Immune Cell Infiltration Levels Research Articles

Next-generation sequencing identified that RET variation associates with lymph node metastasis and the immune microenvironment in thyroid papillary carcinoma

BackgroundTo date, although most thyroid carcinoma (THCA) achieves an excellent prognosis, some patients experience a rapid progression episode, even with differentiated THCA. Nodal metastasis is an unfavorable predictor. Exploring the underlying mechanism may bring a deep insight into THCA.MethodsA total of 108 THCA from Chinese patients with next-generation sequencing (NGS) were recruited. It was used to explore the gene alteration spectrum of THCA and identify gene alterations related to nodal metastasis in papillary thyroid carcinoma (PTC). The Cancer Genome Atlas THCA cohort was further studied to elucidate the relationship between specific gene alterations and tumor microenvironment. A pathway enrichment analysis was used to explore the underlying mechanism.ResultsGene alteration was frequent in THCA. BRAF, RET, POLE, ATM, and BRCA1 were the five most common altered genes. RET variation was positively related to nodal metastasis in PTC. RET variation is associated with immune cell infiltration levels, including CD8 naïve, CD4 T and CD8 T cells, etc. Moreover, Step 3 and Step 4 of the cancer immunity cycle (CIC) were activated, whereas Step 6 was suppressed in PTC with RET variation. A pathway enrichment analysis showed that RET variation was associated with several immune-related pathways.ConclusionRET variation is positively related to nodal metastasis in Chinese PTC, and anti-tumor immune response may play a role in nodal metastasis triggered by RET variation.

A novel disulfide death-related genes prognostic signature identifies the role of IPO4 in glioma progression

Background“Disulfide death,” a form of cellular demise, is triggered by the abnormal accumulation of intracellular disulfides under conditions of glucose deprivation. However, its role in the prognosis of glioma remains undetermined. Therefore, the main objective of this study is to establish prognostic signature based on disulfide death-related genes (DDRGs) and to provide new solutions in choosing the effective treatment of glioma.MethodsThe RNA transcriptome, clinical information, and mutation data of glioma samples were sourced from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), while normal samples were obtained from the Genotype-Tissue Expression (GTEx). DDRGs were compiled from previous studies and selected through differential analysis and univariate Cox regression analysis. The molecular subtypes were determined through consensus clustering analysis. Further, LASSO analysis was employed to select characteristic genes, and subsequently, a risk model comprising seven DDRGs was constructed based on multivariable Cox analysis. Kaplan-Meier survival curves were employed to assess survival differences between high and low-risk groups. Additionally, functional analyses (GO, KEGG, GSEA) were conducted to explore the potential biological functions and signaling pathways of genes associated with the model. The study also explored immune checkpoint (ICP) genes, immune cell infiltration levels, and immune stromal scores. Finally, the effect of Importin-4(IPO4) on glioma has been further confirmed through RT-qPCR, Western blot, and cell functional experiments.Results7 genes associated with disulfide death were obtained and two subgroups of patients with different prognosis and clinical characteristics were identified. Risk signature was subsequently developed and proved to serve as an prognostic predictor. Notably, the high-risk group exhibited an immunosuppressive microenvironment characterized by a high concentration of M2 macrophages and regulatory T cells (Tregs). In contrast, the low-risk group showed lower half-maximal inhibitory concentration (IC50) values. Therefore, patients in the high-risk group may benefit more from immunotherapy, while patients in the low-risk group may benefit more from chemotherapy. In addition, in vitro experiments have shown that inhibition of the expression of IPO4 leads to a significant reduction in the proliferation, migration, and invasion of glioma cells.ConclusionThis study identified two glioma subtypes and constructed a prognostic signature based on DDRGs. The signature has the potential to optimize the selection of patients for immune- and chemotherapy and provided a potential therapeutic target for glioma.

Bioinformatics analysis of PANoptosis regulators in the diagnosis and subtyping of steroid-induced osteonecrosis of the femoral head.

