Immune Cell Infiltration Analysis Research Articles

Peptidyl-prolyl isomerase F as a prognostic biomarker associated with immune infiltrates and mitophagy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is among the most prevalent malignancies worldwide, with unfavorable treatment outcomes. Peptidyl-prolyl isomerase F (PPIF) is known to influence the malignancy traits of tumor progression by modulating the bioenergetics and mitochondrial permeability in cancer cells; however, its role in LUAD remains unclear. Our study seeks to investigate the clinical significance, tumor proliferation, and immune regulatory functions of PPIF in LUAD. The expression of PPIF in LUAD tissues and cells was assessed using bioinformatics analysis, immunohistochemistry (IHC), and Western blotting. Survival curve analysis was conducted to examine the prognostic association between PPIF expression and LUAD. The immunomodulatory role of PPIF in LUAD was assessed through the analysis of PPIF expression and immune cell infiltration. A series of gain- and loss-of-function experiments were conducted on PPIF to investigate its biological functions in LUAD both in vitro and in vivo. The mechanisms underlying PPIF's effects on LUAD were delineated through functional enrichment analysis and Western blotting assays. PPIF exhibited overexpression in LUAD tissues compared to normal controls. Survival curve analysis revealed that patients with LUAD exhibiting higher PPIF expression demonstrated decreased overall survival and a shorter progression-free interval. PPIF was implicated in modulating immune cell infiltration, particularly in regulating the T helper 1-T helper 2 cell balance. Functionally, PPIF was discovered to promote tumor cell proliferation and advance cell-cycle progression. Furthermore, PPIF could impede mitophagy by targeting the FOXO3a/PINK1-Parkin signaling pathway. The findings of this study indicate that the prognosis-related gene PPIF may have a significant role in the regulation of LUAD cell proliferation, tumor-associated immune cell infiltration, and mitophagy, and thus PPIF may be a promising therapeutic target of LUAD.

Cathepsin S (CTSS) in IgA nephropathy: an exploratory study on its role as a potential diagnostic biomarker and therapeutic target.

IgA nephropathy (IgAN), a prevalent form of glomerulonephritis globally, exhibits complex pathogenesis. Cathepsins, cysteine proteases within lysosomes, are implicated in various physiological and pathological processes, including renal conditions. Prior observational studies have suggested a potential link between cathepsins and IgAN, yet the precise causal relationship remains unclear. We conducted a comprehensive bidirectional and multivariable Mendelian randomization (MR) study using publicly available genetic data to explore the causal association between cathepsins and IgAN systematically. Additionally, immunohistochemical (IHC) staining and enzyme-linked immunosorbent assay (ELISA) were employed to evaluate cathepsin expression levels in renal tissues and serum of IgAN patients. We investigated the underlying mechanisms via gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Molecular docking and virtual screening were also performed to identify potential drug candidates through drug repositioning. Univariate MR analyses demonstrated a significant link between increased cathepsin S (CTSS) levels and a heightened risk of IgAN. This was evidenced by an odds ratio (OR) of 1.041 (95% CI=1.009-1.073, P=0.012) as estimated using the inverse variance weighting (IVW) method. In multivariable MR analysis, even after adjusting for other cathepsins, elevated CTSS levels continued to show a strong correlation with an increased risk of IgAN (IVW P=0.020, OR=1.037, 95% CI=1.006-1.069). However, reverse MR analyses did not establish a causal relationship between IgAN and various cathepsins. IHC and ELISA findings revealed significant overexpression of CTSS in both renal tissues and serum of IgAN patients compared to controls, and this high expression was unique to IgAN compared with several other primary kidney diseases such as membranous nephropathy, minimal change disease and focal segmental glomerulosclerosis. Investigations into immune cell infiltration, GSEA, and GSVA highlighted the role of CTSS expression in the immune dysregulation observed in IgAN. Molecular docking and virtual screening pinpointed Camostat mesylate, c-Kit-IN-1, and Mocetinostat as the top drug candidates for targeting CTSS. Elevated CTSS levels are associated with an increased risk of IgAN, and this enzyme is notably overexpressed in IgAN patients' serum and renal tissues. CTSS could potentially act as a diagnostic biomarker, providing new avenues for diagnosing and treating IgAN.

