Gene Set Enrichment Research Articles

ACSM3 Suppresses Ovarian Cancer Progression by Inactivating the IFN-γ/JAK/STAT3 Signaling Pathway.

This study aimed to investigate the role of Acyl-CoA medium-chain synthetase 3 (ACSM3) in ovarian cancer (OC) progression. ACSM3 expression in OC tissues and cells is detected by real-time quantitative polymerase chain reaction, immunohistochemistry, or western blot. The influences of ACSM3 on OC progression are assessed in vitro and in vivo experiments. Gene set enrichment analysis is performed to find significantly enriched pathways related to ACSM3. In this study, ACSM3 expression in OC tissues and cells is downregulated. ACSM3 inhibited tumor growth in vivo. Knockdown of ACSM3 promoted cell proliferation, migration, and invasion, inhibited cell apoptosis, and activated JAK/STAT3 signaling pathway in SKOV3 cells, while overexpression of ACSM3 in A2780 cells led to the opposite results. Moreover, treatment with interferon-gamma (IFN-γ) in A2780 cells reversed the effects of ACSM3 on cell proliferation, migration, invasion, and apoptosis, but IFN-γ further enhanced the effects of ACSM3 knockdown on SKOV3 cell proliferation, migration, invasion, and apoptosis. In conclusion, ACSM3 inhibited OC cell proliferation, migration, and invasion, and promoted cell apoptosis by suppressing the IFN-γ/JAK/STAT3 signaling pathway, which might provide a promising therapeutic target for OC treatment.

The role of FTO in m6A RNA methylation and immune regulation in Staphylococcus aureus infection-related osteomyelitis

BackgroundRegulators of n6-methyladenosine (m6A) RNA modification play important roles in many diseases; however, their involvement in Staphylococcus aureus (S. aureus)-related osteomyelitis remains inadequately explored. Therefore, this study aims to investigate the role of m6A in S. aureus infection-related osteomyelitis and elucidate its underlying mechanisms.MethodsWe downloaded the S. aureus infection-related osteomyelitis infection dataset GSE30119 from the Gene Expression Omnibus database. Initially, we constructed a diagnostic model based on m6A genes and predicted the hub node miRNAs and transcription factors by constructing a protein–protein interaction network. Subsequently, a prognostic model was built using LASSO regression, the receiver operating characteristic curve of the model was plotted, and the predictive performance of the diagnostic model was validated. Further, unsupervised clustering analysis, gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were employed to assess immune cell infiltration. Additionally, we validated the expression of fat mass and obesity-associated protein (FTO) in S. aureus-infected Raw264.7 macrophages using qPCR and western blotting. Moreover, we conducted si-FTO experiments on mouse Raw264.7 macrophages to investigate the anti-inflammatory regulatory role of si-FTO during S. aureus infection.ResultsWe identified 19 co-expressed genes closely related to FTO were identified, along with 206 related transcription factor regulatory genes and 589 miRNAs. Enrichment analyses suggested that these genes were involved in pathways related to the proliferation and oxidation of various immune cells, cellular senescence, and various tumors and immune cells, as well as cell cycle-related functions. GSEA revealed that PD-1, TH1, TH2, CTLA4, and other pathways were significantly enriched in patients with high FTO expression. GSVA indicated that the differentially enriched pathways were related to included amino acid metabolism, immunity, and infection. Correlation analysis of immune infiltration revealed that monocytes, M2 macrophages, resting mast cells, and neutrophils were present in normal and diseased samples. Differences in expression were observed between the groups. The western blotting and qPCR analyses confirmed that the protein expression of FTO was reduced in macrophages after infection with S. aureus, consistent with the observed changes in mRNA expression. Furthermore, we validated that FTO may influence the regulation of inflammation through the FoxO1/NF-kB pathway.ConclusionThe m6A RNA methylation regulator FTO may serve as a potential diagnostic marker and therapeutic target, involved in the pathogenesis of S. aureus infection-related osteomyelitis. This finding provides new insights into the relationship between FTO-mediated m6A RNA methylation and osteomyelitis.

Combined protein and transcriptomics identifies DCTPP1 as a putative biomarkers for predicting immunotherapy responsiveness in gastric cancer patients.

