IGF1 were identified. 10-hub genes were closely associated with age and immune-related pathways. There was a strong binding interaction between Sirolimus-CCR2. CCR2 may be a key target for Sirolimus in the treatment of cardiac aging.

Conclusion

The 10 hub genes may be potential therapeutic targets for cardiac aging, and our study provided new ideas for the treatment of cardiac aging.

Role of CCT4/ErbB signaling in nephroblastoma: Implications for a biomarker of Wilms tumor

Wilms tumor is a common abdominal malignant tumor in children. However, the molecular mechanism of Wilms tumor is unclear. GSE66405 and GSE197047 were obtained from the Gene Expression Omnibus database. To identify differentially expressed genes (DEGs) in Wilms tumor, the R package "limma" was used. Weighted gene co-expression network analysis was performed to identify the significant module. The list of DEGs was input into the Search Tool for the Retrieval of Interacting Genes database to construct a protein-protein interaction network for predicting core genes. Gene Ontology analysis and the Kyoto Encyclopedia of Genes and Genomes analysis are computational methods for assessing gene function and biological pathways. The genome was analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes and developed by gene set enrichment analysis. Comparative Toxicogenomics Database analysis was performed to find the diseases most related to the core genes. TargetScan was used to screen for miRNAs that regulate hub genes. A total of 925 DEGs were identified. The differently expressed genes were mainly enriched in the metabolic pathway, AMPK signaling pathway, ErbB signaling pathway, mRNA detection pathway, and folded protein binding. A total of 16 core genes (HNRNPK, PABPC1, HNRNPD, NCL, YBX1, EIF4G1, KHDRBS1, HNRNPAB, HSPA4, EEF2, HSP90AA1, EEF1A1, A TP5A1, SDHA, CCT4, CCT5) were obtained. chaperonin containing TCP-1 subunit 4 (CCT4) was downregulated in tumor tissue samples, which may have reverse regulatory significance for Wilms tumor. CCT4, HSP90AA1, NCL, PABPC1, and YBX1 were found to be associated with kidney disease, acute kidney injury, edema, tumor metastasis, transitional cell carcinoma, necrosis, and inflammation. The research found that the related miRNA of the CCT4 gene was hsamiR-7-5p. CCT4 might play an essential role in the occurrence and development of Wilms tumor, and they may participate in the occurrence and development of Wilms tumor through the ERBB signal pathway. CCT4 may be a promising biomarker of Wilms tumor.

Identification of diagnostic biomarks and immune cell infiltration in ulcerative colitis

We aimed to explore diagnostic biomarks and immune cell infiltration characteristics in ulcerative colitis (UC). We used the dataset GSE38713 as the training set and dataset GSE94648 as the test set. A total of 402 differentially expressed genes (DEGs) were obtained from GSE38713. Annotating, visualizing, and integrating discovery of these differential genes was performed using Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia Pathway (KEGG), and Gene Set Enrichment Analysis (GSEA). Protein-protein interaction networks were constructed from the STRING database, and protein functional modules were identified using the CytoHubba plugin of Cytoscape. Random forest and LASSO regression were used to screen for UC-related diagnostic markers, and ROC curves were generated to validate their diagnostic value. The composition of 22 immune cells was analyzed, and the immune cell infiltration in UC was analyzed using CIBERSORT. Results: Seven diagnostic markers associated with UC were identified: TLCD3A, KLF9, EFNA1, NAAA,WDR4, CKAP4, and CHRNA1. Immune cell infiltration assessment revealed that macrophages M1, activated dendritic cells, and neutrophil cells infiltrated relatively more compared to normal control samples. Our results suggest a new functional feature of UC and suggest potential biomarkers for UC through comprehensive analysis of integrated gene expression data.