Diagnostic value of immune-related biomarker FAM83A in differentiating malignant from benign pleural effusion in lung adenocarcinoma

BackgroundMalignant pleural effusion (MPE) is frequently observed in patients with advanced lung adenocarcinoma (LUAD). Pleural fluid cytology is a less invasive procedure compared to pleural biopsy. Therefore, it is crucial to identify novel effective biomarkers for LUAD-associated pleural fluid cytology.MethodsThe RNA sequencing (RNA-Seq) and clinical data of LUAD cases were downloaded from TCGA and OncoSG databases. Differential gene expression analysis, survival analysis and immune cell infiltration analysis were performed on the LUAD datasets. The expression levels of FAM83A, TFF-1, and NapsinA in 94 paired LUAD and adjacent normal tissues, and in the pleural effusion specimens of 40 LUAD and 21 non-neoplastic patients were evaluated by immunohistochemistry.ResultsFAM83A expression levels were significantly different between the LUAD and normal tissue datasets, and correlated with overall or disease-free survival, and histological grade of the tumors. Furthermore, the in-situ expression of FAM83A was higher in 89/94 LUAD tissues compared to the paired normal tissues. FAM83A expression was significantly correlated with immune cell infiltration, and showed a positive association with macrophage infiltration. In addition, FAM83A staining was positive in 37 LUAD pleural effusion samples, and negative in 20 non-neoplastic pleural effusion samples. The expression pattern of FAM83A in the pleural effusion of LUAD patients was relatively consistent with that of TFF-1 and NapsinA, and even stronger in some specimens that were weakly positive or negative for TTF1/NapsinA.ConclusionsFAM83A is a promising immune-related biomarker in LUAD biopsy specimens and pleural fluid, and can distinguish between malignant and benign pleural effusion.

Computational identification of novel potential genetic pathogenesis and otherwise biomarkers in acute liver allograft rejection

Acute cellular rejection (ACR) is a prevalent postoperative complication following liver transplantation (LT), exhibiting an increasing incidence of morbidity and mortality. However, the molecular mechanisms of ACR following LT remain unclear. To explore the genetic pathogenesis and identify biomarkers of ACR following LT, three relevant Gene Expression Omnibus (GEO) datasets consisting of data on ACR or non-ACR patients after LT were comprehensively investigated by computational analysis. A total of 349 upregulated and 260 downregulated differentially expressed genes (DEGs) and eight hub genes (ISG15, HELZ2, HNRNPK, TIAL1, SKIV2L2, PABPC1, SIRT1, and PPARA) were identified. Notably, HNRNPK, TIAL1, and PABPC1 exhibited the highest predictive potential for ACR with AUCs of 0.706, 0.798, and 0.801, respectively. KEGG analysis of hub genes revealed that ACR following LT was predominately associated with ferroptosis, protein processing in the endoplasmic reticulum, complement and coagulation pathways, and RIG-I/NOD/Toll-like receptor signaling pathway. According to the immune cell infiltration analysis, γδT cells, NK cells, Tregs, and M1/M2-like macrophages had the highest levels of infiltration. Compared to SIRT1, ISG15 was positively correlated with γδT cells and M1-like macrophages but negatively correlated with NK cells, CD4+ memory T cells, and Tregs. In conclusion, this study identified eight hub genes and their potential pathways, as well as the immune cells involved in ACR following LT with the greatest levels of infiltration. These findings provide a new direction for future research on the underlying mechanism of ACR following LT.

Integrative bioinformatics analysis to identify ferroptosis-related genes in non-obstructive azoospermia.

The objective of this study was to discern ferroptosis-related genes (FRGs) linked to non-obstructive azoospermia and investigate the associated molecular mechanisms. A dataset related to azoospermia was retrieved from the Gene Expression Omnibus database, and FRGs were sourced from GeneCards. Ferroptosis-related differentially expressed genes (FRDEGs) were discerned. Subsequently, these genes underwent analyses encompassing Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, as well as protein-protein interaction (PPI) networks and assessments of functional similarity. Following the identification of hub genes, an exploration of immune infiltration, single-cell expression, diagnostic utility, and interactions involving hub genes, RNA-binding proteins (RBPs), transcription factors (TFs), microRNAs (miRNAs), and drugs was conducted. A total of 35 differentially expressed FRGs were discerned. These genes demonstrated enrichment in functions and pathways associated with ferroptosis. From the PPI network, eight hub genes were selected. Functional similarity analysis highlighted the potential pivotal roles of HMOX1 and GPX4 in azoospermia. Analysis of immune cell infiltration indicated a significant decrease in activated dendritic cells in the azoospermia group, with notable correlations between hub genes, particularly SAT1 and HMGCR, and immune cell infiltration. Unique expression patterns of hub genes across various cell types in the human testis were observed, with GPX4 prominently enriched in spermatid/sperm. Eight hub genes exhibited robust diagnostic value (AUC > 0.75). Lastly, a comprehensive hub gene-miRNA-TF-RBP-drug network was constructed. In summary, our investigation unveiled eight FRDEGs associated with azoospermia, which hold potential as biomarkers for the diagnosis and treatment of azoospermia.

