Immune Cell Infiltration Analysis Research Articles

Pan-cancer analysis of oncogenic role of CEP55 and experiment validation in clear cell renal cell carcinoma.

Immunotherapy has emerged as a vital component in the contemporary landscape of cancer treatment. Recent studies have indicated that CEP55 plays an oncogenic role; however, its specific mechanisms in promoting tumor proliferation and its potential value in prognosis and immunotherapy prediction across various cancers remain to be elucidated. CEP55 was significantly overexpressed in 22 cancer types compared with their adjacent normal tissues. Elevated CEP55 expression was positively correlated with younger onset age, worse tumor stage, lower response rate to the first treatment, lower tumor-free survival rate, and poorer overall survival (OS) and disease-free survival (DFS) prognosis in most cancers. Moreover, CEP55 expression was positively correlated with its binding and related genes, such as KIF11 (R = 0.83, P < 0.001), CDK1 (R = 0.77, P < 0.001) and CCNA2 (R = 0.76, P < 0.001), and the classic proliferation markers, including MKI67 and PCNA. Enrichment analyses indicated that CEP55 was predominantly associated with cell division, cell cycle activities and proliferation. Immune cell infiltration analysis by TIMER2.0 revealed that CEP55 expression was positively correlated with many kinds of infiltrating cells, such as Th2 cells and some CD4+ T cell subsets. The CEP55 expression was positively associated with increased MSI and TMB in various cancers. Our analyzation indicated that the CEP55 expression level in patients with complete remission (CR) or partial remission (PR) to anti-PDL1 therapy was significantly higher than patients with stable disease (SD) or progressive disease (PD) based on IMvigor210 cohort. We also used Gene Set Cancer Analysis (GSCA) to predict a serious of small molecule CEP55 targeted drugs, such as AZ628, SB52334, SB590885, A-770,041, AZD7762, Elesclomol, panobinostat, BRD-A94377914, and LRRK2-IN-1. Furthermore, the patients with high level of CEP55-posivie tumor epithelial cells had inferior overall survival in ccRCC according to single-cell analysis. Finally, our wet lab experiments verified that the CEP55-positive rate in ccRCC tissues (19/30, 63.3%) was significantly higher than that in renal adjacent tissues (10/30, 33.3%). The clinicopathologic analysis revealed that CEP55 protein level was significantly associated with tumor size (P = 0.044), histology grade (P < 0.001) and stage (P = 0.034). Our study indicated that CEP55 overexpression in most caner types was associated with poor prognosis. Notably, CEP55 was closely relevant to immune cell infiltration and impacted the response to immunotherapy and small molecule drugs against cancers.

Machine learning model based on SERPING1, C1QB, and C1QC: A novel diagnostic approach for latent tuberculosis infection

AbstractBackgroundLatent tuberculosis infection (LTBI) is a significant source of active tuberculosis (ATB), yet distinguishing between them is challenging because specific biomarkers are lacking.MethodsWe analyzed four microarray datasets (GSE19491, GSE37250, GSE54992, GSE28623) from the gene expression omnibus to identify differentially expressed genes (DEGs). Using protein–protein interaction (PPI) networks and LASSO‐SVM algorithms, we selected three candidate biomarkers and evaluated their diagnostic efficacy. The expression levels of core genes were validated by RNA sequencing of healthy, ATB, and LTBI groups in a real‐world cohort. We conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, predicted shared upstream miRNAs, constructed miRNA–hub and transcription factor (TF)–hub gene networks, and performed immune infiltration analysis.ResultsThree hub genes (SERPING1, C1QC, C1QB) were identified from 45 DEGs by PPI networks and machine learning screening. The diagnostic model based on the three hub genes had an area under the curve (AUC) value of 0.843 in the training set GSE19491 and 0.865 in the validation set GSE28623. Real‐world transcriptome sequencing confirmed the expression trends of the hub genes across healthy, LTBI, and ATB groups. GO analysis showed that the 45 hub genes were primarily associated with immune inflammatory responses and pattern recognition receptors, whereas KEGG analysis indicated enrichment in complement and coagulation cascades. The miRNA–hub and TF–hub gene network analysis identified nine miRNAs and the zinc finger TF GATA2 as potential co‐regulators of SERPING1, C1QC, and C1QB. Immune cell infiltration analysis identified significant differences in the immune microenvironment between LTBI and ATB, with macrophages and natural killer cells showing significant correlations with tuberculosis infection.ConclusionThe diagnostic model with SERPING1, C1QC, and C1QB shows promise in distinguishing LTBI from ATB, indicating its potential as a diagnostic tool.