In this study, we aimed to investigate the involvement of PANoptosis, a form of regulated cell death, in the development of steroid-induced osteonecrosis of the femoral head (SONFH). The underlying pathogenesis of PANoptosis in SONFH remains unclear. To address this, we employed bioinformatics approaches to analyze the key genes associated with PANoptosis. Our analysis was based on the GSE123568 dataset, allowing us to investigate both the expression profiles of PANoptosis-related genes (PRGs) and the immune profiles in SONFHallowing us to investigate the expression profiles of PRGs as well as the immune profiles in SONFH. We conducted cluster classification based on PRGs and assessed immune cell infiltration. Additionally, we used the weighted gene co-expression network analysis (WGCNA) algorithm to identify cluster-specific hub genes. Furthermore, we developed an optimal machine learning model to identify the key predictive genes responsible for SONFH progression. We also constructed a nomogram model with high predictive accuracy for assessing risk factors in SONFH patients, and validated the model using external data (area under the curve; AUC = 1.000). Furthermore, we identified potential drug targets for SONFH through the Coremine medical database. Using the optimal machine learning model, we found that 2 PRGs, CASP1 and MLKL, were significantly correlated with the key predictive genes and exhibited higher expression levels in SONFH. Our analysis revealed the existence of 2 distinct PANoptosis molecular subtypes (C1 and C2) within SONFH. Importantly, we observed significant variations in the distribution of immune cells across these subtypes, with C2 displaying higher levels of immune cell infiltration. Gene set variation analysis indicated that C2 was closely associated with multiple immune responses. In conclusion, our study sheds light on the intricate relationship between PANoptosis and SONFH. We successfully developed a risk predictive model for SONFH patients and different SONFH subtypes. These findings enhance our understanding of the pathogenesis of SONFH and offer potential insights into therapeutic strategies.

Transcriptome analysis revealed SMURF2 as a prognostic biomarker for oral cancer.

The activation of TGF-β pathway can facilitate tumorigenesis. Understanding the TGF-related genes (TRGs) in oral cancer and determining their prognostic value is of utmost importance. The TRGs were selected to develop a prognostic model based on lasso regression. Oral cancer patients were classified into high-risk and low-risk groups based on the risk model. Subsequently, multivariate COX regression was employed to identify the prognostic marker. Additionally, the expression of SMURF2 was validated using quantitative real-time polymerase chain reaction (qRT-PCR) and the Human Protein Atlas (HPA) database. To investigate the relationship between SMURF2 expression and immune cell infiltrations, we conducted single-sample Gene Set Enrichment Analysis (ssGSEA) analyses. We identified 16 differentially expressed TRGs in oral cancer, all of which showed upregulation. From these, we selected eight TRGs as prognostic signatures. Furthermore, the high-risk group demonstrated lower infiltration levels of immune cells, immune score, and higher tumor purity. Interestingly, we also found that SMURF2 serves as an independent prognostic biomarker. SMURF2 was upregulated in oral cancer, as confirmed by public databases and qRT-PCR analysis. Importantly, our results indicate a close association between SMURF2 expression and the immune microenvironment. The 8-TRG signature prognosis model that we constructed has the ability to predict the survival rate and immune activity of oral cancer patients. SMURF2 could be effective in recognizing prognosis and evaluating immune efficacy for oral cancer.

Bioinformatics analysis and validation of HAUS6 as a key prognostic gene in squamous cell carcinoma of the tongue

ObjectiveTo explore the expression of HAUS6 in squamous cell carcinoma of the tongue (TSCC) and its relationship with the clinicopathological features of patients, and to further provide new ideas and therapeutic targets for curing TSCC.DesignThe Cancer Genome Atlas (TCGA) database was used to screen for differentially expressed genes (DEGs) between TSCC and normal tissues and survival analysis. DEGs of HAUS6 were screened and analyzed for GO, KEGG and GSEA enrichment. Exploring the correlation of HAUS6 with immune cell infiltration and immune checkpoint-related genes. The expression of HAUS6 in tumor and paraneoplastic tissues was confirmed by immunohistochemistry and Western Blot. ResultsAnalysis of the TCGA database results showed that expression of HAUS6 mRNA was significantly enhanced and correlated with overall survival (OS, p<0.05) in TSCC. HAUS6 expression correlated with the level of immune cell infiltration and immune checkpoint-related genes. Immunohistochemistry and Western Blot confirmed that the expression level of HAUS6 protein was significantly higher in tumor tissues than in paraneoplastic tissues, and that tumor size and hypo-differentiation were higher in the HAUS6 high expression group than in the low expression group in TSCC (p<0.05). ConclusionsIn conclusion, these analyses suggest that HAUS6 can act as an independent predictor of prognosis (p<0.05) and high HAUS6 expression is strongly associated with poor prognosis.