Integrated analysis of single-cell RNA-seq, bulk RNA-seq, Mendelian randomization, and eQTL reveals T cell-related nomogram model and subtype classification in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a systemic disease that attacks the joints and causes a heavy economic burden on humans worldwide. T cells regulate RA progression and are considered crucial targets for therapy. Therefore, we aimed to integrate multiple datasets to explore the mechanisms of RA. Moreover, we established a T cell-related diagnostic model to provide a new method for RA immunotherapy. scRNA-seq and bulk-seq datasets for RA were obtained from the Gene Expression Omnibus (GEO) database. Various methods were used to analyze and characterize the T cell heterogeneity of RA. Using Mendelian randomization (MR) and expression quantitative trait loci (eQTL), we screened for potential pathogenic T cell marker genes in RA. Subsequently, we selected an optimal machine learning approach by comparing the nine types of machine learning in predicting RA to identify T cell-related diagnostic features to construct a nomogram model. Patients with RA were divided into different T cell-related clusters using the consensus clustering method. Finally, we performed immune cell infiltration and clinical correlation analyses of T cell-related diagnostic features. By analyzing the scRNA-seq dataset, we obtained 10,211 cells that were annotated into 7 different subtypes based on specific marker genes. By integrating the eQTL from blood and RA GWAS, combined with XGB machine learning, we identified a total of 8 T cell-related diagnostic features (MIER1, PPP1CB, ICOS, GADD45A, CD3D, SLFN5, PIP4K2A, and IL6ST). Consensus clustering analysis showed that RA could be classified into two different T-cell patterns (Cluster 1 and Cluster 2), with Cluster 2 having a higher T-cell score than Cluster 1. The two clusters involved different pathways and had different immune cell infiltration states. There was no difference in age or sex between the two different T cell patterns. In addition, ICOS and IL6ST were negatively correlated with age in RA patients. Our findings elucidate the heterogeneity of T cells in RA and the communication role of these cells in an RA immune microenvironment. The construction of T cell-related diagnostic models provides a resource for guiding RA immunotherapeutic strategies.

Integrative Analysis by Mendelian Randomization and Large-Scale Single-Cell Transcriptomics Reveals Causal Links between B Cell Subtypes and Diabetic Kidney Disease

Introduction: The increasing incidence of diabetic kidney disease (DKD) and the challenges in its management highlight the necessity for a deeper understanding of its pathogenesis. While recent studies have underscored the substantial impact of circulating immunity on the development of diabetic microvascular complications such as retinopathy and neuropathy, research on circulating immunity in DKD remains limited. Methods: This study utilized Mendelian randomization analysis to explore the potential independent causal relationships between circulating immune cells and DKD pathogenesis. Additionally, a combination of single-cell disease relevance score (scDRS) and immune cell infiltration analysis was employed to map the circulating immunity landscape in DKD patients. Results: Ten immune traits, including 5 of B cells, 2 of T cells, 2 of granulocytes, and one of monocytes, were defined to be associated with the pathogenesis of DKD. Notably, IgD-CD27-B cell Absolute Count (IVW: OR, 1.102 [1.023–1.189], p = 0.011) and IgD-CD24-B cell Absolute Count (IVW: OR, 1.106 [1.030–1.188], p = 0.005) were associated with promoting DKD pathogenesis, while CD24+CD27+B cell %B cell (IVW: OR, 0.943 [0.898–0.989], p = 0.016) demonstrated a protective effect against DKD onset. The presence of B cell-activating factor receptor (BAFF-R) on CD20−CD38−B cell (IVW: OR, 0.946 [0.904–0.989], p = 0.015) and BAFF-R on IgD-CD38+B cell (IVW: OR, 0.902 [0.834–0.975], p = 0.009) also indicated a potential role in preventing DKD. scDRS analysis revealed that two main subsets of B cells, naïve B and memory B cells, had a higher proportion of DKD-related cells or a higher scDRS score of DKD phenotype, indicating their strong association with DKD. Furthermore, immune infiltrate deconvolution analysis showed a notable decrease in the circulating memory B cells and class-switched memory B cells in DKD patients compared to those of DM patients without DKD. Conclusion: Our study revealed the causal relations between circulating immunity and DKD susceptibility, particularly highlighted the potential roles of B cell subtypes in DKD development. Further studies addressing the related mechanisms would broaden the current understanding of DKD pathogenesis.

Tumor microenvironment characteristics of lipid metabolism reprogramming related to ferroptosis and EndMT influencing prognosis in gastric cancer