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

ObjectiveAbdominal 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.MethodsSourced 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.ResultsWe 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.ConclusionThree 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.

Analyzing immune cell infiltrates in skeletal muscle of infantile-onset Pompe disease using bioinformatics and machine learning

Pompe disease, a severe lysosomal storage disorder, is marked by heart problems, muscle weakness, and respiratory difficulties. This study aimed to identify novel markers for infantile-onset Pompe disease by analyzing key genes and immune cells infiltrating affected skeletal muscles, building on existing research linking its progression to the immune cell infiltration. The datasets GSE38680 and GSE159062 were downloaded and differential expression genes (DEGs) were identified. Potential markers were screened using support vector machine recursive feature elimination (SVM-RFE) analysis and least absolute shrinkage and selection operator (LASSO) regression models. To analyze DEGs and immune processes, 22 immune cell types in affected tissues were examined using CIBERSORT. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed significant enrichment in the calcium signaling pathway, phosphatidylinositol signaling system, ubiquitin-mediated proteolysis and JAK-STAT signaling pathway. Machine learning identified GPNMB, CALML6 and TRIM7 as key genes. Immune cell infiltration analysis showed that the expression levels of the above three genes were closely related to immune cells in our target population under study, including T cells regulatory (Tregs), monocytes, macrophages M0, resting and activated dendritic cells, naive and memory B cells. Overall, our study results may provide new clues for exploring markers related to Pompe disease by highlighting key genes and their relationship with immune infiltration, offering insights into the disease’s development. However, previous studies have shown limited evidence of significant immune cell infiltration in muscle biopsies from Pompe patients, and this lack of direct evidence necessitates further investigation and experimental validation in laboratory settings to confirm these associations.

M6A-related genes and their role in Parkinson's disease: Insights from machine learning and consensus clustering.

Parkinson disease (PD) is a chronic neurological disorder primarily characterized by a deficiency of dopamine in the brain. In recent years, numerous studies have highlighted the substantial influence of RNA N6-methyladenosine (m6A) regulators on various biological processes. Nevertheless, the specific contribution of m6A-related genes to the development and progression of PD remains uncertain. In this study, we performed a differential analysis of the GSE8397 dataset in the Gene Expression Omnibus database and selected important m6A-related genes. Candidate m6A-related genes were then screened using a random forest model to predict the risk of PD. A nomogram model was built based on the candidate m6A-related genes. By employing a consensus clustering method, PD was divided into different m6A clusters based on the selected significant m6A-related genes. Finally, we performed immune cell infiltration analysis to explore the immune infiltration between different clusters. We performed a differential analysis of the GSE8397 dataset in the Gene Expression Omnibus database and selected 11 important m6A-related genes. Four candidate m6A-related genes (YTH Domain Containing 2, heterogeneous nuclear ribonucleoprotein C, leucine-rich pentatricopeptide repeat motif containing protein and insulin-like growth factor binding protein-3) were then screened using a random forest model to predict the risk of PD. A nomogram model was built based on the 4 candidate m6A-related genes. The decision curve analysis indicated that patients can benefit from the nomogram model. By employing a consensus clustering method, PD was divided into 2 m6A clusters (cluster A and cluster B) based on the selected significant m6A-related genes. The immune cell infiltration analysis revealed that cluster A and cluster B exhibit distinct immune phenotypes. In conclusion, m6A-related genes play a significant role in the development of PD and our study on m6A clustering may potentially guide personalized treatment strategies for PD in the future.

SNAP25 as a prognostic marker in transcriptome analysis of meningioma.

Meningiomas are the most common intracranial tumors and their diagnosis relies mostly on neuroimaging and histology. However, the histology grades cannot predict the outcome exactly and some meningiomas tend to recur after resection of even benign tumors. Therefore, it is necessary to explore prognostic and diagnostic molecular targets. Differential expression analysis between meningiomas and meninges was performed based on the merged data of GSE43290 and GSE84263. Next, we performed gene set enrichment analysis (GSEA), immune cell infiltration analysis, protein-protein interaction analysis, and survival analysis using public data. The expression level of Synaptosome-associated-protein-25kDa (SNAP25) was verified by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) and Western blotting in meningioma tissues. There were 263 upregulated and 592 downregulated genes identified in meningiomas by differential expression analysis. GSEA results revealed that meningiomas were negatively related to the pathway of soluble N-ethylmaleimide sensitive factor attachment protein receptor interactions in vascular transport and chemokine signaling. SNAP25 was characterized as a hub gene and downregulated in meningiomas. The Kaplan-Meier plot indicated that high expression of SNAP25 is a favorable factor. SNAP25 was downregulated and identified as a potential prognostic marker in meningioma.