This study aimed to screen the changes after overexpression of dCTP pyrophosphatase 1 (DCTPP1) in human gastric adenocarcinoma cells (AGS) cells by proteome and transcriptome sequencing. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed to explore the functional significance of the differentially expressed DCTPP1 in gastric cancer (GC). Cell Counting Kit-8 (CCK-8) assay was used to detect the proliferation of cells. Western blot was used to detect the expression of proteins. A total of 28 genes that were significantly associated with DCTPP1 overexpression and had prognostic value were screened by Cox regression analysis. The results of gene set enrichment analysis showed that the genomes of patients with subtype A exhibited significant enrichment in pathways such as DNA repair, pyrimidine synthesis, and glucose metabolism. The tumor immune dysfunction and exclusion and The Cancer Immunome Atlas databases showed that patients with type A GC were better candidates for immunotherapy than patients with type B GC. Furthermore, the CCK-8 assay indicated significantly enhanced proliferative activity after overexpressing DCTPP1 in AGS cells, corroborating the findings from the bioinformatic analysis. The data suggest a potential association between DCTPP1 expression and both the prognosis of GC patients and the efficacy of immunotherapy. These findings offer valuable insights for the potential optimization of therapeutic strategies in gastric cancer.

The effects of the ketone body ?-hydroxybutyrate on the neuronal transcriptome

The ketogenic diet is emerging as an effective therapeutic option for patients with neurological disorders. The diet results in metabolism of fatty acids to ketone bodies like ?-hydroxybutyrates (BHBs), which serve as an alternative fuel source for brain cells. However, the molecular effect of BHB on neurons is not well understood. We hypothesized that BHB administration will induce upregulation of energy metabolism-related processes in neurons. To assess the effect of BHB administration on the neuronal transcriptome, we reanalyzed a publicly available RNAseq dataset (GSE252513) using a bioinformatic “3-pod” approach. We conducted pathway analysis and identified leading edge genes using Gene Set Enrichment Analysis (GSEA) and conducted chemical perturbagen analysis using the iLINCS repository to identify drugs that are concordant and discordant with BHB administration. We identified significantly altered (p<0.05) pathways associated with inflammation and immunity such as “regulation of proinflammatory cytokine production” and “modulation of inflammatory responses.” We did not identify significant modulation of energy metabolism-related pathways in response to BHB administration. Our results suggest that under normal conditions, BHBs primary actions include modulation of cellular neuronal immune responses.

NT5E (CD73) as a prognostic biomarker and therapeutic target associated with immune infiltration in lung adenocarcinoma

Lung adenocarcinoma (LUAD), the most common type of lung cancer, is a leading cause of cancer-related mortality. NT5E, an ecto-5’-nucleotidase enzyme, has been implicated in cancer progression, particularly in efferocytosis. Despite its potential involvement, the prognostic significance of NT5E and relationship with immune cell infiltration in LUAD have not been extensively explored. In this study, we performed a comprehensive analysis to elucidate the expression patterns of NT5E and its prognostic implications in LUAD using data from diverse public databases. Multiple computational algorithms, including CIBERSORT, ESTIMATE, and xCell, were employed to assess the correlation between NT5E expression and immune cell infiltration. We found that NT5E was significantly overexpressed at both the mRNA and protein levels in LUAD tissues. Elevated NT5E expression was significantly linked to multiple clinicopathological factors, including metastasis and pathological stage, and served as a strong predictor of poor prognosis in LUAD patients. Gene Set Enrichment Analysis (GSEA) indicated that NT5E plays a crucial role in regulating immune responses, as evidenced by differential gene expression associated with NT5E levels. A strong positive correlation was observed between NT5E expression and the presence of immune cells, including dendritic cells, macrophages, and CD4+ T cells, as well as the expression of various immune cell markers, suggesting that NT5E may influence the prognosis of LUAD patients by regulating immune cell infiltration. Additionally, drug sensitivity analysis highlights the potential of selumetinib and PD318088, both MEK1/2 inhibitors, to target NT5E in LUAD treatment, suggesting their use as single agents or in combination with other therapies. Collectively, these findings establish NT5E as a promising prognostic biomarker and therapeutic target in LUAD, particularly in the context of immune cell infiltration.

Integration of metabolomics and transcriptomics reveals the mechanism of TMEM30A downregulation induced FSGS podocyte injury.