A Novel Aging-Related Prognostic lncRNA Signature Correlated with Immune Cell Infiltration and Response to Immunotherapy in Breast Cancer

Breast cancer (BC) is among the most universal malignant tumors in women worldwide. Aging is a complex phenomenon, caused by a variety of factors, that plays a significant role in tumor development. Consequently, it is crucial to screen for prognostic aging-related long non-coding RNAs (lncRNAs) in BC. The BC samples from the breast-invasive carcinoma cohort were downloaded from The Cancer Genome Atlas (TCGA) database. The differential expression of aging-related lncRNAs (DEarlncRNAs) was screened by Pearson correlation analysis. Univariate Cox regression, LASSO-Cox analysis, and multivariate Cox analysis were performed to construct an aging-related lncRNA signature. The signature was validated in the GSE20685 dataset from the Gene Expression Omnibus (GEO) database. Subsequently, a nomogram was constructed to predict survival in BC patients. The accuracy of prediction performance was assessed through the time-dependent receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, principal component analyses, decision curve analysis, calibration curve, and concordance index. Finally, differences in tumor mutational burden, tumor-infiltrating immune cells, and patients' response to chemotherapy and immunotherapy between the high- and low-risk score groups were explored. Analysis of the TCGA cohort revealed a six aging-related lncRNA signature consisting of MCF2L-AS1, USP30-AS1, OTUD6B-AS1, MAPT-AS1, PRR34-AS1, and DLGAP1-AS1. The time-dependent ROC curve proved the optimal predictability for prognosis in BC patients with areas under curves (AUCs) of 0.753, 0.772, and 0.722 in 1, 3, and 5 years, respectively. Patients in the low-risk group had better overall survival and significantly lower total tumor mutational burden. Meanwhile, the high-risk group had a lower proportion of tumor-killing immune cells. The low-risk group could benefit more from immunotherapy and some chemotherapeutics than the high-risk group. The aging-related lncRNA signature can provide new perspectives and methods for early BC diagnosis and therapeutic targets, especially tumor immunotherapy.

Identification and preliminary validation of synovial tissue-specific genes and their-mediated biological mechanisms in rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease. It is well known that the formation of positive feedback between synovial hyperplasia and inflammatory infiltration is intimately associated with the occurrence and development of RA. However, the exact mechanisms still remain unknown, making the early diagnosis and therapy of RA difficult. This study was designed to identify prospective diagnostic and therapeutic biomarkers, as well as their-mediated biological mechanisms in RA.

METHODS

Three microarray datasets (GSE36700, GSE77298 and GSE153015) and two RNA-sequencing datasets (GSE89408 and GSE112656) of synovial tissues, as well as three other microarray datasets (GSE101193, GSE134087 and GSE94519) of peripheral blood were downloaded for integrated analysis. The differently expressed genes (DEGs) were identified by "limma" package of R software. Then, weight gene co-expression analysis and gene set enrichment analysis were performed to investigate synovial tissue-specific genes and their-mediated biological mechanisms in RA. The expression of candidate genes and their diagnostic value for RA were verified by quantitative real-time PCR and receiver operating characteristic (ROC) curve, respectively. Relevant biological mechanisms were explored through cell proliferation and colony formation assay. The suggestive anti-RA compounds were discovered by CMap analysis.

RESULTS

We identified a total of 266 DEGs, which were mainly enriched in cellular proliferation and migration, infection and inflammatory immune signaling pathways. Bioinformatics analysis and molecular validation revealed 5 synovial tissue-specific genes, which exhibited excellent diagnostic value for RA. The infiltration level of immune cells in RA synovial tissue was significantly higher than that in control individuals. Moreover, preliminary molecular experiments suggested that these characteristic genes may be responsible for the high proliferation potential of RA fibroblast-like synoviocytes (FLSs). Finally, 8 small molecular compounds with anti-RA potential were obtained.

CONCLUSIONS

We have proposed 5 potential diagnostic and therapeutic biomarkers (CDK1, TTK, HMMR, DLGAP5, and SKA3) in synovial tissues that may contribute to the pathogenesis of RA. These findings may shed light on the early diagnosis and therapy of RA.