Spermine Synthase : A Potential Prognostic Marker for Lower-Grade Gliomas.

The objective of this study was to assess the relationship between spermine synthase (SMS) expression, tumor occurrence, and prognosis in lower-grade gliomas (LGGs). A total of 523 LGG patients and 1152 normal brain tissues were included as controls. Mann-Whitney U test was performed to evaluate SMS expression in the LGG group. Functional annotation analysis was conducted to explore the biological processes associated with high SMS expression. Immune cell infiltration analysis was performed to examine the correlation between SMS expression and immune cell types. The association between SMS expression and clinical and pathological features was assessed using Spearman correlation analysis. In vitro experiments were conducted to investigate the effects of overexpressing or downregulating SMS on cell proliferation, apoptosis, migration, invasion, and key proteins in the protein kinase B (AKT)/epithelialmesenchymal transition signaling pathway. The study revealed a significant upregulation of SMS expression in LGGs compared to normal brain tissues. High SMS expression was associated with certain clinical and pathological features, including older age, astrocytoma, higher World Health Organization grade, poor disease-specific survival, disease progression, non-1p/19q codeletion, and wild-type isocitrate dehydrogenase. Cox regression analysis identified SMS as a risk factor for overall survival. Bioinformatics analysis showed enrichment of eosinophils, T cells, and macrophages in LGG samples, while proportions of dendritic (DC) cells, plasmacytoid DC (pDC) cells, and CD8+ T cells were decreased. High SMS expression in LGGs may promote tumor occurrence through cellular proliferation and modulation of immune cell infiltration. These findings suggest the prognostic value of SMS in predicting clinical outcomes for LGG patients.

Regulatory T cells-related gene in primary sclerosing cholangitis: evidence from Mendelian randomization and transcriptome data.

The present study utilized large-scale genome-wide association studies (GWAS) summary data (731 immune cell subtypes and three primary sclerosing cholangitis (PSC) GWAS datasets), meta-analysis, and two PSC transcriptome data to elucidate the pivotal role of Tregs proportion imbalance in the occurrence of PSC. Then, we employed weighted gene co-expression network analysis (WGCNA), differential analysis, and 107 combinations of 12 machine-learning algorithms to construct and validate an artificial intelligence-derived diagnostic model (Tregs classifier) according to the average area under curve (AUC) (0.959) in two cohorts. Quantitative real-time polymerase chain reaction (qRT-PCR) verified that compared to control, Akap10, Basp1, Dennd3, Plxnc1, and Tmco3 were significantly up-regulated in the PSC mice model yet the expression level of Klf13, and Scap was significantly lower. Furthermore, immune cell infiltration and functional enrichment analysis revealed significant associations of the hub Tregs-related gene with M2 macrophage, neutrophils, megakaryocyte-erythroid progenitor (MEP), natural killer T cell (NKT), and enrichment scores of the autophagic cell death, complement and coagulation cascades, metabolic disturbance, Fc gamma R-mediated phagocytosis, mitochondrial dysfunction, potentially mediating PSC onset. XGBoost algorithm and SHapley Additive exPlanations (SHAP) identified AKAP10 and KLF13 as optimal genes, which may be an important target for PSC.

Screening of pathologically significant diagnostic biomarkers in tears of thyroid eye disease based on bioinformatic analysis and machine learning.