Identification of a Gene Expression Signature to Predict the Risk of Early Recurrence and the Degree of Immune Cell Infiltration in Triple-negative Breast Cancer.

Patients with triple-negative breast cancer (TNBC) have a high rate of recurrence within 3 years of diagnosis and a high rate of death within 5 years compared to other subtypes. The number of clinical trials investigating various new agents and combination therapies has recently increased; however, current strategies benefit only a minority of patients. This study aimed to identify specific genes that predict patients at high risk of recurrence and the immune status of the tumor microenvironment at an early stage, thereby providing insight into potential therapeutic targets to improve clinical outcomes in TNBC patients. We evaluated the prognostic significance of microarray mRNA expression of 20,603 genes in 233 TNBC patients from the METABRIC dataset and further validated the results using RNA-seq mRNA expression data in 143 TNBC patients from the GSE96058 dataset. Eighteen differentially expressed genes (AKNA, ARHGAP30, CA9, CD3D, CD3G, CD6, CXCR6, CYSLTR1, DOCK10, ENO1, FLT3LG, IFNG, IL2RB, LPXN, PRKCB, PVRIG, RASSF5, and STAT4) identified in both datasets were found to be reliable biomarkers for predicting TNBC recurrence and progression. Notably, the genes whose low expression was associated with increased risk of recurrence and death were immune-related genes, with significant differences in levels of immune cell infiltration in the tumor microenvironment between high- and low- expression groups. Genes reported herein may be effective biomarkers to identify TNBC patients who will and will not benefit from immunotherapy and may be particularly important genes for developing future treatment strategies, including immunotherapy.

Establishment of a diagnostic model of endometriosis based on disulfidptosis-related genes.

We aimed to establish a diagnostic model of endometriosis (EM) based on disulfidptosis-related genes (DRGs). The mRNA expression data of EM were downloaded from the gene expression omnibus database and subjected to differential analysis, and co-expression analysis was performed based on 10 disulfidptosis genes to acquire DRGs. The differentially expressed DRGs were subjected to biofunctional analysis. Lasso analysis and support vector machine-recursive feature elimination (SVM-RFE) analysis were employed to extract the intersection of feature genes as biomarkers, and the diagnostic values of biomarkers for EM were evaluated based on receiver operating characteristic curves. The correlations between biomarkers and the immune microenvironment were assessed by Pearson analysis of biomarkers and immune cell infiltration levels. Transforming growth factor β stimulated protein clone 22 domain family member 4 (TSC22D4), and F-box/SPRY domain-containing protein 1 (FBXO45) worked as the diagnostic classifiers in EM, with an obvious decrease in FBXO45 expression and an evident increase in TSC22D4 expression. The areas under the curves of FBXO45 and TSC22D4 were 0.752 and 0.706, respectively, and the area of FBXO45 combined with TSC22D4 reached 0.865, suggesting that TSC22D4 and FBXO45 had high predictive values. The diagnostic markers were closely correlated with immune cell infiltration. The diagnostic markers constructed based on disulfidptosis are good predictors for EM, which have close correlations with EM.

Identification of Lung Adenocarcinoma Subtypes Based on MHC-II Gene Expression Profile and Immunological Analysis

