Endoplasmic reticulum stress promotes hepatocellular carcinoma by modulating immunity: a study based on artificial neural networks and single-cell sequencing.

INTRODUCTION: Hepatocellular carcinoma (HCC) is characterized by the complex pathogenesis, limited therapeutic methods, and poor prognosis. Endoplasmic reticulum stress (ERS) plays an important role in the development of HCC, therefore, we still need further study of molecular mechanism of HCC and ERS for early diagnosis and promising treatment targets. METHOD: The GEO datasets (GSE25097, GSE62232, and GSE65372) were integrated to identify differentially expressed genes related to HCC (ERSRGs). Random Forest (RF) and Support Vector Machine (SVM) machine learning techniques were applied to screen ERSRGs associated with endoplasmic reticulum stress, and an artificial neural network (ANN) diagnostic prediction model was constructed. The ESTIMATE algorithm was utilized to analyze the correlation between ERSRGs and the immune microenvironment. The potential therapeutic agents for ERSRGs were explored using the Drug Signature Database (DSigDB). The immunological landscape of the ERSRGs central gene PPP1R16A was assessed through single-cell sequencing and cell communication, and its biological function was validated using cytological experiments. RESULTS: An ANN related to the ERS model was constructed based on SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1. The area under the curve (AUC) of the model in the training set was 0.979, and the AUC values in three validation sets were 0.958, 0.936, and 0.970, respectively, indicating high reliability and effectiveness. Spearman correlation analysis suggests that the expression levels of ERSRGs are significantly correlated with immune cell infiltration and immune-related pathways, indicating their potential as important targets for immunotherapy. Mometasone was predicted to be the most promising treatment drug based on its highest binding score. Among the six ERSRGs, PPP1R16A had the highest mutation rate, predominantly copy number mutations, which may be the core gene of the ERSRGs model. Single-cell analysis and cell communication indicated that PPP1R16A is predominantly distributed in liver malignant parenchymal cells and may reshape the tumor microenvironment by enhancing macrophage migration inhibitory factor (MIF)/CD74 + CXCR4 signaling pathways. Functional experiments revealed that after siRNA knockdown, the expression of PPP1R16A was downregulated, which inhibited the proliferation, migration, and invasion capabilities of HCCLM3 and Hep3B cells in vitro. CONCLUSION: The consensus of various machine learning algorithms and artificial intelligence neural networks has established a novel predictive model for the diagnosis of liver cancer associated with ERS. This study offers a new direction for the diagnosis and treatment of HCC.

Exploring the Roles of m6A-Modified circRNAs in Myasthenia Gravis Based on Multi-Omics Analysis.

Myasthenia gravis (MG) is an autoimmune disease mediated by autoantibodies. The important roles of circRNAs modified by m6A methylation have been reported in the pathogenesis of other autoimmune diseases, but remain unclear in MG. To address this point, we collected peripheral blood mononuclear cells from six MG patients and six healthy controls and performed m6A­circRNA epitranscriptomic microarray and RNA sequencing. Differentially m6A-modified circRNAs and differentially expressed genes (DEGs) were analyzed. A network was constructed containing 17 circRNAs, 30 miRNAs, and 34 DEGs. The GSE85452 dataset was downloaded. DEGs that were differentially expressed in the GSE85452 dataset were selected as seed genes. Finally, four candidate m6A-modified circRNAs (hsa_circ_0084735, hsa_circ_0018652, hsa_circ_0025731, and hsa_circ_0030997) were identified through a random walk with restart. We found that they had different degree correlations with different immune cells. The results of MeRIP-qPCR showed that the m6A methylated levels of hsa_circ_0084735 and hsa_circ_0025731 were downregulated in MG patients, while the other two circRNAs were not significantly different between MG and control group. For the first time, we explored the pathogenesis of MG at the epigenetic transcriptome level. Our results will open new perspectives for MG research and identify potential biomarkers and therapeutic targets for MG.