Lacrimal gland enlargement is a common pathological change in patients with thyroid eye disease (TED). Tear fluid has emerged as a new source of diagnostic biomarkers, but tear-based diagnostic biomarkers for TED with high efficacy are still lacking. We aim to investigate genes associated with TED-associated lacrimal gland lesions. Additionally, we seek to identify potential biomarkers for diagnosing TED in tear fluid. We obtained two expression profiling datasets related to TED lacrimal gland samples from the Gene Expression Omnibus (GEO). Subsequently, we combined the two separate datasets and conducted differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) on the obtained integrated dataset. The genes were employed for Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The genes were intersected with the secretory proteins profile to get the potential proteins in the tear fluid. Machine learning techniques were then employed to identify optimal biomarkers and develop a diagnostic nomogram for predicting TED. Finally, gene set enrichment analysis (GSEA) and immune infiltration analysis were conducted on screened hub genes to further elucidate their potential mechanisms in TED. In our analysis of the integrated TED dataset, we identified 2,918 key module genes and 157 differentially expressed genes and finally obtained 84 lacrimal-associated key genes. Enrichment analysis disclosed that these 84 genes primarily pertain to endoplasmic reticulum organization. After intersecting with the secretory proteins, 13 lacrimal gland-associated secretory protein genes (LaSGs) were identified. The results from machine learning indicated the substantial diagnostic value of dyslexia associated gene (KIAA0319) and peroxiredoxin4 (PRDX4) in TED-associated lacrimal gland lesions. The two hub genes were chosen as candidate biomarkers in tear fluid and employed to establish a diagnostic nomogram. Furthermore, single-gene GSEA results and immune cell infiltration analysis unveiled immune dysregulation in the lacrimal gland of TED, with KIAA0319 and PRDX4 showing significant associations with infiltrating immune cells. We uncovered the distinct pathophysiology of TED-associated lacrimal gland enlargement compared to TED-associated orbital adipose tissue enlargement. We have demonstrated the endoplasmic reticulum-related pathways involved in TED-associated lacrimal gland lesions and established a diagnostic nomogram for TED utilizing KIAA0319 and PRDX4 through integrated bioinformatics analysis. This contribution offers novel insights for non-invasive, prospective diagnostic approaches in the context of TED.

Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in Crohn’s disease by integrating bioinformatics and machine learning

Crohn’s disease (CD) presents significant diagnostic and therapeutic challenges due to its unclear etiology, frequent relapses, and limited treatment options. Traditional monitoring often relies on invasive and costly gastrointestinal procedures. This study aimed to identify specific diagnostic markers for CD using advanced computational approaches. Four gene expression datasets from the Gene Expression Omnibus (GEO) were analyzed, identifying differentially expressed genes (DEGs) through gene set enrichment analysis in R. Key biomarkers were selected using machine learning algorithms, including LASSO logistic regression, SVM‑RFE, and Random Forest, and their accuracy was assessed using receiver operating characteristic (ROC) curves and nomogram models. Immune cell infiltration was analyzed using the CIBERSORT algorithm, which helped reveal associations between diagnostic markers and immune cell patterns in CD. From a training set of 605 CD samples and 82 normal controls, we identified eight significant biomarkers: LCN2, FOLH1, CXCL1, FPR1, S100P, IGFBP5, CHP2, and AQP9. The diagnostic model showed high predictive power (AUC=0.954) and performed well in external validation (AUC = 1). Immune cell infiltration analysis highlighted various immune cells involved in CD, with all diagnostic markers strongly linked to immune cell interactions. Our findings propose candidate hub genes and present a nomogram for CD diagnosis, providing potential diagnostic biomarkers for clinical applications in CD.

Characterizing adipocytokine-related signatures for prognosis prediction in prostate cancer.

Prostate cancer (PCa) is a prevalent malignant tumor in males, with a significant incidence of biochemical recurrence (BCR) despite advancements in treatment. Adipose tissue surrounding the prostate, known as periprostatic adipose tissue (PPAT), contributes to PCa invasion through adipocytokine production. However, the relationship between adipocytokine-related genes and PCa prognosis remains understudied. This study was conducted to provide a theoretical basis and serve as a reference for the use of adipocytokine-related genes as prognostic markers in PCa. Transcriptome and survival data of PCa patients from The Cancer Genome Atlas (TCGA) database were analyzed. Differential gene expression analysis was conducted using the DESeq2 and limma packages. Prognostic genes were identified through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. A prognostic model was developed and validated utilizing receiver operating characteristic (ROC) and Kaplan-Meier (K-M) curves. Assessments of immune cell infiltration and drug sensitivity were also carried out. Subsequently, the function of BNIP3L gene in PCa was verified. A total of 47 adipocytokine-related differentially expressed genes (DEGs) were identified. Five genes (PPARGC1A, APOE, BNIP3L, STEAP4, and C1QTNF3) were selected as prognostic markers. The prognostic model demonstrated significant predictive accuracy in both training and validation cohorts. Patients with higher risk scores exhibited poorer survival outcomes. Immune cell infiltration analysis revealed that the high-risk group had increased immune and ESTIMATE scores, while the low-risk group had higher tumor purity. In vitro experiments confirmed the suppressive effects of BNIP3L on PCa cell proliferation, migration, and invasion. The prognostic model independently predicts the survival of patients with PCa, aiding in prognostic prediction and therapeutic efficacy. It expands the study of adipocytokine-related genes in PCa, presenting novel targets for treatment.