Introduction: Major histocompatibility complex class II molecule (MHC-II) is pivotal in anti-tumor immunity, and targeting MHC-II in tumors may help improve patient survival. But function of MHC-II in the immunotherapy and prognosis of lung adenocarcinoma (LUAD) patients has not been thoroughly studied and reported. Methods: We selected LUAD-related MHC-II genes from public databases based on previous literature reports. We identified different subtypes according to expression differences of these genes in different LUAD samples through cluster analysis. We used R package to conduct a series of analyses on different subtypes, exploring their survival differences, gene expression differences, pathway enrichment differences, and differences in immune characteristics and immune therapy. Finally, we screened potential drugs from the cMAP database. Results: We identified two MHC-II-related LUAD subtypes. Our analyses presented that patients with cluster2 subtype showed better prognosis, higher immune scores, higher levels of immune cell infiltration and immune function activation. In addition, patients with this subtype had higher immunophenoscore, lower TIDE scores, and DEPTH scores. We also identified 10 small molecule drugs, such as lenalidomide, VX-745, and tyrphostin-AG-1295. Conclusion: Overall, MHC-II is not only a potential biomarker for accurately distinguishing LUAD subtypes but also a predictive factor for their survival. Our study offers novel insights into understanding of impact of MHC-II in LUAD and offers a new perspective for improving the accurate classification of LUAD patients and enhancing drug treatment.

Targeting the aryl hydrocarbon receptor with FICZ regulates IL-2 and immune infiltration to alleviate Hashimoto's thyroiditis in mice

Hashimoto's thyroiditis (HT) is the most frequent autoimmune disorder. Growing work points to the involvement of aryl hydrocarbon receptor (AhR), a ligand-dependent transcription factor, in the regulation of immune homeostasis. However, the roles of AhR and its ligands in HT remains unclear. In this study, we leveraged public human database analyses to postulate that the AhR expression was predominantly in thyroid follicular cells, correlating significantly with the thyroid infiltration levels of multiple immune cells in HT patients. Using a thyroglobulin-induced HT mouse model and in vitro thyroid follicular epithelial cell cultures, we found a significant downregulation of AhR expression in thyrocytes both in vivo and in vitro. Conversely, activating AhR by FICZ, a natural AhR ligand, mitigated inflammation and apoptosis in thyrocytes in vitro and conferred protection against HT in mice. RNA sequencing (RNA-seq) of thyroid tissues indicated that AhR activation moderated HT-associated immune or inflammatory signatures. Further, immunoinfiltration analysis indicated that AhR activation regulated immune cell infiltration in the thyroid of HT mice, such as suppressing cytotoxic CD8+ T cell infiltration and promoting anti-inflammatory M2 macrophage polarization. Concomitantly, the expression levels of interleukin-2 (IL-2), a lymphokine that downregulates immune responses, were typically decreased in HT but restored upon AhR activation. In silico validation substantiated the binding interaction between AhR and IL-2. In conclusion, targeting the AhR with FICZ regulates IL-2 and immune infiltration to alleviate experimental HT, shedding new light on the therapeutic intervention of this prevalent disease.

Bioinformatics analysis and experimental verification of NLRX1 as a prognostic factor for esophageal squamous cell carcinoma.

Nucleotide binding and oligomeric domain-like receptor X1 (NLRX1), a member of the NLR family, is associated with the physiological and pathological processes of inflammation, autophagy, immunity, metabolism and mitochondrial regulation, and has been demonstrated to have pro- or antitumor effects in various tumor types. However, the biological function of NLRX1 in esophageal squamous cell carcinoma (ESCC) has remained elusive. In the present study, by using bioinformatics methods, the differential expression of NLRX1 at the mRNA level was examined. Overall survival, clinical correlation, receiver operating characteristic curve, Cox regression, co-expression, enrichment, immune infiltration and drug sensitivity analyses were carried out. A nomogram and a calibration curve were constructed. Changes in protein expression levels were investigated by immunohistochemistry and western blotting. The impact of NLRX1 on i) cell proliferation was evaluated by Cell Counting Kit-8 assays; ii) migration was examined by wound-healing assays; iii) migration and invasion were evaluated by Transwell assays; and iv) apoptosis was assessed by Annexin V/PI staining and flow cytometry. The results revealed that, compared to normal adjacent tissue, NLRX1 was lowly expressed in ESCC, and patients with low NLRX1 expression had a shorter survival time. NLRX1 was an independent prognostic factor for ESCC and was associated with tumor grading. Patients in the low-NLRX1 group showed a decrease in the infiltration of activated natural killer cells, monocytes and M0 macrophages, and these immune-cell infiltration levels were positively correlated with NLRX1 expression. Knocking down NLRX1 promoted the proliferation of KYSE450 cells, while overexpression of NLRX1 inhibited the proliferation of ECA109 cells. NLRX1 negatively regulated the PI3K/AKT signaling pathway in ESCC. These findings indicate that, through several mechanisms, NLRX1 suppresses tumor growth in ESCC, which offers new insight for investigating the causes and progression of ESCC, as well as for identifying more efficient therapeutic approaches.