Podocyte injury plays a critical role in the pathogenesis and progression of focal and segmental glomerulosclerosis (FSGS). Transmembrane protein 30A (TMEM30A) downregulation participates in podocyte injury. This study aimed to identify the critical pathways and molecules associated with the downregulation of TMEM30A in the context of FSGS podocyte injury. In our study, we found TMEM30A and podocyte marker Synaptopodin were significantly downregulated in kidney tissues from patients with FSGS compared to those in normal controls. Using transcriptomic and metabolomic analyses, we characterized Tmem30a knockdown (KD) and normal mouse podocytes to identify differentially expressed genes and metabolites. Then, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Protein-Protein Interaction (PPI) network were constructed, and the differentially expressed genes and metabolites were enriched into glycolytic pathway. Furthermore, we found the key glycolytic enzymes were downregulated in patients with FSGS, podocyte-specific Tmem30aLoxP/LoxP; NPHS2-Cre mice, and Tmem30a KD mouse podocytes. For rescue experiments, shTmem30a-resistant cDNA (resTmem30a) was created to intervene Tmem30a KD mouse podocytes. And we observed that podocyte-related molecules were downregulated in the Tmem30a KD group, along with glycolysis-related molecules, but the resTmem30a partially reversed this trend. Our findings clarified TMEM30A downregulation initiates podocyte injury by reducing glycolysis-related molecules (ALDOA, HK2, LDHA, and GAPDH) in FSGS and have implications for early diagnosis, prevention, and treatment.

Identification of Lipophagy-Related Gene Signature for Diagnosis and Risk Prediction of Alzheimer’s Disease

Background: Recent research indicates that lipid metabolism and autophagy play crucial roles in the development of Alzheimer’s disease (AD). Investigating the relationship between AD diagnosis and gene expression related to lipid metabolism, autophagy, and lipophagy may improve early diagnosis and the identification of therapeutic targets. Methods: Transcription datasets from AD patients were obtained from the Gene Expression Omnibus (GEO). Genes associated with lipid metabolism, autophagy, and lipophagy were sourced from the Gene Set Enrichment Analysis (GSEA) database and the Human Autophagy Database (HADb). Lipophagy-related hub genes were identified using a combination of Limma analysis, weighted gene co-expression network analysis (WGCNA), and machine learning techniques. Based on these hub genes, we developed an AD risk prediction nomogram and validated its diagnostic accuracy using three external validation datasets. Additionally, the expression levels of the hub genes were assessed through quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results: Our analysis identified three hub genes—ACBD5, GABARAPL1, and HSPA8—as being associated with AD progression. The nomogram constructed from these hub genes achieved an area under the curve (AUC) value of 0.894 for AD risk prediction, with all validation sets yielding AUC values greater than 0.8, indicating excellent diagnostic efficacy. qRT-PCR results further corroborated the associations between these hub genes and AD development. Conclusions: This study identified and validated three lipophagy-related hub genes and developed a reliable diagnostic model, offering insights into the pathology of AD and facilitating the diagnosis of AD patients.

Identification of m6A methyltransferase-related WTAP and ZC3H13 predicts immune infiltrates in glioblastoma

Glioblastoma (GBM) is a prevalent and highly fatal primary malignant brain tumor. N6-methyladenosine (m6A) modification plays a critical role in the development of brain tumor. WTAP and ZC3H13 have been identified across various species. Immune contexture, which includes the tumor microenvironment (TME), plays a significant role in cancer progression and treatment. This study aimed to explore the potential impact between WTAP and ZC3H13 on the immunological characteristics of GBM. We utilized data from TCGA-GBM, GEO and CGGA datasets to obtain platform and probe data. Patients with GBM were stratified into two clusters based on the expression of WTAP and ZC3H13 using consensus clustering approach. Immune infiltration within the tumor microenvironment was assessed using ESTIMATE, CIBERSORT and ssGSEA methodologies. Functional disparities were determined through gene set enrichment analysis (GSEA). Tumor mutation burden (TMB) and immune checkpoint inhibitors (ICIs) were also analyzed. Co-expression network analysis (WGCNA) was used to identify genes associated with WTAP/ZC3H13 and immunity. Validation was performed using GEO and CGGA datasets. Our analysis revealed that cluster1 exhibited higher WTAP expression but lower ZC3H13 expression compared to cluster2. Cluster1 showed higher levels of immune infiltration and TMB compared to cluster2. WGCNA identified 15 genes closely associated with WTAP/ZC3H13 expression and immune scores, notably CTLA4, CD27, ICOS, and LAG3. Our results suggested that WTAP and ZC3H13 influence on immune contexture of GBM, providing new insights into tumor immunity in GBM.