A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6

BackgroundBreast cancer is a significant public health issue worldwide, being the most prevalent cancer among women and a leading cause of death related to this disease. The molecular processes that propel breast cancer progression are not fully elucidated, highlighting the intricate nature of the underlying biology and its crucial impact on global health. The objective of this research was to perform bioinformatics analyses on breast cancer-related datasets to gain a comprehensive understanding of the molecular mechanisms at play and to identify key genes associated with the disease.MethodsThe toolkit analyses involve techniques such as differential gene expression analysis, Gene Set Enrichment Analysis (GSEA), Weighted Co-Expression Network Analysis (WGCNA), and Machine Learning algorithms. Furthermore, in vitro cell experiments have demonstrated the impact of HSPB6 on cell migration, proliferation, and apoptosis.ResultsThe study identified multiple genes that displayed differential expression in breast cancer, notably FHL1 and HSPB6. A machine learning model was developed in this study and specifically trained for breast cancer diagnosis using these genes, achieving high precision. Furthermore, analysis of immune cell infiltration revealed an enrichment of Tregs and M2 macrophages in the treated group, showcasing its significant impact on the tumor’s immunological context. A temporal analysis of breast cancer cells using single-cell RNA sequencing provided insights into cellular developmental trajectories and highlighted changes in expression patterns across key genes during disease progression. The upregulation of HSPB6 in MCF7 cells significantly inhibited both cell migration and proliferation abilities, suggesting that promoting HSPB6 expression could induce ferroptosis in breast cancer cells.ConclusionOur findings have identified compelling molecular targets and distinctive diagnostic markers for the clinical management of breast cancer. This data will serve as crucial guidance for further research in the field.

Identification of potential immune-related genes and infiltrations in temporomandibular joint osteoarthritis

Objective: The aim of this study was to investigate the potential inflammatory cytokines and chemokines markers for temporomandibular joint osteoarthritis (TMJOA) diagnosis using a bioinformatics analysis. Methods: The differentially expressed genes of mRNA (DEGs) and transcripts of lncRNA (DETs) were identified between TMJOA samples and normal controls curated from GSE205389 by the “DESeq. 2” R package. KEGG and GO were conducted using the R package “ggplot2” and “clusterProfiler”. A PPI network was constructed to identify hub genes by using the STRING and Cytoscape. The co-expression network was constructed between mRNA and lncRNA to check the potential regulation and function of lncRNA on protein coding genes. Finally, the immune cell infiltration analysis was conducted with CIBERSORTx and confirmed with xCells. Results: We identified 171 DEGs and DETs, of which the DEGs were closely related to immune response, T cell activation, cytokine-cytokine-receptor interaction, and the muscle system process. PPI network of the DEGs screened the top 10 hub genes, including IL6, IL1B, IL10, CCL2, CCL5, CXCL1, CXCL10, ICAM1, CSF1 and MMP1. Additionally, the immune cell infiltration analysis showed that CD8+ T cells, M1 macrophage and B cells infiltration were increased in TMJOA samples. Finally, we demonstrated that the co-expression between mRNA and lncRNA was mainly enriched in inflammatory and muscle related pathways. Conclusions: We found that immune and muscle system related pathways as well as the immune infiltration played a significant role in the TMJOA development. Additionally, inflammatory cytokines and chemokines could be crucial markers for early-stage TMJOA diagnosis and personalized treatment strategies.

Exploring diagnostic biomarkers of type 2 cardio-renal syndrome based on secreted proteins and bioinformatics analysis