The vasculogenic mimicry related signature predicts the prognosis and immunotherapy response in renal clear cell carcinoma

BackgroundClear cell carcinoma of the kidney is a common urological malignancy characterized by poor patient prognosis and treatment outcomes. Modulation of vasculogenic mimicry in tumor cells alters the tumor microenvironment and the influx of tumor-infiltrating lymphocytes, and the combination of its inducers and immune checkpoint inhibitors plays a synergistic role in enhancing antitumor effects.MethodsWe downloaded the data from renal clear cell carcinoma samples and vasculogenic mimicry-related genes to establish a new vasculogenic mimicry-related index (VMRI) using a machine learning approach. Based on VMRI, patients with renal clear cell carcinoma were divided into high VMRI and low VMRI groups, and patients’ prognosis, clinical features, tumor immune microenvironment, chemotherapeutic response, and immunotherapeutic response were systematically analyzed. Finally, the function of CDH5 was explored in renal clear cell carcinoma cells.ResultsVMRI can be used for prognostic and immunotherapy efficacy prediction in a variety of cancers, which consists of four vasculogenic mimicry-related genes (CDH5, MMP9, MAPK1, and MMP13), is a reliable predictor of survival and grade in patients with clear cell carcinoma of the kidney and has been validated in multiple external datasets. We found that the high VMRI group presented higher levels of immune cell infiltration, which was validated by pathological sections. We performed molecular docking prediction of vasculogenic mimicry core target proteins and identified natural small molecule drugs with the highest affinity for the target protein. Knockdown of CDH5 inhibited the proliferation and migration of renal clear cell carcinoma.ConclusionsThe VMRI identified in this study allows for accurate prognosis assessment of patients with renal clear cell carcinoma and identification of patient populations that will benefit from immunotherapy, providing valuable insights for future precision treatment of patients with renal clear cell carcinoma.

Integrated Analysis Construct a Tumor-Associated Macrophage Novel Signature with Promising Implications in Predicting the Prognosis and Immunotherapeutic Response of Gastric Cancer Patients.

Gastric cancer (GC) remains one of the most prevalent malignant tumors worldwide. At present, tumor-associated macrophages (TAMs) are essential in the progression, metastasis, and drug resistance of tumors. Therefore, TAMs can be a crucial target for tumor treatment. We intended to investigate the TAM characteristics in GC and develop a risk signature based on TAM to predict the prognosis of GC patients. The single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data were acquired from a publicly available database. We utilized the Seurat pipeline to process the scRNA-seq data and determine TAM cell types using marker genes. Univariate Cox regression analysis was utilized to examine TAM-related prognostic genes, and then we employed Lasso-Cox regression analysis, and Multivariate Cox regression analysis established a novel risk profile to forecast the clinical value of the model with a new nomogram combining risk profiles and clinicopathological characteristics. The current study employed scRNA-seq data to identify five TAM clusters in GC, among which four were significantly associated with GC prognosis. Accordingly, we further developed a TAM-related risk signature utilizing nine genes. After evaluation, our model accurately predicted the prognosis of gastric cancer. Generally, GC patients with low TAMS scores exhibited a more favorable prognosis, greater benefits from immunotherapy, and higher levels of immune cell infiltration. The prognosis of GC can be effectively predicted by TAM-based risk signatures, and the signature may provide a new perspective for comprehensively guiding clinical diagnosis, prediction, and immunotherapy for gastric cancer.