TMEM45A enhances palbociclib resistance and cellular glycolysis by activating AKT/mTOR signaling pathway in HR+ breast cancer

Palbociclib, a CDK4/6 inhibitor, plays a crucial role in the treatment of HR+ breast cancer. However, resistance to palbociclib is a significant concern that merits further investigation. Our investigation identifies TMEM45A as a potential driver of palbociclib resistance and its association with increased cellular glycolysis. We demonstrate that TMEM45A is highly expressed in palbociclib-resistant breast cancer (BRCA) cells, correlating with enhanced tumor progression. Silencing TMEM45A enhances sensitivity to palbociclib, promotes cell cycle arrest and apoptosis, and inhibits the proliferation of BRCA cells. Moreover, attenuation of TMEM45A expression reduces cancer aggressiveness by decreasing the expression of EMT and glycolysis-related proteins. Subsequent gene set enrichment analysis (GSEA) confirms that TMEM45A activates the AKT/mTOR signaling pathway, which is integral to cell cycle progression and glycolysis. In a cell line-derived xenograft (CDX) mouse model, TMEM45A knockdown significantly restores sensitivity to palbociclib and suppresses tumor growth. Additionally, the use of engineered exosomes loaded with siRNA targeting TMEM45A presents a promising strategy for enhancing CDK4/6 inhibitor sensitivity without observable toxic side effects in a patient-derived xenograft (PDX) model. Collectively, our findings suggest that TMEM45A may be a therapeutic target for overcoming palbociclib resistance, and exosomal siRNA delivery could be a viable strategy for precision medicine in HR+ breast cancer.

Investigating shared risk variants and genetic etiology between Alzheimer’s disease and three stress-related psychiatric disorders: a large-scale genome-wide cross-trait analysis

IntroductionObservational studies have reported that patients with Alzheimer’s disease (AD) have a greater burden of comorbidities typically associated with stress-related psychiatric disorders. However, the contribution of hereditary factors to this comorbidity remains unclear. We evaluated phenotypic associations using observational data from the UK Biobank.MethodOur study focused on investigating the shared risk variants and genetic etiology underlying AD and three stress-related psychiatric disorders: post-traumatic stress disorder, anxiety disorder, and major depressive disorder. By leveraging summary statistics from genome-wide association studies, we investigated global genetic correlations using linkage disequilibrium score regression, genetic covariance analysis, and high-definition likelihood. Genome-wide cross-trait analysis with association analysis based on subsets and cross-phenotype association were performed to discover genome-wide significant risk variants shared between AD and the three stress-related psychiatric disorders.ResultsA significant positive genetic correlation was observed between AD and major depressive disorder using linkage disequilibrium score regression (rg = 0.231; P = 0.018), genetic covariance analysis (rg = 0.138; P < 0.001), and high-definition likelihood (rg = 0.188; P < 0.001). Association analysis based on subsets and cross-phenotype association revealed thirteen risk variants in six genes shared between AD and post-traumatic stress disorder; seven risk variants in four genes shared between AD and anxiety disorder; and 23 risk variants in four genes shared between AD and major depressive disorder. Functional annotation and gene-set enrichment analysis indicated that 12 genes for comorbidity shared between patients with AD and all three stress-related psychiatric disorders were enriched in the spleen, pancreas, and whole blood.ConclusionThese results advance our knowledge of the shared genetic origins of comorbidities and pave the way for advancements in the diagnosis, management, and prevention of stress-related AD.

Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer

Gastric cancer (GC) is one of the most common tumors; one of the reasons for its poor prognosis is that GC cells can resist normal cell death process and therefore develop distant metastasis. Cuproptosis is a novel type of cell death and a limited number of studies have been conducted on the relationship between cuproptosis-related genes (CRGs) in GC. The purpose of the present study was to establish a prognostic model of CRGs and provide directions for the diagnosis and treatment of GC. Transcriptome and clinical data of patients with GC were collected from The Cancer Genome Atlas and Gene Expression Omnibus datasets. Single sample gene set enrichment analysis (GSEA) and the randomized forest method were used to establish the prognostic model. Kaplan-Meier survival curve, receiver operating characteristics diagram and a nomogram were used to evaluate the reliability of the model. GSEA and gene set variation analysis (GSVA) were used to examine enrichment pathways between high and low risk groups. Finally, immunohistochemical analysis was used to examine ephrin 4 (EFNA4) expression in GC samples and determine the prognosis of patients with GC based on the expression pattern of EFNA4. A group of 7 predictive models (RTKN2, INO80B, EFNA4, ELF2, MUSTN, KRTAP4, and ARHGEF40) was established which were correlated with CRGs. This model can be used as an independent prognostic factor to predict the prognosis of patients with GC. GSEA and GSVA results indicated that high risk patients with GC were mainly associated with the enrichment of ANGIOGENESIS and TGF_BETA_SIGNALING pathways. Finally, EFNA4 expression in GC was significantly higher than that in normal tissues, and patients with GC and high EFNA4 expression exhibited improved prognosis. In conclusion, the prognosis model based on CRGs could be used as the basis for predicting the potential prognosis of patients with GC and provide new insights for the treatment of GC.