Chronic heart failure (CHF) can induce chronic kidney disease (CKD), called type 2 cardio-renal syndrome (CRS2). The mechanism is not completely clear, and there is a lack of early warning biomarkers of CKD in the context of CHF. Two CKD, one CHF-PBMC and four CHF-cardiac tissue expression profile datasets were obtained from GEO database. Differential expression analysis and WGCNA were used to detect CKD key genes and CHF-related secreted proteins. Protein–protein interaction (PPI), functional enrichment, and cMAP analysis reveal potential mechanisms and drugs of CHF-related CKD. Five machine learning algorithms were used to screen candidate biomarkers, construct a diagnostic nomogram for CKD and validate it in two external cohorts. Clinical serum samples were collected in our hospital to evaluate the correlation and diagnostic value of biomarkers and CKD. 225 CKD key genes and 316 CHF-related secreted proteins were identified. Four key subgroups, including 204 genes, were identified as CRS2-related pathogenic genes by PPI analysis. Enrichment analysis revealed that the identified subgroups exhibited significant enrichment in cytokine action, immune responses, and inflammatory processes. The cMAP analysis highlighted metiradone as a drug with greater potential for therapeutic intervention for CRS2. Utilizing five machine learning algorithms, three hub genes (CD48, COL3A1, LOXL1) were pinpointed as potential biomarkers for CKD, and a nomogram model was constructed. Receiver operating characteristic analysis demonstrated that the nomogram’s area under the curve (AUC) exceeded 0.80 in both the CKD combined dataset and two external cohorts. In addition, the three biomarkers were significantly correlated with the glomerular filtration rate, and the AUC of the model predicting disease progression was 0.944. Furthermore, analysis of immune cell infiltration indicated a correlation between the three biomarkers and the infiltration fraction of macrophages, neutrophils, and other immune cells in CKD. Our clinical cohort validated the expression patterns of three biomarkers in serum, and the diagnostic model achieved an AUC of 0.876. CHF has the potential to facilitate the progression of CKD via the release of cardiac and PBMC secreted proteins. Furthermore, CD48, COL3A1, and LOXL1 have been identified as potential biomarkers for the detection of CKD.

Construction of a disulfidptosis-associated lncRNAs risk model to predict prognosis and immuno-infiltration analysis of lung adenocarcinoma

Objective: To develop a risk model based on LncRNAs associated with disulfidptosis to forecast the prognosis and assess immune infiltration of Lung adenocarcinoma (LUAD). Methods: This study employed a bioinformatics approach. The study was conducted from March 29, 2023 and concluded on July 1, 2023 at Guangzhou University of Chinese Medicine, Guangzhou, China. Transcriptomic data specific to LUAD were collected from TCGA database. Disulfidptosis-related LncRNAs were preliminarily screened using co-expression analysis, followed by screening using Lasso regression and Cox regression to identify LncRNAs. Subsequently, prognostic prediction models were constructed. To assess the model, survival analysis, subject operating characteristic curves, and calibration curves were employed. To evaluate the tumor microenvironment, the “estimate” package was used, while the “ggpubr” package was utilized to visualize the variations. Additionally, we employed CIBERSORT to examine immune cell infiltration abundance. Results: A prognostic prediction model was constructed using five LncRNAs. The high-risk group displayed a shorter overall survival and progression-free survival (P&lt;0.05). The concordance index was calculated as 0.704 (95% CI: 0.654-0.754). GSEA analysis reveals that high risk group is associated with the cell cycle pathway and steroid hormone biosynthesis pathway, while the low-risk group is associated with hematopoietic cell pathway and allograft rejection pathway. Immune cell infiltration analysis indicated associations between the prognostic model and activated T cells CD4 memory, T cells CD8, etc. Conclusions: The risk model of Disulfidptosis-related LncRNAs can predict the prognosis of LUAD and evaluate the immune infiltration, providing a new direction for the treatment of LUAD. doi: https://doi.org/10.12669/pjms.40.10.9025 How to cite this: Liu J, Nie H, Du W, Song W. Construction of a disulfidptosis-associated lncRNAs risk model to predict prognosis and immuno-infiltration analysis of lung adenocarcinoma. Pak J Med Sci. 2024;40(10):2368-2372. doi: https://doi.org/10.12669/pjms.40.10.9025 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Identification and Analysis of Potential Biomarkers Associated with Neutrophil Extracellular Traps in Cervicitis.