ELAC2 Functions as a Key Gene in the Early Development of Placental Formation Based on WGCNA

The placenta plays a crucial role in mammalian fetal growth. The most important cell type in the placenta is the trophoblast cell. Many genes have been reported to play important functions in the differentiation of early placental trophoblast cells. Weighted gene co-expression network analysis (WGCNA) is a systematic biological method for describing the correlation patterns among genes across microarray samples. We used WGCNA to screen placental trophoblast development-related genes, and through experimental confirmation, we showed that, among these genes, ELAC2 may play an important regulatory role in the early development of mammalian placental formation. ELAC2 regulates early placental trophoblast differentiation by affecting cell migration and cell proliferation. In addition, ELAC2 may be involved in regulating cell migration processes in a manner that affects epithelial mesenchymal transition (EMT).

Screening of Therapeutic Targets for Pancreatic Cancer by Bioinformatics Methods

Pancreatic cancer (PC) has the lowest survival rate and the highest mortality rate among all cancers due to lack of effective treatments. The objective of the current study was to identify potential therapeutic targets in PC. Three transcriptome datasets, namely GSE62452, GSE46234, and GSE101448, were analyzed for differentially expressed genes (DEGs) between cancer and normal samples. Several bioinformatics methods, including functional analysis, pathway enrichment, hub genes, and drugs were used to screen therapeutic targets for PC. Fisher's exact test was used to analyze functional enrichments. To screen DEGs, the paired t-test was employed. The statistical significance was considered at p <0.05. Overall, 60 DEGs were detected. Functional enrichment analysis revealed enrichment of the DEGs in "multicellular organismal process", "metabolic process", "cell communication", and "enzyme regulator activity". Pathway analysis demonstrated that the DEGs were primarily related to "Glycolipid metabolism", "ECM-receptor interaction", and "pathways in cancer". Five hub genes were examined using the protein-protein interaction (PPI) network. Among these hub genes, 10 known drugs targeted to the CPA1 gene and CLPS gene were found. Overall, CPA1 and CLPS genes, as well as candidate drugs, may be useful for PC in the future.

Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome

Introduction

Influenza susceptibility difference is a widely existing trait that has great practical significance for the accurate prevention and control of influenza.

Methods

Here, we focused on the human susceptibility to the seasonal influenza A/H3N2 of healthy adults at baseline level. Whole blood expression data for influenza A/H3N2 susceptibility from GEO were collected firstly (30 symptomatic and 19 asymptomatic). Then to explore the differences at baseline, a suite of systems biology approaches - the differential expression analysis, co-expression network analysis, and immune cell frequencies analysis were utilized.

Results

We found the baseline condition, especially immune condition between symptomatic and asymptomatic, was different. Co-expression module that is positively related to asymptomatic is also related to immune cell type of naïve B cell. Function enrichment analysis showed significantly correlation with "B cell receptor signaling pathway", "immune response-activating cell surface receptor signaling pathway" and so on. Also, modules that are positively related to symptomatic are also correlated to immune cell type of neutrophils, with function enrichment analysis showing significantly correlations with "response to bacterium", "inflammatory response", "cAMP-dependent protein kinase complex" and so on. Responses of symptomatic and asymptomatic hosts after virus exposure show differences on resisting the virus, with more effective frontline defense for asymptomatic hosts. A prediction model was also built based on only baseline transcription information to differentiate symptomatic and asymptomatic population with accuracy of 0.79.

Discussion

The results not only improve our understanding of the immune system and influenza susceptibility, but also provide a new direction for precise and targeted prevention and therapy of influenza.

Single-cell sequencing combined with machine learning reveals the mechanism of interaction between epilepsy and stress cardiomyopathy

Background

Epilepsy is a disorder that can manifest as abnormalities in neurological or physical function. Stress cardiomyopathy is closely associated with neurological stimulation. However, the mechanisms underlying the interrelationship between epilepsy and stress cardiomyopathy are unclear. This paper aims to explore the genetic features and potential molecular mechanisms shared in epilepsy and stress cardiomyopathy.