WNT3A promotes the cementogenic differentiation of dental pulp stem cells through the FOXO1 signaling pathway

BackgroundDental pulp stem cells (DPSCs) possess capability of multidirectional differentiation, and their cementogenic differentiation potential enables them to participate in cementum repair and regeneration. The molecular mechanisms underlying cementogenic differentiation of DPSCs remain unclear.MethodsDPSC data set GSE138179 was retrieved from gene expression omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was employed to identify significant modules. Pathway enrichment exploration was conducted utilizing gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Metascape tools. CIBERSORT was utilized to analyze immune cell infiltration analysis. The comparative toxicogenomics database (CTD) was utilized for the validation of core targets. Subsequently, cell experiments were conducted to validate the core targets. Changes in protein expression related to the FOXO1 signaling pathway, cell cycle, and apoptosis were evaluated using western blotting (WB).ResultsDifferentially expressed genes (DEGs) associated with DPSC cementogenic differentiation were predominantly enriched in crucial pathways such as the signaling pathway, cell apoptosis, and Wnt signaling pathway. Bioinformatics analysis confirmed WNT3A as a pivotal biomarker for DPSC cementogenic differentiation, and WNT3A was highly expressed in the cementogenic differentiation group. Western blotting results demonstrated that compared to the DPSC group, molecules such as Caspase-3, Caspase-9, FAS, P53, and BAX were downregulated in the CDDPSC group, suggesting reduced apoptosis. Furthermore, upregulation of WNT3A expression in CDDPSC–OE further suppressed the expression of these apoptotic molecules, suggesting a mitigated apoptotic response. Downregulation of WNT3A expression in CDDPSC–KO resulted in increased expression of apoptosis-related molecules, thereby enhancing apoptosis.ConclusionsWNT3A is highly expressed in the cementogenic differentiation of DPSC, and WNT3A mediates FOXO1 pathway to promote differentiation of dental pulp stem cells into cementogenic differentiation, thus realizing the formation and maintenance of cementum tissue.

Elevated POSTN expression predicts poor prognosis and is associated with radioresistance in cervical cancer patients treated with radical radiotherapy

Cervical cancer (CC) is a significant global health issue and remains one of the leading causes of cancer-related mortality in women. Radiotherapy is a crucial treatment modality for CC; however, tumor heterogeneity and resistance to radiotherapy often result in suboptimal outcomes for some patients, including recurrence and metastasis. Periostin (POSTN), a matricellular protein within the tumor microenvironment, has been implicated in the promotion of tumor progression and treatment resistance, particularly through mechanisms such as epithelial-mesenchymal transition (EMT). Despite this, the role of POSTN in radiotherapy resistance in CC patients remains underexplored. Therefore, in this study, we investigated the prognostic significance of POSTN expression in CC patients undergoing radical radiotherapy and explored potential mechanisms underlying radiotherapy resistance. We analyzed data from 92 CC patients in The Cancer Genome Atlas (TCGA) and 153 patients from our institution, assessing POSTN expression levels through mRNA analysis and immunohistochemistry (IHC). Our findings revealed that high POSTN expression was significantly associated with advanced tumor stages, poorer radiotherapy outcomes, and worse overall survival (OS). Additionally, multivariate Cox regression analysis identified POSTN as an independent prognostic factor for CC patients undergoing radical radiotherapy. A prognostic nomogram integrating POSTN expression and clinicopathological features demonstrated superior predictive accuracy for OS. Drug sensitivity analysis suggested that high POSTN expression may be linked to resistance to multiple chemotherapeutic agents. Furthermore, weighted correlation network analysis (WGCNA) and gene set enrichment analysis (GSEA) identified EMT as a top enriched pathway in patients with high POSTN expression, suggesting it may play a critical role in radiotherapy resistance. Subsequently, in vitro experiments confirmed that POSTN knockdown significantly inhibited HeLa cell proliferation, invasion, and enhanced radiosensitivity, while promoting apoptosis. These findings indicate that high POSTN expression is a risk factor for poor prognosis in CC patients undergoing radical radiotherapy, and targeting POSTN may improve radiotherapy efficacy by reducing tumor proliferation and resistance.