BackgroundGastric cancer (GC) is a refractory malignant tumor with high tumor heterogeneity, a low rate of early diagnosis, and poor patient prognosis. Lipid metabolism reprogramming plays a critical role in tumorigenesis and progression, but its prognostic role and regulatory mechanism in GC are rarely studied. Thus, the identification of signatures related to lipid metabolism is necessary and may present a new avenue for improving the overall prognosis of GC. MethodsLipid metabolism-associated genes (LMAGs) with differential expression in tumor and tumor-adjacent tissue were acquired to identify lipid metabolism-associated subtypes. The differentially expressed genes (DEGs) between the two clusters were then utilized for prognostic analysis and signature construction. Additionally, pathway enrichment analysis and immune cell infiltration analysis were employed to identify the characteristics of the prognostic model. Further analyses were conducted at the single-cell level to better understand the model’s prognostic mechanism. Finally, the prediction of immunotherapy response was used to suggest potential treatments. ResultsTwo lipid metabolism-associated subtypes were identified and 9 prognosis-related genes from the DEGs between the two clusters were collected for the construction of the prognostic model named lipid metabolism-associated signature (LMAS). Then we found the low LMAS patients with favorable prognoses were more sensitive to ferroptosis in the Cancer Genome Atlas of Stomach Adenocarcinoma (TCGA-STAD). Meanwhile, the tumor cells exhibiting high levels of lipid peroxidation and accumulation of reactive oxygen species (ROS) in single-cell levels were primarily enriched in the low LMAS group, which was more likely to induce ferroptosis. In addition, endothelial cells and cancer-associated fibroblasts (CAFs) facilitated tumor angiogenesis, proliferation, invasion, and metastasis through endothelial-mesenchymal transition (EndMT), affecting the prognosis of the patients with high LMAS scores. Moreover, CD1C− CD141− dendritic cells (DCs) also secreted pro-tumorigenic cytokines to regulate the function of endothelial cells and CAFs. Finally, the patients with low LMAS scores might have better efficacy in immunotherapy. ConclusionsA LMAS was constructed to guide GC prognosis and therapy. Meanwhile, a novel anti-tumor effect was found in lipid metabolism reprogramming of GC which improved patients’ prognosis by regulating the sensitivity of tumor cells to ferroptosis. Moreover, EndMT may have a negative impact on GC prognosis.

A comprehensive analysis of GAS2 family members identifies that GAS2L1 is a novel biomarker and promotes the proliferation of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common primary liver cancer with a high incidence and mortality. Members of the growth-arresting-specific 2 (GAS2) family are involved in various biological processes in human malignancies. To date, there is only a limited amount of information available about the expression profile and clinical importance of GAS2 family in HCC. In this study, we found that GAS2L1 and GAS2L3 were distinctly upregulated in HCC specimens compared to non-tumor specimens. Pan-cancer assays indicated that GAS2L1 and GAS2L3 were highly expressed in most cancers. The Pearson’s correlation revealed that the expressions of GAS2, GAS2L1 and GAS2L2 were negatively associated with methylation levels. Survival assays indicated that GAS2L1 and GAS2L3 were independent prognostic factors for HCC patients. Immune cell infiltration analysis revealed that GAS2, GAS2L1 and GAS2L3 were associated with several immune cells. Finally, we confirmed that GAS2L1 was highly expressed in HCC cells and its knockdown suppressed the proliferation of HCC cells. Taken together, our findings suggested the expression patterns and prognostic values of GAS2 members in HCC, providing insights for further study of the GAS2 family as sensitive diagnostic and prognostic markers for HCC.

HTR2B as a novel biomarker of chronic obstructive pulmonary disease with lung squamous cell carcinoma

Chronic obstructive pulmonary disease (COPD) is often associated with lung squamous cell carcinoma (LUSC), which has the same etiology (smoking, inflammation, oxidative stress, microenvironmental changes, and genetics). Smoking, inflammation, and airway remodeling are the most important and classical mechanisms of COPD comorbidity in LUSC patients. Cancer can occur during repeated airway damage and repair (airway remodeling). Changes in the inflammatory and immune microenvironments, which can cause malignant transformation of some cells, are currently being revealed in both LUSC and COPD patients. We obtained the GSE76925 dataset from the Gene Expression Omnibus database. Screening for possible COPD biomarkers was performed using the LASSO regression model and a random forest classifier. The compositional patterns of the immune cell fraction in COPD patients were determined using CIBERSORT. HTR2B expression was analyzed using validation datasets (GSE47460, GSE106986, and GSE1650). HTR2B expression in COPD cell models was determined via real-time quantitative PCR. Epithelial–mesenchymal transition (EMT) marker expression levels were determined after knocking down or overexpressing HTR2B. HTR2B function and mechanism in LUSC were analyzed with the Kaplan‒Meier plotter database. HTR2B expression was inhibited to detect changes in LUSC cell proliferation. A total of 1082 differentially expressed genes (DEGs) were identified in the GSE76925 dataset (371 genes were significantly upregulated, and 711 genes were significantly downregulated). Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that the DEGs were mainly enriched in the p53 signaling and β-alanine metabolism pathways. Gene Ontology enrichment analysis indicated that the DEGs were largely related to transcription initiation from the RNA polymerase I promoter and to the regulation of mononuclear cell proliferation. The LASSO regression model and random forest classifier results revealed that HTR2B, DPYS, FRY, and CD19 were key COPD genes. Immune cell infiltration analysis indicated that these genes were closely associated with immune cells. Analysis of the validation sets suggested that HTR2B was upregulated in COPD patients. HTR2B was significantly upregulated in COPD cell models, and its upregulation was associated with increased EMT marker expression. Compared with that in bronchial epithelial cells, HTR2B expression was upregulated in LUSC cells, and inhibiting HTR2B expression led to the inhibition of LUSC cell proliferation. In conclusions, HTR2B might be a new biomarker and therapeutic target in COPD patients with LUSC.