Methods

By analyzing the epilepsy dataset and stress cardiomyopathy dataset separately, the intersection of the two disease co-expressed differential genes is obtained, the co-expressed differential genes reveal the biological functions, the network is constructed, and the core modules are identified to reveal the interaction mechanism, the co-expressed genes with diagnostic validity are screened by machine learning algorithms, and the co-expressed genes are validated in parallel on the epilepsy single-cell data and the stress cardiomyopathy rat model.

Results

Epilepsy causes stress cardiomyopathy, and its key pathways are Complement and coagulation cascades, HIF-1 signaling pathway, its key co-expressed genes include SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3. The key immune cell subpopulations localized by single-cell data are the T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup, and Oligodendrocytes subgroup.

Conclusion

We believe epilepsy causing stress cardiomyopathy results from a multi-gene, multi-pathway combination. We identified the core co-expressed genes (SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3) and the pathways that function in them (Complement and coagulation cascades, HIF-1 signaling pathway, JAK-STAT signaling pathway), and finally localized their key cellular subgroups (T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup, and Oligodendrocytes subgroup). Also, combining cell subpopulations with hypercoagulability as well as sympathetic excitation further narrowed the cell subpopulations of related functions.

Development of a ferroptosis-based model to predict prognosis, tumor microenvironment, and drug response for lung adenocarcinoma with weighted genes co-expression network analysis

Objective: The goal of this study was to create a risk model based on the ferroptosis gene set that affects lung adenocarcinoma (LUAD) patients' prognosis and to investigate the potential underlying mechanisms. Material and Methods: A cohort of 482 LUAD patients from the TCGA database was used to develop the prognostic model. We picked the module genes from the ferroptosis gene set using weighted genes co-expression network analysis (WGCNA). The least absolute shrinkage and selection operator (LASSO) and univariate cox regression were used to screen the hub genes. Finally, the multivariate Cox analysis constructed a risk prediction score model. Three other cohorts of LUAD patients from the GEO database were included to validate the prediction ability of our model. Furthermore, the differentially expressed genes (DEG), immune infiltration, and drug sensitivity were analyzed. Results: An eight-gene-based prognostic model, including PIR, PEBP1, PPP1R13L, CA9, GLS2, DECR1, OTUB1, and YWHAE, was built. The patients from the TCGA database were classified into the high-RS and low-RS groups. The high-RS group was characterized by poor overall survival (OS) and less immune infiltration. Based on clinical traits, we separated the patients into various subgroups, and RS had remarkable prediction performance in each subgroup. The RS distribution analysis demonstrated that the RS was significantly associated with the stage of the LUAD patients. According to the study of immune cell infiltration in both groups, patients in the high-RS group had a lower abundance of immune cells, and less infiltration was associated with worse survival. Besides, we discovered that the high-RS group might not respond well to immune checkpoint inhibitors when we analyzed the gene expression of immune checkpoints. However, drug sensitivity analysis suggested that high-RS groups were more sensitive to common LUAD agents such as Afatinib, Erlotinib, Gefitinib, and Osimertinib. Conclusion: We constructed a novel and reliable ferroptosis-related model for LUAD patients, which was associated with prognosis, immune cell infiltration, and drug sensitivity, aiming to shed new light on the cancer biology and precision medicine.