Integrated Transcriptomic and Metabolomic Analysis of Rat PASMCs Reveals the Underlying Mechanism for Pulmonary Arterial Hypertension.

Pulmonary arterial hypertension (PAH) is a kind of pulmonary vascular lesion characterized by vasoconstriction and reshaping of small pulmonary arteries, ultimately resulting in increased pulmonary artery pressure and pulmonary vascular resistance, and eventually leading to right ventricular failure and death. This study was aimed to construct a platelet-derived growth factor BB (PDGF-BB)-induced rat pulmonary artery smooth muscle cells (PASMCs) model and conduct a combined transcriptomic and metabolomic analysis to identify proliferation-related targets, thereby enhancing understanding of the pathogenesis underlying PAH. Rat PASMCs were isolated and cultured in the presence or absence of PDGF-BB for 24 hours. Cells were collected for transcriptomics and metabolomics investigations. A total of 1288 differentially expressed genes (572 up-regulated and 716 down-regulated) were identified in PDGF-BB-treated rat PASMCs compared to control cells. Subsequently, Gene ontology (GO) enrichment analysis revealed that 791 enriched GO terms were significantly enriched in PDGF-BB treated cells. Similarly, 294 differential metabolic pathways were enriched in PDGF-BB treated cells according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA) were performed on the differentially expressed genes (DEGs). It turned out that 7219 gene sets were more enriched in PDGF-BB treated cells. In addition, a total of 28 secondary differential metabolites were identified in PDGF-BB-treated rat PASMCs compared to control cells (p-value < 0.05 and VIP > 1). We speculate that Mylk, Pla2g4a, Gucy1b1, Adcy8, Adcy4, Gucy1a2, Col3a1, and Plcb4 are potential targets for the treatment of PAH.

Integrative bioinformatic approach reveals novel melatonin-related biomarkers for Alzheimer’s disease

Background: Melatonin (MLT) can improve mitophagy, thereby ameliorating cognitive deficits in Alzheimer’s disease (AD) patients. Hence, our research focused on the potential value of MLT-related genes (MRGs) in AD through bioinformatic analysis. Methods: First, the key cells in the single-cell dataset GSE138852 were screened out based on the proportion of annotated cells and Fisher’s test between the AD and control groups. The differentially expressed genes (DEGs) in the key cell and GSE5281 datasets were identified, and the MRGs in GSE5281 were selected via weighted gene coexpression network analysis. After intersecting two sets of DEGs and MRGs, we performed Mendelian randomization analysis to identify the MRGs causally related to AD. Biomarkers were further ascertained through receiver operating characteristic curve (ROC) and expression analysis in GSE5281 and GSE48350. Furthermore, gene set enrichment analysis, immune infiltration analysis and correlation analysis with metabolic pathways were conducted, as well as construction of a regulator network and molecular docking. Results: According to the Fisher test, oligodendrocytes were regarded as key cells due to their excellent abundance in the GSE138852 dataset, in which there were 281 DEGs between the AD and control groups. After overlapping with 3,490 DEGs and 550 MRGs in GSE5281, four genes were found to be causally related to AD, namely, G protein-coupled receptor, family C, group 5, member B (GPRC5B), Methyltransferase-like protein 7 A (METTL7A), NF-κB inhibitor alpha (NFKBIA) and RAS association domain family 4(RASSF4). Moreover, GPRC5B, NFKBIA and RASSF4 were deemed biomarkers, except for METTL7A, because of their indistinctive expression between the AD and control groups. Biomarkers might be involved in oxidative phosphorylation, adipogenesis and heme metabolism. Moreover, T helper type 17 cells, natural killer cells and CD56dim natural killer cells were significantly correlated with biomarkers. Transcription factors (GATA2, POU2F2, NFKB1, etc.) can regulate the expression of biomarkers. Finally, we discovered that all biomarkers could bind to MLT with a strong binding energy. Conclusion: Our study identified three novel biomarkers related to MLT for AD, namely, GPRC5B, NFKBIA and RASSF4, providing a novel approach for the investigation and treatment of AD patients.