Early diagnosis of cervicitis is important. Previous studies have found that neutrophil extracellular traps (NETs) play pro-inflammatory and anti-inflammatory roles in many diseases, suggesting that they may be involved in the inflammation of the uterine cervix and NETs-related genes may serve as biomarkers of cervicitis. However, what NETs-related genes are associated with cervicitis remains to be determined. Transcriptome analysis was performed using samples of exfoliated cervical cells from 15 patients with cervicitis and 15 patients without cervicitis as the control group. First, the intersection of differentially expressed genes (DEGs) and neutrophil extracellular trap-related genes (NETRGs) were taken to obtain genes, followed by functional enrichment analysis. We obtained hub genes through two machine learning algorithms. We then performed Artificial Neural Network (ANN) and nomogram construction, confusion matrix, receiver operating characteristic (ROC), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Moreover, we constructed ceRNA network, mRNA-transcription factor (TF) network, and hub genes-drug network. We obtained 19 intersecting genes by intersecting 1398 DEGs and 136 NETRGs. 5 hub genes were obtained through 2 machine learning algorithms, namely PKM, ATG7, CTSG, RIPK3, and ENO1. Confusion matrix and ROC curve evaluation ANN model showed high accuracy and stability. A nomogram containing the 5 hub genes was established to assess the disease rate in patients. The correlation analysis revealed that the expression of ATG7 was synergistic with RIPK3. The GSEA showed that most of the hub genes were related to ECM receptor interactions. It was predicted that the ceRNA network contained 2 hub genes, 3 targeted miRNAs, and 27 targeted lnRNAs, and that 5 mRNAs were regulated by 28 TFs. In addition, 36 small molecule drugs that target hub genes may improve the treatment of cervicitis. In this study, five hub genes (PKM, ATG7, CTSG, RIPK3, ENO1) provided new directions for the diagnosis and treatment of patients with cervicitis.

Identification of potential therapeutic targets for systemic lupus erythematosus based on GEO database analysis and Mendelian randomization analysis.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease. Current treatments mainly rely on immunosuppressants, which lack specificity and pose challenges during treatment. This study aims to deeply explore the molecular pathogenic mechanism of SLE through gene expression databases (GEO) and bioinformatics analysis methods, combined with Mendelian randomization analysis, to provide key clues for new therapeutic targets. In this study, the SLE-related gene chip dataset GSE65391 was selected from the GEO database, and the data were preprocessed and statistically analyzed using R language and bioinformatics tools. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), GO, and KEGG enrichment analysis were used to screen differentially expressed genes (DEGs) for functional annotation and pathway localization. Furthermore, Mendelian randomization analysis was conducted to identify core genes closely related to SLE risk, and immune cell infiltration analysis and compound molecular docking studies were performed on the core gene ISG15. The study successfully screened 3,456 DEGs and identified core gene modules highly related to SLE through WGCNA analysis, including key genes closely related to the pathogenesis of SLE, such as STAT1, DDX58, ISG15, IRF7, and IFIH1. In particular, this study found a significant positive correlation between the ISG15 gene and SLE, suggesting that it may be a potential risk factor for SLE. Additionally, through molecular docking technology, it was discovered that the ISG15 gene can effectively bind to two compounds, genistein, and flavopiridol, which have anti-inflammatory and immunosuppressive effects, respectively. This provides new potential drug targets for SLE treatment. As an immunomodulatory cytokine, ISG15 plays a crucial role in the pathogenesis of SLE. This study found that variations in the ISG15 gene may increase the risk of SLE and exacerbate inflammatory responses and tissue damage through multiple mechanisms. Furthermore, molecular docking revealed that genistein and flavopiridol can effectively bind to ISG15, offering a new approach for SLE treatment. These two compounds, with their anti-inflammatory and immunosuppressive properties, have the potential to slow the progression of SLE by influencing the expression and function of ISG15. Through comprehensive bioinformatics analysis and Mendelian randomization analysis, this study deeply explored the molecular pathogenic mechanism of SLE and successfully identified ISG15 as a potential therapeutic target for SLE. Simultaneously, molecular docking technology revealed that two compounds, genistein and flavopiridol, have potential therapeutic effects with ISG15, providing new potential drugs for SLE treatment. These discoveries not only enhance our understanding of the pathogenesis of SLE but also provide important clues for developing new treatment strategies.

Identification of immune feature genes and intercellular profiles in diabetic cardiomyopathy