Analysis of m6A-regulated genes and subtype classification in lupus nephritis

BackgroundLupus nephritis (LN) is the most common and severe clinical manifestation of systemic lupus erythematosus (SLE). N6-methyladenosine (m6A) is a reversible RNA modification and has been implicated in various biological processes. However, the roles of m6A regulators in LN are not fully demonstrated.MethodsWe downloaded the kidney tissue transcriptome dataset of LN patients and normal controls from the GEO database and extracted the expression levels of m6A regulators. We constructed and compared Random Forest (RF) and Support Vector Machine (SVM) models, and subsequently selected featured genes to develop nomogram models. The m6A subtypes were identified based on significantly differentially expressed m6A regulators, and the m6A gene subtypes were identified based on m6A-associated differential genes, and the two m6A modification patterns were comprehensively evaluated.ResultsWe obtained the GSE32591 and GSE112943 datasets from the GEO database, including 78 LN samples and 36 normal control samples. We extracted the expression levels of 20 m6A regulators. By RF analysis we identified 7 characteristic m6A regulators and constructed nomogramh models with these 7 genes. We identified two m6A subtypes based on these seven important m6A regulators, and the immune cell infiltration levels of the two subtype clusters were significantly different. We identified two more m6A gene subtypes based on m6A-associated DEGs. We calculated the m6A scores using the principal component analysis (PCA) algorithm and found that the m6A scores of m6A cluster A and gene cluster A were lower than those of m6A cluster B and gene cluster B. In addition, we found that the levels of inflammatory factors were also significantly different between m6A clusters and gene clusters.ConclusionThis study confirms that m6A regulators are involved in the LN process through different modes of action and provide new diagnostic and therapeutic targets for LN.

E2F-mediated POLA2 upregulation is correlated with macrophage infiltration and poor prognosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the major histological type of primary liver cancer with a relatively poor prognosis, because most HCC cases are usually diagnosed at an advanced stage. Thus, it is essential to explore novel candidate biomarkers for early diagnosis and prognosis predication of HCC. HCC transcription sequencing data were downloaded and analyzed. POLA2 expression pattern was characterized in tumor development process. POLA2 expression was evaluated by western blotting. Kaplan-Meier plot was utilized to evaluate POLA2's ability for prognosis predication. Correlation analysis and enrichment analysis were performed to explore the functional role of POLA2 in HCC development. Knockdown of E2F1 or E2F4 was used to evaluate their ability in regulating POLA2 expression. CYBERSORT (https://cibersortx.stanford.edu/) was used to estimate the infiltration levels of immune cells. POLA2 was overexpressed in HCC. Moreover, western blotting results showed that POLA2 was upregulated by transcription factors E2F1 rather than E2F4 in HCC. POLA2 facilitated cell cycle progression, DNA replication and DNA repair. High POLA2 expression was correlated with poor overall survival in HCC patients. POLA2 induced macrophage infiltration in the tumor microenvironment by upregulating the expression CSF1 and VEGFA expression. POLA2 is a novel diagnostic and prognostic biomarker of HCC with potential clinical value.

Identification of immune-related prognostic biomarkers in triple-negative breast cancer.

Triple-negative breast cancer (TNBC), a type of breast cancer, lacks immune-related markers that can be used for prognosis or prediction. Therefore, we created a predictive framework for TNBC using a risk assessment. Our previous study group consisted of 360 individuals who were diagnosed with TNBC through pathology using RNA sequencing and had clinical data from Fudan University Shanghai Cancer Center (FUSCC). A risk scoring model was constructed using the Cox regression method with the least absolute shrinkage and selection operator (LASSO). A multivariate Cox regression analysis was utilized to develop the prediction model, which was then assessed using the consistency index and calibration plots. The validation cohort of The Cancer Genome Atlas (TCGA) TNBC confirmed the strength of the signatures' predictive value. The prognostic risk score model included 12 genes: TDO2, CHIT1, CARML2, HLA-C, ADIRF, C19orf33, CA8, AHNAK2, RHOV, OPLAH, THEM6, and NEBL. The receiver operator characteristic (ROC) curves for survivability values at 1, 3, and 5 years in the FUSCC TNBC cohort demonstrated area under the curve (AUC) values of 0.78, 0.83, and 0.75, respectively. These results indicated a high level of accuracy in predicting outcomes, which was further confirmed through validation using TCGA database. The patients in the high-risk group showed worse prognoses and lower levels of immune cell infiltration, specifically CD8+ T cells, than those in the low-risk group. Furthermore, the low-risk group exhibited a significant upregulation of genes that encode immune checkpoints, including CD274 and CTLA4, suggesting that immunotherapy may yield enhanced efficacy within this particular group. In conclusion, the prognostic signature consisting of 12 genes can assist in the choice of immunotherapy for TNBC.