Identification of genes involved in energy metabolism in preeclampsia and discovery of early biomarkers

BackgroundPreeclampsia is a complex pregnancy condition marked by hypertension and organ dysfunction, posing significant risks to maternal and fetal health. This study investigates the role of energy metabolism-associated genes in preeclampsia development and identifies potential early diagnostic biomarkers.MethodsPreeclampsia datasets from Gene Expression Omnibus were analyzed for batch correction, normalization, and differential expression. Enrichment analyses using gene ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment were performed. Protein-protein interaction networks were constructed to identify key genes, and regulatory networks involving transcription factors, miRNAs, and RNA-binding proteins were established. Differential expression was validated with receiver operating characteristic curve analyses, and immune infiltration was assessed.ResultsSix energy metabolism-related genes were identified. Enrichment analyses revealed their involvement in glycolysis, gluconeogenesis, lipid transport, bone remodeling, and glucagon secretion. Key differentially expressed genes included CRH(Corticotropin-Releasing Hormone), LEP(Leptin), PDK4(Pyruvate Dehydrogenase Kinase Isozyme 4), SPP1(Secreted Phosphoprotein 1), and SST(Somatostatin). PDK4 exhibited moderate accuracy in receiver operating characteristic analysis. Immune infiltration analysis indicated significant differences between preeclampsia and control samples. qRT-PCR confirmed LEP and CRH increased, while SPP1 expression in preeclampsia samples.ConclusionDysregulated energy metabolism-related genes may contribute to preeclampsia through metabolic and immune changes. Identifying these genes aids in understanding preeclampsia’s molecular basis and early diagnosis. Future studies should validate these markers in larger cohorts and explore targeted treatments.

Identifying potential signatures of immune cells in hepatocellular carcinoma using integrative bioinformatics approaches and machine-learning strategies.

Hepatocellular carcinoma (HCC) is a malignant tumor regulated by the immune system. Immunotherapy using checkpoint inhibitors has shown encouraging outcomes in a subset of HCC patients. The main challenges in checkpoint immunotherapy for HCC are to expand treatment options and to broaden the beneficiary population. Therefore, the search for potential signatures of immune cells is meaningful in the development of immunotherapy for HCC. The HCC related datasets were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Differential expression analysis and functional analysis were performed first. Then support vector machine-recursive feature elimination (SVM-RFE), random forests (RF), least absolute shrinkage and selection operation (LASSO), and weighed gene co-expression network analysis (WGCNA) were employed to screen for critical genes, and receiver operating characteristic (ROC) analysis was performed to compare diagnostic performance. Subsequently, single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between signatures and immune cells. Finally, we validated the expression of these biomarkers in human HCC samples. 531 overlapping differentially expressed genes (DEGs) were identified. Furthermore, enrichment analysis revealed pathways associated with immune activation processes, immune cell involvement and inflammatory signaling. After using multiple machine-learning strategies, extracellular matrix protein 1 (ECM1), leukemia inhibitory factor receptor (LIFR), sushi repeat containing protein X-linked (SRPX), and thromboxane A2 receptor (TBXA2R) were identified as critical signatures, and exhibited high expression in tumor-adjacent normal tissues. According to the ssGSEA results, ECM1, LIFR, SRPX and TBXA2R were all significantly associated with diverse immune cells, such as monocytes and neutrophils. Moreover, immunostaining of human HCC samples showed that these critical signatures all colocalized with CD14-positive monocytes. Our findings report the potential signatures of immune cells in HCC and confirm that they localize in monocytes of tumor-adjacent normal tissues. ECM1, LIFR, SRPX and TBXA2R could become new potential targets for predictive diagnosis, early intervention and immunotherapy of HCC in the future.

Assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltration

ObjectiveTo build a new prognostic risk assessment model based on immune cell co-expression networks for predicting overall survival and evaluating the efficacy of immunotherapy for colon cancer patients.MethodsThe Cancer Genome Atlas (TCGA) database was used to obtain mRNA expression profiling data, clinical information, and somatic mutation data from colorectal cancer patients. The degree of tumor immune cell infiltration of the samples was analyzed using the CIBERSORT algorithm. Co-expression of immune-related genes was analyzed using weighted correlation network analysis (WGCNA) and gene modules were identified. Prognosis-related genes were screened and models were constructed using LASSO-Cox analysis. The models were validated by survival analysis. The prognostic potential of the models was quantitatively assessed using Cox regression analysis and the development of column line plots. Immunotherapy sensitivity analysis was performed using CIBERSORT and TIMER algorithms. Gene biofunction analysis was performed using Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA). And the chemotherapeutic response to different drugs was assessed.ResultsWe established a novel prognostic model utilizing the WGCNA method, which demonstrated robust predictive accuracy for patient survival. The high-risk subgroup in our model exhibited elevated immune cell infiltration coupled with a higher tumor mutation burden, but the difference in response to immunotherapy was not significant compared to the low-risk group. Furthermore, we identified distinct chemotherapy responses to 39 drugs between these risk subgroups.ConclusionThis study revealed a significant correlation between high levels of immune infiltration and unfavorable prognosis in patients with colon cancer. Furthermore, an accurate prognostic risk prediction model based on the co-expression of relevant genes by immune cells was developed, enabling precise prediction of survival of colon cancer patients. These findings offer valuable insights for accurate prognostication and comprehensive management of individuals diagnosed with colon cancer.