Joint effects of CD8A and ICOS in Long QT Syndrome (LQTS) and Beckwith-Wiedemann Syndrome (BWS)

BackgroundLong QT Syndrome (LQTS) and Beckwith-Wiedemann Syndrome (BWS) are complex disorders with unclear origins, underscoring the need for in-depth molecular investigations into their mechanisms. The main aim of this study is to identify the shared key genes between LQTS and BWS, shedding light on potential common molecular pathways underlying these syndromes.MethodsThe LQTS and BWS datasets are available for download from the GEO database. Differential expression genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) was used to detect significant modules and central genes. Gene enrichment analysis was performed. CIBERSORT was used for immune cell infiltration analysis. The predictive protein interaction (PPI) network of core genes was constructed using STRING, and miRNAs regulating central genes were screened using TargetScan.ResultsFive hundred DEGs associated with Long QT Syndrome and Beckwith-Wiedemann Syndrome were identified. GSEA analysis revealed enrichment in pathways such as T cell receptor signaling, MAPK signaling, and adrenergic signaling in cardiac myocytes. Immune cell infiltration indicated higher levels of memory B cells and naive CD4 T cells. Four core genes (CD8A, ICOS, CTLA4, LCK) were identified, with CD8A and ICOS showing low expression in the syndromes and high expression in normal samples, suggesting potential inverse regulatory roles.ConclusionThe expression of CD8A and ICOS is low in long QT syndrome and Beckwith-Wiedemann syndrome, indicating their potential as key genes in the pathogenesis of these syndromes. The identification of shared key genes between LQTS and BWS provides insights into common molecular mechanisms underlying these disorders, potentially facilitating the development of targeted therapeutic strategies.

Thrombomodulin as a potential diagnostic marker of acute myocardial infarction and correlation with immune infiltration: Comprehensive analysis based on multiple machine learning

BackgroundAcute myocardial infarction (AMI) is a global health problem with high mortality. Early diagnosis can prevent the development of AMI and provide valuable information for subsequent treatment. Angiogenesis has been shown to be a critical factor in the development of infarction and targeting this process may be a potential protective strategy for preventing myocardial injury and improving the prognosis of AMI patients. This study aimed to screen and verify diagnostic markers related to angiogenesis in AMI and to investigate the molecular mechanisms of action associated with AMI in terms of immune cell infiltration. MethodsThe GSE66360 and the GSE60993 datasets were both downloaded from the GEO database and were used as the training cohort and the external validation cohort, respectively. Angiogenesis-related genes (ARGs) were downloaded from the MSigDB database. The hub ARGs were identified via LASSO, RF, and SVM-RFE algorithms. ROC curves were used to assess the accuracy of the hub ARGs. The potential mechanisms of the hub ARGs were analyzed by GSEA. The ssGSEA algorithm was used to determine differences in immune cell infiltration and immune function. The CIBERSORT algorithm was used for immune cell infiltration analysis. In addition, we constructed a ceRNA network map of differentially expressed ARGs. ResultsWe identified the thrombomodulin (THBD) gene from ARGs as a potential diagnostic marker for AMI based on the LASSO, SVM-RFE, and RF algorithms. THBD was differentially expressed and had a potential diagnostic value (area under the curve [AUC] = 0.931 and 0.765 in the training and testing datasets, respectively). GSEA showed that the MAPK signaling pathway was more enriched in the high-expression group of THBD (P < 0.05). Immune cell infiltration analysis demonstrated that THBD was mainly positively correlated with monocytes (R = 0.48, P = 0.00055) and neutrophils (R = 0.36, P = 0.013). Finally, in the ceRNA regulatory network, THBD was closely associated with 9 miRNAs and 42 lncRNAs involved in AMI. ConclusionTHBD can be used as a potential diagnostic marker for AMI. This study provides new insights for future AMI diagnosis and molecular mechanism research. Moreover, immune cell infiltration plays an essential role in the occurrence and development of AMI.