BACKGROUND Diabetic cardiomyopathy (DCM) is a multifaceted cardiovascular disorder in which immune dysregulation plays a pivotal role. The immunological molecular mechanisms underlying DCM are poorly understood. AIM To examine the immunological molecular mechanisms of DCM and construct diagnostic and prognostic models of DCM based on immune feature genes (IFGs). METHODS Weighted gene co-expression network analysis along with machine learning methods were employed to pinpoint IFGs within bulk RNA sequencing (RNA-seq) datasets. Single-sample gene set enrichment analysis (ssGSEA) facilitated the analysis of immune cell infiltration. Diagnostic and prognostic models for these IFGs were developed and assessed in a validation cohort. Gene expression in the DCM cell model was confirmed through real time-quantitative polymerase chain reaction and western blotting techniques. Additionally, single-cell RNA-seq data provided deeper insights into cellular profiles and interactions. RESULTS The overlap between 69 differentially expressed genes in the DCM-associated module and 2483 immune genes yielded 7 differentially expressed immune-related genes. Four IFGs showed good diagnostic and prognostic values in the validation cohort: Proenkephalin (Penk) and retinol binding protein 7 (Rbp7), which were highly expressed, and glucagon receptor and inhibin subunit alpha, which were expressed at low levels in DCM patients (all area under the curves &gt; 0.9). SsGSEA revealed that IFG-related immune cell infiltration primarily involved type 2 T helper cells. High expression of Penk (P &lt; 0.0001) and Rbp7 (P = 0.001) was detected in cardiomyocytes and interstitial cells and further confirmed in a DCM cell model in vitro . Intercellular events and communication analysis revealed abnormal cellular phenotype transformation and signaling communication in DCM, especially between mesenchymal cells and macrophages. CONCLUSION The present study identified Penk and Rbp7 as potential DCM biomarkers, and aberrant mesenchymal-immune cell phenotype communication may be an important aspect of DCM pathogenesis.

Identification of novel biomarkers for atherosclerosis using single-cell RNA sequencing and machine learning.

Atherosclerosis (AS) is a predominant etiological factor in numerous cardiovascular diseases, with its associated complications such as myocardial infarction and stroke serving as major contributors to worldwide mortality rates. Here, we devised dependable AS-related biomarkers through the utilization of single-cell RNA sequencing, weighted co-expression network (WGCNA), and differential expression analysis. Furthermore, we employed various machine learning techniques (LASSO and SVM-RFE) to enhance the identification of AS biomarkers, subsequently validating them using the GEO dataset. Following this, CIBERSORT was employed to investigate the correlation between biomarkers and infiltrating immune cells. Consequently, 256 differentially expressed genes (DEGs) were selected in samples of AS and normal. GO and KEGG analyses indicated that these DEGs may be related to the negative regulation of leukocyte-mediated immunity, leukocyte cell-cell adhesion, and immune system processes. Notably, C1QC and COL1A1 were pinpointed as potential diagnostic markers for AS, a finding that was further validated in the GSE21545 dataset. Moreover, the area under the curve (AUC) values for these markers exceeded 0.8, underscoring their diagnostic utility. Analysis of immune cell infiltration revealed that the expression of C1QC was correlated with M0 macrophages, gamma delta T cells, activated mast cells and memory B cells. Similarly, COL1A1 expression was linked to M0 macrophages, memory B cells, activated mast cells, gamma delta T cells, and CD4 native T cells. Finally, these results were validated using mice and human samples through immunofluorescence, immunohistochemistry, and ELISA analysis. Overall, C1QC and COL1A1 would be potential biomarkers for AS diagnosis, and that would provides novel perspectives on the diagnosis and treatment of AS.

Bioinformatics analysis of immune infiltration in human diabetic retinopathy and identification of immune-related hub genes and their ceRNA networks

Diabetic retinopathy (DR) is the most common microvascular complication in diabetic patients, and recent studies have shown that immune regulatory mechanisms are closely associated with retinal damage in DR. Therefore, this study focused on exploring immune cells and immune-related genes (IRGs) in DR and gaining insight into the ceRNA mechanisms by which IRGs regulate DR progression. Four datasets from human DR model retinal tissues were obtained from the Gene Expression Omnibus (GEO) database. R software was first used to identify differentially expressed mRNAs (DE-mRNAs) in the dataset GSE160306-mRNAs, then the distribution of immune cells in the gene matrix was analyzed by xCell and ImmuCellAI, ImmPort and InnateDB database were used to obtain immune-related hub genes (IRHGs) in the DR, and finally the STRING online tool and Cytoscape to construct the immune-related ceRNA network. The datasets GSE102485, GSE160308 and GSE160306-lncRNAs were used to validate the results of the ceRNA network further. The results of immune cell infiltration analysis showed that macrophages are important immune cells in DR; immune-related gene screening showed that FCGR2B is an IRHG in DR, and 2 immune-related ceRNA networks of IRHG were obtained: DDN-AS1/miR-10a-5p/FCGR2B and LINC01515/miR-10a-5p/FCGR2B. Our study suggests that infiltration of immune cells, especially the immune role of macrophages, is an important component of DR progression; the immune-related hub gene FCGR2B and its ceRNA network may be a key regulatory network for DR progression. The discovery of key immune cells, IRHG and ceRNA networks in this study may provide new prospects for early intervention and targeted treatment of DR.