Exploring biomarkers and molecular mechanisms of Type 2 diabetes mellitus promotes colorectal cancer progression based on transcriptomics

Type 2 diabetes mellitus (T2DM) has been confirmed as an independent risk factor for colorectal cancer (CRC) in many studies. However, the mechanisms behind T2DM’s role in the progression of CRC remain unclear. This study aims to explore the potential biomarkers and molecular mechanisms involved in T2DM-promoted CRC progression. The limma package was used to identify differentially expressed genes in tumor tissue from CRC patients with or without T2DM. The key biological processes were screened by gene ontology and gene set enrichment analysis. A diagnostic model for co-morbidities was constructed by logistic regression model with least absolute shrinkage and selection operator (Lasso) regularization method. The diagnostic performance was assessed by supplementing external datasets to draw ROC curves on the diagnostic model. The diagnostic model was further screened for key genes by prognostic analysis. The relationship of key genes with immune cells and other cells was evaluated by immune infiltration algorithm and single-cell transcription analysis. Drug prediction was performed by cMAP and the obtained drugs were molecularly docked with the key genes. The differentially expressed genes of T2DM-promoted CRC progression were mainly enriched to O-linked glycosylation-related processes. The diagnostic model constructed based on Lasso logistic regression had good diagnostic performance (AUC > 0.8). COX11 was the key gene for co-morbidities: in tumor tissues, COX11 expression was significantly higher than that in normal colon tissues. However, COX11 gene expression was significantly lower in patients with comorbidities than in patients without T2DM in tumor tissue. External datasets confirmed from both mRNA and protein expression levels that low COX11 expression was significantly associated with poor CRC prognosis. Immune infiltration analysis suggested that its expression related to the proportion of M2 macrophages. Single-cell transcriptome analysis revealed a close association of COX11 expression with endothelial cells and macrophages. The top4 drugs predicted bound well to COX11. Our study revealed that the pathogenesis of T2DM-promoted CRC progression related to O-linked glycosylation. We constructed a diagnostic model for T2DM-CRC co-morbidity. Meanwhile, we identified COX11 as a potential immune-related molecular marker closely associated with T2DM-promoted CRC progression. These mechanisms and molecular markers may provide new ideas for further studies of T2DM-promoted CRC progression and contribute to drug discovery for the treatment of co-morbidities.

Susceptibility of Brca1(L63X/+) rat to ovarian reserve dissipation by chemotherapeutic agents to breast cancer.

BRCA1 is one of the causative genes for hereditary breast and ovarian cancer syndrome with a high risk of early-onset breast cancer. Whereas olaparib (OLA), an inhibitor of poly-ADP-ribose polymerase, has been applied as adjuvant therapy to those cancer patients, its effect on ovarian reproductive function remains unelucidated. Recently, a rat model (MUT; Brca1(L63X/+) mutation) mimicking a human BRCA1 pathogenic variant has been established. Using this model, we evaluated the effects of OLA on ovarian reproductive function in comparison with the wild-type (WT) rats. MUT showed a significantly reduced number of primordial follicles and subfertility in accordance with aging. Oxidative stress was significantly elevated in the young MUT granulosa cells (GCs) accompanied by increased mTOR but decreased PTEN signals. OLA administration in MUT further decreased primordial follicles, with gene set enrichment analysis, indicating upregulated DNA repair pathways. Furthermore, a combination of OLA and cyclophosphamide (CPA) induced empty primordial follicles, recognized as CPA-induced severe ovarian toxicity. Whereas OLA + CPA caused greater reduction in primordial follicles both in MUT and WT in comparison with CPA alone, MUT ovaries were more susceptible to oxidative stress, potentially depleting primordial follicles via activation of GCs and inducing oocyte death due to accumulated DNA damage by OLA treatment. Our findings in this preclinical model underscore the importance of evaluating ovarian reserve prior to chemotherapy by performing reproductive consultation with female patients with BRCA1 pathogenic variants.