Overexpression of COX7A1 Promotes the Resistance of Gastric Cancer to Oxaliplatin and Weakens the Efficacy of Immunotherapy

Gastric cancer (GC) is one of the most common clinical malignant tumors worldwide, with high morbidity and mortality. Presently, the overall response rate to immunotherapy is low, and current methods for predicting the prognosis of GC are not optimal. Therefore, novel biomarkers with accuracy, efficiency, stability, performance ratio and wide clinical application are needed. Based on public data sets, the Chemotherapy Cohort and the Immunotherapy Cohort from Sun Yat-sen University Cancer Center, a series of bioinformatics analyses, such as differential expression analysis, survival analysis, drug sensitivity prediction, enrichment analysis, tumor immune dysfunction and exclusion (TIDE) analysis, single-sample gene set enrichment analysis (ssGSEA), stemness index calculation, immune cell infiltration analysis, were performed for screening and preliminary exploration. Immunohistochemical staining and in vitro experiments were performed for further verification. Overexpression of COX7A1 promoted the resistance of GC cells to Oxaliplatin. COX7A1 may induce immune escape by regulating the number of fibroblasts and their cellular communication with immune cells. In summary, measuring the expression levels of COX7A1 in the clinic may be useful to predict the prognosis of GC patients, the degree of chemotherapy resistance and the efficacy of immunotherapy.

Chaperonin-containing TCP-1 subunit genes are potential prognostic biomarkers and are correlated with Th2 cell infiltration in lung adenocarcinoma: An observational study.

A family of molecular chaperone complexes called chaperonin-containing T-complex protein 1 (TCP-1) subunit genes (CCTs) aids in the folding of numerous proteins. With regard to lung adenocarcinoma (LUAD), this study provided a thorough understanding of the diagnostic and prognostic use of CCTs. The expression of CCTs in LUAD was evaluated by using databases including UALCAN and the Gene Expression Omnibus. Immunohistochemistry (IHC) was conducted to validate the expression of CCTs in LUAD. The mutation in the CCTs was identified through the cBioPortal database, while promoter methylation was measured by the UALCAN database. The prognostic value of CCTs was evaluated using the PrognoScan analysis. The GEPIA2.0 database was used to measure the prognostic value of CCTs and associated Hub genes. Correlation analysis between CCTs expression in LUAD was based on the GEPIA2.0 database. The ROC curves, clinical correlation analysis, gene ontology, Kyoto Encyclopedia of Genes and Genome analysis, and immune cell infiltration analysis were downloaded from The Cancer Genome Atlas database and then analyzed and visualized using the R language. The STRING database was used for protein-protein interaction analysis. Upregulation of CCTs expression in patients with LUAD indicated advanced diseases and a poor prognosis. ROC curve analysis revealed that the CCTs may serve as diagnostic indicators. The functional enrichment analysis showed that CCTs were involved in the mitosis-mediated cell cycle process. Additionally, 10 hub genes associated with CCTs that were linked to LUAD prognosis and tumor progression were identified. Immune cell infiltration analysis showed that CCTs expression in tumor tissues tends to be related to T helper type 2 cell infiltration. This study revealed that CCTs may serve as valuable biomarkers for the diagnosis and targeted therapy of LUAD.

Tumor-Derived Exosomal miR-143-3p Induces Macrophage M2 Polarization to Cause Radiation Resistance in Locally Advanced Esophageal Squamous Cell Carcinoma.

We aimed to determine whether monitoring tumor-derived exosomal microRNAs (miRNAs) could be used to assess radiotherapeutic sensitivity in patients with locally advanced esophageal squamous cell carcinoma (ESCC). RNA sequencing was employed to conduct a comparative analysis of miRNA expression levels during radiotherapy, focusing on identifying miRNAs associated with progression. Electron microscopy confirmed the existence of exosomes, and co-cultivation assays and immunofluorescence validated their capacity to infiltrate macrophages. To determine the mechanism by which exosomal miR-143-3p regulates the interplay between ESCC cells and M2 macrophages, ESCC cell-derived exosomes were co-cultured with macrophages. Serum miR-143-3p and miR-223-3p were elevated during radiotherapy, suggesting resistance to radiation and an unfavorable prognosis for ESCC. Increased levels of both miRNAs independently predicted shorter progression-free survival (p = 0.015). We developed a diagnostic model for ESCC using serum microRNAs, resulting in an area under the curve of 0.751. Radiotherapy enhanced the release of miR-143-3p from ESCC cell-derived exosomes. Immune cell infiltration analysis at the Cancer Genome Atlas (TCGA) database revealed that ESCC cell-derived miR-143-3p triggered M2 macrophage polarization. Mechanistically, miR-143-3p upregulation affected chemokine activity and cytokine signaling pathways. Furthermore, ESCC cell exosomal miR-143-3p could be transferred to macrophages, thereby promoting their polarization. Serum miR-143-3p and miR-223-3p could represent diagnostic and prognostic markers for patients with ESCC undergoing radiotherapy. Unfavorable prognosis could be linked to the increased levels of ESCC cell-derived exosomal miR-143-3p, which might promote tumor progression by interacting with macrophages.