Downregulation of NAT1 Expression is Associated with Poor Prognosis and Immune Infiltration in COAD.

An increasing corpus of evidence has identified the involvement of N-acetyltransferase 1 (NAT1), a member of the NAT family, in the progression of various cancers. However, the specific function of NAT1 in colon cancer (COAD) remains elusive. This study aims to decip her the role of NAT1 in COAD and its associated mechanisms. The Tumor Immunity Evaluation Resource (TIMER), The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO) databases were employed to assess the NAT1 expression level in COAD. The differential expression between COAD and normal colon tissue was further validated using quantitative real-time reverse-transcription PCR (RT-qPCR) and Western blot (WB) analyses. Additionally, survival analysis of NAT1 in COAD was carried out using the PrognoScan database and TCGA dataset. The functions of NAT1 were explored through gene set enrichment analysis (GSEA) and immuno-infiltration analysis. There was a significant reduction in NAT1 expression in COAD samples compared to normal tissue. Notably, low NAT1 expression in COAD correlated significantly with various clinical parameters such as tumor stage (T stage, N stage, M stage, pathologic stage), primary therapy outcome, carcinoembryonic antigen (CEA) level, and lymphatic invasion. The downregulation of NAT1 was also strongly linked with poor outcomes in overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS). Cox regression analysis highlighted NAT1 as an independent prognostic indicator for overall survival in COAD patients. GSEA results revealed NAT1's involvement in multiple pathways, including the neuroactive ligand-receptor interaction, olfactory transduction, olfactory signaling, extracellular matrix receptor interaction, calcium signaling, and focal adhesion pathways. Furthermore, NAT1 expression was found to significantly correlate with infiltration levels of various immune cells. The findings reveal NAT1's potential as a valuable prognostic biomarker for COAD. Moreover, its associated mechanisms offer insights that might pave the way for therapeutic interventions for COAD patients.

Potential value of expression of receptor accessory protein 4 for evaluating the prognosis of lower-grade glioma patients.

REEP4 is involved in the regulation of the biological process of mitosis. Lower grade glioma (LGG), as a malignant tumor, is accompanied by abnormalities in mitosis, but there have been no reports of REEP4 so far. We collected transcriptome data, DNA methylation data and the clinical characteristics of thousands of patients with LGG. Various big data analysis methods and molecular biology experiments were employed to reveal the impact of REEP4 on the pathological process of LGG. It was found that the expression of REEP4 was significantly elevated and negatively regulated by its methylation site. Therefore, both the high expression of REEP4 and low methylation state of cg16311504 showed that the patients are correlated with lower patient survival rate. In addition, high REEP4 expression participates in the regulation of various cancer-related cellular signaling pathways, such as the cell cycle, MAPK signaling pathway, NOD-like receptor signaling pathway, etc. More importantly, the level of immune cell infiltration significantly increased in the high expression group of REEP4 in the LGG tumor microenvironment and REEP4 has a high positive correlation with PD-L1 and other immune checkpoints. In brief, this study is the first to introduce REEP4 in malignant tumors, which can be used as an independent risk factor that participates in the malignant process of LGG. More importantly, REEP4 has the potential to become a new star in the field of anti-tumor treatment.

The prognostic model and immune landscape based on cancer-associated fibroblast features for patients with locally advanced rectal cancer