Integrative analyses of immune-related biomarkers and associated mechanisms in coronary heart disease

Various studies showed that the effect of immune activation is pro-atherogenic and coronary heart disease (CHD) should therefore be considered an autoimmune disease. This study aimed to identify potential immune-related biomarkers, pathways, and the potential regulatory networks underlying CHD. Differentially expressed genes (DEGs) between CHD and control samples were determined by analyzing GSE71226 and GSE9128. The overlapping differential expression immune-related genes (DE-IRGs) for CHD were identified by analyzing the ImmPort database and two GEO databases. A total of 384 DE-IRGs were identified. Subsequently, comprehensive enrichment analyses suggested that DE-IRGs were enriched in immune-related pathways, including autoimmune thyroid disease, the intestinal immune network for IGA production, and downstream signaling events of B cell receptors. The signature of DE-IRGs was validated using an external independent dataset GSE20681 (AUC = 0.875). Furthermore, we conducted protein–protein interaction network analysis and identified eight hub genes, which were most enriched in regulation of defense response, NF-κB signaling pathway, regulation of JNK cascade, and regulation of cytokine production. Moreover, networks of miRNAs-mRNAs and transcription factors (TFs)-mRNA underlying the integrated data were established, involving eight miRNAs and 76 TF-targeting hub genes. Ultimately, 17 SNPs in miRNA-mediated gene networks were identified. We screened potential immune-related genes in CHD and constructed miRNA-mRNA-TF and SNP-miRNA networks, which not only provide inspired insights into the occurrence and the molecular mechanisms of CHD but also lay a foundation for targeting potential biomarkers using immunotherapy and for understanding the molecular mechanisms of CHD.

Characteristics of circadian rhythm-related genes and establishment of a prognostic scoring system for lung adenocarcinoma with experimental verification: a bioinformatics analysis

BACKGROUND

Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors worldwide. Lung adenocarcinoma (LUAD), the main subtype of NSCLC, has a poor prognosis. In recent years, circadian rhythm (CR)-related genes (CRRGs) have demonstrated associations with tumor occurrence and development, but the relationship between CRRGs and LUAD is not clear. Because of the correlation between CRRGs and tumors, we have reason to believe that CRRGs will become an important prognostic indicator for LUAD and guide the treatment of LUAD prognosis.

METHODS

Based on data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we explored the biological function and immune cell infiltration of LUAD in different CR clusters and quantified CR genes using principal component analysis (PCA). Then, we built a CR scoring system (CRscore) to explore the relationship between CRRGs and LUAD prognosis.

RESULTS

Patients were divided into three clusters (A, B, and C). Biological characteristics, such as survival, immune cell infiltration, and gene enrichment, were significantly different among the three clusters (P<0.001). We then established the usefulness of the CR score, which could probably predict the prognosis of LUAD. Specifically, patients with a high CR score had a better prognosis and were more sensitive to chemotherapy than those with a low CR score.

CONCLUSIONS

It is possible to use CRRGs to assess the prognosis of patients with LUAD. Quantification of CR using the CRscore tool in patients with LUAD maybe help to guide personalized cancer immunotherapy strategies in the future. Thus, the CRscore may be a prognostic tool for LUAD.

Identification of key pathways and genes that regulate cashmere development in cashmere goats mediated by exogenous melatonin

The growth of secondary hair follicles in cashmere goats follows a seasonal cycle. Melatonin can regulate the cycle of cashmere growth. In this study, melatonin was implanted into live cashmere goats. After skin samples were collected, transcriptome sequencing and histological section observation were performed, and weighted gene co-expression network analysis (WGCNA) was used to identify key genes and establish an interaction network. A total of 14 co-expression modules were defined by WGCNA, and combined with previous analysis results, it was found that the blue module was related to the cycle of cashmere growth after melatonin implantation. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that the first initiation of exogenous melatonin-mediated cashmere development was related mainly to the signaling pathway regulating stem cell pluripotency and to the Hippo, TGF-beta and MAPK signaling pathways. Via combined differential gene expression analyses, 6 hub genes were identified: PDGFRA, WNT5A, PPP2R1A, BMPR2, BMPR1A, and SMAD1. This study provides a foundation for further research on the mechanism by which melatonin regulates cashmere growth.

Improved biomarker discovery through a plot twist in transcriptomic data analysis

Background

Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human.

Results

In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery.

Conclusions

We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01398-w.