Comprehensive Analysis of ceRNA Network and Immune Cell Infiltration Pattern of Autophagy-Related Genes in IgA Nephropathy

Introduction: IgA nephropathy (IgAN) is a prevalent worldwide glomerular disease with a complex pathophysiology that has significant economic implications. Despite the lack of successful research, this study aims to discover the potential competing endogenous RNA (ceRNA) network of autophagy-associated genes in IgAN and examine their correlation with immune cell infiltration. Methods: Autophagy-related hub genes were discovered by assessing the GSE116626 dataset and constructing a protein-protein interaction network. Nephroseq v5 analysis engine was used to analyze correlations between hub genes and proteinuria, glomerular filtration rate (GFR), and serum creatinine levels. Then, a ceRNA network construction and the CIBERSORT tool for immune cell infiltration analysis were also performed. Additionally, the differentially expressed autophagy-related genes were used to predict potential targeted medications for IgAN. Results: Overall, 1,396 differentially expressed genes were identified in IgAN along with 25 autophagy-related differentially expressed messenger RNAs. Enrichment analysis revealed significant involvement of autophagy and apoptosis in biological processes. Next, we evaluated the top hub nodes based on their highest degrees. The ability of IgAN discrimination was confirmed in the GSE35487 and GSE37460 datasets by validating the five hub genes: SIRT1, FOS, CCL2, CDKN1A, and MYC. In the Nephroseq v5 analysis engine, the clinical correlation of the five hub genes was confirmed. Furthermore, the ceRNA network identified 18 circular RNAs and 2 microRNAs associated with hub autophagy-related genes in IgAN. Our investigation identified hsa-miR-32-3p and hsa-let-7i-5p as having elevated expression levels and substantial diagnostic value. Finally, four distinctively infiltrated immune cells were found to be associated with the hub autophagy-related genes, and 67 drugs were identified as potential therapeutic options for IgAN. Conclusion: This study sheds light on a novel ceRNA regulatory network mechanism associated with autophagy in IgAN development.

A study on construction of a prognosis model for liver cancer based on analgesic targets and screening therapeutic drugs.

Liver cancer is one of the most malignant liver diseases in the world, and the 5-year survival rate of such patients is low. Analgesics are often used to cure pain prevalent in liver cancer. The expression changes and clinical significance of the analgesic targets (ATs) in liver cancer have not been deeply understood. The purpose of this study is to clarify the expression pattern of ATs gene in liver cancer and its clinical significance. Through the comprehensive analysis of transcriptome data and clinical parameters, the prognosis model related to ATs gene is established, and the drug information sensitive to ATs is mined. The study primarily utilized transcriptomic data and clinical information from liver cancer patients sourced from The Cancer Genome Atlas (TCGA) database. These data were employed to analyze the expression of ATs, conduct survival analysis, gene set variation analysis (GSVA), immune cell infiltration analysis, establish a prognostic model, and perform other bioinformatic analyses. Additionally, data from liver cancer patients in the International Cancer Genome Consortium (ICGC) were utilized to validate the accuracy of the model. Furthermore, the impact of analgesics on key genes in the prognostic model was assessed using data from the Comparative Toxicogenomics Database (CTD). The study investigated the differential expression of 58 ATs genes in liver cancer compared to normal tissues. Patients were stratified based on ATs expression, revealing varied survival outcomes. Functional enrichment analysis highlighted distinctions in spindle organization, centrosome, and spindle microtubule functions. Prognostic modeling identified low TP53 expression as protective, while elevated CCNA2, NEU1, and HTR2C levels posed risks. Commonly used analgesics, including acetaminophen and others, were found to influence the expression of these genes. These findings provide insights into potential therapeutic strategies for liver cancer and shed light on the molecular mechanisms underlying its progression. The collective analysis of gene signatures associated with ATs suggests their potential as prognostic predictors in hepatocellular carcinoma patients. These findings not only offer insights into cancer therapy but also provide novel avenues for the development of indications for analgesics.

Exploring the molecular mechanisms of ferroptosis-related genes in periodontitis: a multi-dataset analysis