A comprehensive pan-cancer analysis revealing SPAG6 as a novel diagnostic, prognostic and immunological biomarker in tumor.

Background: There have been studies on the role of sperm-associated antigen 6 (SPAG6) in cytoskeleton formation and growth cone stability, but it is also unknown how spag6 affect tumor growth and development. The aim of this study was to clarify the role of SPAG6 in pan-cancer, with some findings about thyroid carcinoma (THCA) validated through experiments. Methods: We examined the role of SPAG6 in pan-cancer, with the data being collected from databases. Further analysis was conducted to assess its correlations with prognosis, gene heterogeneity, stemness, and tumor immunity. The interacting proteins of SPAG6 were also identified, and gene ontology enrichment analysis was performed to determine its biological function. We preliminarily confirmed the role of SPAG6 via in vitro experiments and immunofluorescence staining. Results: This study found that SPAG6 expression was differentially expressed in cancers and at various tumor stages and grades. In stomach and esophageal carcinoma (STES), stomach adenocarcinoma (STAD), kidney renal clear cell carcinoma (KIRC), lung squamous cell carcinoma (LUSC), and adrenocortical carcinoma (ACC), SPAG6 expression was correlated with gender. SPAG6 expression was also found to be correlated with prognostic value, with low expression being associated with poor prognosis. Furthermore, SPAG6 expression was positively linked with immune-related cells in HNSC, chemokine receptors in LUSC, and immune checkpoint genes in THCA. Furthermore, SPAG6 overexpression suppressed the malignant phenotypes of THCA cells, manifested by slower proliferation and decreased migration. The different SPAG6 expression in THCA led to different malignant phenotypes, which are involved in the upregulation of DNA repair, MYC targets, peroxisome, and G2M checkpoint. Conclusions: SPAG6 plays a significant role as an oncogene and can be used as a marker to predict the prognosis of cancer. SPAG6 influences both the tumor immune infiltration and microenvironment, making it a promising immunotherapeutic target for tumor therapy.

A novel mitochondrial function-associated programmed cell death-related prognostic signature for predicting the prognosis of early breast cancer.

Purpose: To screen mitochondrial function-associated PCD-related biomarkers and construct a risk model for predicting the prognosis of early breast cancer. Methods: Data on gene expression levels and clinical information were obtained from the TCGA database, and GSE42568 and GSE58812 datasets were obtained from GEO database. The mitochondrial function-associated programmed cell death (PCD) related genes in early breast cancer were identified, then LASSO logistic regression, SVM-RFE, random forest (RF), and multiple Cox logistic regression analysis were employed to construct a prognostic risk model. Differences in immune infiltration, drug sensitivity, and immunotherapy response were evaluated between groups. Lastly, the qRT-PCR was employed to confirm the key genes. Results: Total 1,478 DEGs were screened between normal and early breast cancer groups, and these DEGs were involved in PI3K-Akt signaling pathway, focal adhesion, and ECM-receptor interaction pathways. Then total 178 mitochondrial function-associated PCD related genes were obtained, followed by a four mitochondrial function-associated PCD related genes prognostic model and nomogram were built. In addition, total 2 immune checkpoint genes were lowly expressed in the high-risk group, including CD47 and LAG3, and the fraction of some immune cells in high- and low-risk groups had significant difference, such as macrophage, eosinophil, mast cell, etc., and the Top3 chemotherapeutics with significant differences were included FH535, MK.2206, and bicalutamide. Finally, the qRT-qPCR results shown that the CREB3L1, CAPG, SPINT1 and GRK3 mRNA expression were in line with the bioinformatics analysis results. Conclusion: Four mitochondrial function-associated PCD-related genes were identified, including CREB3L1, CAPG, SPINT1, and GRK3, and the prognostic risk model and nomogram were established for predicting the survival of early breast cancer patient. The chemotherapeutics, containing FH535, MK.2206, and bicalutamide, might be used for early breast cancer.