BackgroundThis study aimed to construct a nomogram based on CAF features to predict the cancer-specific survival (CSS) rates of locally advanced rectal cancer (LARC) patients. MethodsThe EPIC algorithm was employed to calculate the proportion of CAFs. based on the differentially expressed genes between the high and low CAF proportion subgroups, prognostic genes were identified via LASSO and Cox regression analyses. They were then used to construct a prognostic risk signature. Moreover, the GSE39582 and GGSE38832 datasets were used for external validation. Lastly, the level of immune infiltration was evaluated using ssGSEA, ESTIMATE, CIBERSORTx, and TIMER. ResultsA higher level of CAF infiltration was associated with a worse prognosis. Additionally, the number of metastasized lymph nodes and distant metastases, as well as the level of immune infiltration were higher in the high CAF proportion subgroup. Five prognostic genes (SMOC2, TUBAL3, C2CD4A, MAP1B, BMP8A) were identified and subsequently incorporated into the prognostic risk signature to predict the 1-, 3-, and 5-year CSS rates in the training and validation sets. Differences in survival rates were also determined in the external validation cohort. Furthermore, independent prognostic factors, including TNM stage and risk score, were combined to established a nomogram. Notably, our results revealed that the proportions of macrophages and neutrophils and the levels of cytokines secreted by M2 macrophages were higher in the high-risk subgroup. Finally, the prognostic genes were significantly associated with the level of immune cell infiltration. ConclusionHerein, a nomogram based on CAF features was developed to predict the CSS rate of LARC patients. The risk model was capable of reflecting differences in the level of immune cell infiltration.

Identification of HDAC9 and ARRDC4 as potential biomarkers and targets for treatment of type 2 diabetes

We aimed to identify the key potential insulin resistance (IR)-related genes and investigate their correlation with immune cell infiltration in type 2 diabetes (T2D). The GSE78721 dataset (68 diabetic patients and 62 controls) was downloaded from the Gene Expression Omnibus database and utilized for single-sample gene set enrichment analysis. IR-related genes were obtained from the Comparative Toxicology Genetics Database, and the final IR-differentially expressed genes (DEGs) were screened by intersecting with the DEGs obtained from the GSE78721 datasets. Functional enrichment analysis was performed, and the networks of the target gene with microRNA, transcription factor, and drug were constructed. Hub genes were identified based on a protein–protein interaction network. Least absolute shrinkage and selection operator regression and Random Forest and Boruta analysis were combined to screen diagnostic biomarkers in T2D, which were validated using the GSE76894 (19 diabetic patients and 84 controls) and GSE9006 (12 diabetic patients and 24 controls) datasets. Quantitative real-time polymerase chain reaction was performed to validate the biomarker expression in IR mice and control mice. In addition, infiltration of immune cells in T2D and their correlation with the identified markers were computed using CIBERSORT. We identified differential immune gene set regulatory T-cells in the GSE78721 dataset, and T2D samples were assigned into three clusters based on immune infiltration. A total of 2094 IR-DEGs were primarily enriched in response to endoplasmic reticulum stress. Importantly, HDAC9 and ARRDC4 were identified as markers of T2D and associated with different levels of immune cell infiltration. HDAC9 mRNA level were higher in the IR mice than in control mice, while ARRDC4 showed the opposite trend. In summary, we discovered potential vital biomarkers that contribute to immune cell infiltration associated with IR, which offers a new sight of immunotherapy for T2D.

Immunological profile of lactylation-related genes in Crohn’s disease: a comprehensive analysis based on bulk and single-cell RNA sequencing data

BackgroundCrohn's disease (CD) is a disease characterized by intestinal immune dysfunction, often accompanied by metabolic abnormalities. Disturbances in lactate metabolism have been found in the intestine of patients with CD, but studies on the role of lactate and related Lactylation in the pathogenesis of CD are still unknown.MethodsWe identified the core genes associated with Lactylation by downloading and merging three CD-related datasets (GSE16879, GSE75214, and GSE112366) from the GEO database, and analyzed the functions associated with the hub genes and the correlation between their expression levels and immune infiltration through comprehensive analysis. We explored the Lactylation levels of different immune cells using single-cell data and further analyzed the differences in Lactylation levels between inflammatory and non-inflammatory sites.ResultsWe identified six Lactylation-related hub genes that are highly associated with CD. Further analysis revealed that these six hub genes were highly correlated with the level of immune cell infiltration. To further clarify the effect of Lactylation on immune cells, we analyzed single-cell sequencing data of immune cells from inflammatory and non-inflammatory sites in CD patients and found that there were significant differences in the levels of Lactylation between different types of immune cells, and that the levels of Lactylation were significantly higher in immune cells from inflammatory sites.ConclusionsThese results suggest that Lactylation-related genes and their functions are closely associated with changes in inflammatory cells in CD patients.