PurposeThis study aims to elucidate the biological functions of ferroptosis-related genes in periodontitis, along with their correlation to tumor microenvironment (TME) features such as immune infiltration. It aims to provide potential diagnostic markers of ferroptosis for clinical management of periodontitis.MethodsUtilizing the periodontitis-related microarray dataset GSE16134 from the Gene Expression Omnibus (GEO) and a set of 528 ferroptosis-related genes identified in prior studies, this research unveils differentially expressed ferroptosis-related genes in periodontitis. Subsequently, a protein–protein interaction network was constructed. Subtyping of periodontitis was explored, followed by validation through immune cell infiltration and gene set enrichment analyses. Two algorithms, randomForest and SVM(Support Vector Machine), were employed to reveal potential ferroptosis diagnostic markers for periodontitis. The diagnostic efficacy, immune correlation, and potential transcriptional regulatory networks of these markers were further assessed. Finally, potential targeted drugs for differentially expressed ferroptosis markers in periodontitis were predicted.ResultsA total of 36 ferroptosis-related genes (30 upregulated, 6 downregulated) were identified from 829 differentially expressed genes between 9 periodontitis samples and the control group. Subsequent machine learning algorithm screening highlighted 4 key genes: SLC1A5(Solute Carrier Family 1 Member 5), SLC2A14(Solute Carrier Family 1 Member 14), LURAP1L(Leucine Rich Adaptor Protein 1 Like), and HERPUD1(Homocysteine Inducible ER Protein With Ubiquitin Like Domain 1). Exploration of these 4 key genes, supported by time-correlated ROC analysis, demonstrated reliability, while immune infiltration results indicated a strong correlation between key genes and immune factors. Furthermore, Gene Set Enrichment Analysis (GSEA) was conducted for the four key genes, revealing enrichment in GO/KEGG pathways that have a significant impact on periodontitis. Finally, the study predicted potential transcriptional regulatory networks and targeted drugs associated with these key genes in periodontitis.ConclusionsThe ferroptosis-related genes identified in this study, including SLC1A5, SLC2A14, LURAP1L, and HERPUD1, may serve as novel diagnostic and therapeutic targets for periodontitis. They are likely involved in the occurrence and development of periodontitis through mechanisms such as immune infiltration, cellular metabolism, and inflammatory chemotaxis, potentially linking the ferroptosis pathway to the progression of periodontitis. Targeted drugs such as flurofamide, L-733060, memantine, tetrabenazine, and WAY-213613 hold promise for potential therapeutic interventions in periodontitis associated with these ferroptosis-related genes.

Identification of key genes and biological pathways associated with vascular aging in diabetes based on bioinformatics and machine learning.

Vascular aging exacerbates diabetes-associated vascular damage, a major cause of microvascular and macrovascular complications. This study aimed to elucidate key genes and pathways underlying vascular aging in diabetes using integrated bioinformatics and machine learning approaches. Gene expression datasets related to vascular smooth muscle cell (VSMC) senescence and diabetic vascular aging were analyzed. Differential expression analysis identified 428 genes associated with VSMC senescence. Functional enrichment revealed their involvement in cellular senescence, ECM-receptor interaction, PI3K-Akt and AGE-RAGE signaling pathways. Further analysis of diabetic vascular aging datasets revealed 52 differentially expressed genes, enriched in AMPK signaling, AGE-RAGE signaling, cellular senescence, and VEGF signaling pathways. Machine learning algorithms, including LASSO regression and SVM-RFE, pinpointed six key genes: TFB1M, FOXRED2, LY75, DALRD3, PI4K2B, and NDOR1. Immune cell infiltration analysis demonstrated correlations between diabetic vascular aging, the identified key genes, and infiltration levels of plasma cells, M1 macrophages, CD8+ T cells, eosinophils, and regulatory T cells. In conclusion, this study identified six pivotal genes (TFB1M, FOXRED2, LY75, DALRD3, PI4K2B, and NDOR1) closely associated with diabetic vascular aging through integrative bioinformatics and machine learning approaches. These genes are linked to alterations in the immune microenvironment during diabetic vascular aging. This study provides a reference and basis for molecular mechanism research, biomarker mining, and diagnosis and treatment evaluation of diabetes-related vascular aging.

Bioinformatics identification and validation of maternal blood biomarkers and immune cell infiltration in preeclampsia: An observational study.

Preeclampsia (PE) is a pregnancy complication characterized by placental dysfunction. However, the relationship between maternal blood markers and PE is unclear. It is helpful to improve the diagnosis and treatment of PE using new biomarkers related to PE in the blood. Three PE-related microarray datasets were obtained from the Gene Expression Synthesis database. The limma software package was used to identify differentially expressed genes (DEGs) between PE and control groups. Least absolute shrinkage and selection operator regression, support vector machine, random forest, and multivariate logistic regression analyses were used to determine key diagnostic biomarkers, which were verified using clinical samples. Subsequently, functional enrichment analysis was performed. In addition, the datasets were combined for immune cell infiltration analysis and to determine their relationships with core diagnostic biomarkers. The diagnostic performance of key genes was evaluated using the receiver operating characteristic (ROC) curve, C-index, and GiViTi calibration band. Genes with potential clinical applications were evaluated using decision curve analysis (DCA). Seventeen DEGs were identified, and 6 key genes (FN1, MYADM, CA6, PADI4, SLC4A10, and PPP4R1L) were obtained using 3 types of machine learning methods and logistic regression. High diagnostic performance was found for PE through evaluation of the ROC, C-index, GiViti calibration band, and DCA. The 2 types of immune cells (M0 macrophages and activated mast cells) were significantly different between patients with PE and controls. All of these genes except SLC4A10 showed significant differences in expression levels between the 2 groups using quantitative reverse transcription-polymerase chain reaction. This model used 6 maternal blood markers to predict the occurrence of PE. The findings may stimulate ideas for the treatment and prevention of PE.