Machine learning-based autophagy-related prognostic signature for personalized risk stratification and therapeutic approaches in bladder cancer.

OBJECTIVE: Bladder cancer (BCa) is a highly lethal urological malignancy characterized by its notable histological heterogeneity. Autophagy has swiftly emerged as a diagnostic and prognostic biomarker in diverse cancer types. Nonetheless, the currently accessible autophagy-related signature specific to BCa remains limited. METHODS: A refined autophagy-related signature was developed through a 10-fold cross-validation framework, incorporating 101 combinations of machine learning algorithms. The performance of this signature in predicting prognosis and response to immunotherapy was thoroughly evaluated, along with an exploration of potential drug targets and compounds. In vitro and in vivo experiments were conducted to verify the regulatory mechanism of hub gene. RESULTS: The autophagy-related prognostic signature (ARPS) has exhibited superior performance in predicting the prognosis of BCa compared to the majority of clinical features and other developed markers. Higher ARPS is associated with poorer prognosis and reduced sensitivity to immunotherapy. Four potential targets and five therapeutic agents were screened for patients in the high-ARPS group. In vitro and vivo experiments have confirmed that FKBP9 promotes the proliferation, invasion, and metastasis of BCa. CONCLUSIONS: Overall, our study developed a valuable tool to optimize risk stratification and decision-making for BCa patients.

Investigation of prognostic values of immune infiltration and LGMN expression in the microenvironment of osteosarcoma.

BACKGROUND: Osteosarcoma (OS), the most common primary malignant bone tumor, predominantly affects children and young adults and is characterized by high invasiveness and poor prognosis. Despite therapeutic advancements, the survival rate remains suboptimal, indicating an urgent need for novel biomarkers and therapeutic targets. This study aimed to investigate the prognostic significance of LGMN expression and immune cell infiltration in the tumor microenvironment of OS. METHODS: We performed an integrative bioinformatics analysis utilizing the GEO and TARGET-OS databases to identify differentially expressed genes (DEGs) associated with LGMN in OS. We conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to explore the biological pathways and functions. Additionally, we constructed protein-protein interaction (PPI) networks, a competing endogenous RNA (ceRNA) network, and applied the CIBERSORT algorithm to quantify immune cell infiltration. The diagnostic and prognostic values of LGMN were evaluated using the area under the receiver operating characteristic (ROC) curve and Cox regression analysis. Furthermore, we employed Consensus Clustering Analysis to explore the heterogeneity within OS samples based on LGMN expression. RESULTS: The analysis revealed significant upregulation of LGMN in OS tissues. DEGs were enriched in immune response and antigen processing pathways, suggesting LGMN's role in immune modulation within the TME. The PPI and ceRNA network analyses provided insights into the regulatory mechanisms involving LGMN. Immune cell infiltration analysis indicated a correlation between high LGMN expression and increased abundance of M2 macrophages, implicating an immunosuppressive role. The diagnostic AUC for LGMN was 0.799, demonstrating its potential as a diagnostic biomarker. High LGMN expression correlated with reduced overall survival (OS) and progression-free survival (PFS). Importantly, Consensus Clustering Analysis identified two distinct subtypes of OS, highlighting the heterogeneity and potential for personalized medicine approaches. CONCLUSIONS: Our study underscores the prognostic value of LGMN in osteosarcoma and its potential as a therapeutic target. The identification of LGMN-associated immune cell subsets and the discovery of distinct OS subtypes through Consensus Clustering Analysis provide new avenues for understanding the immunosuppressive TME of OS and may aid in the development of personalized treatment strategies. Further validation in larger cohorts is warranted to confirm these findings.

Reparative effects after low-dose radiation exposure: Inhibition of atherosclerosis by reducing NETs release.

OBJECTIVE: The cardiovascular system effects of environmental low-dose radiation exposure on radiation practitioners remain uncertain and require further investigation. The aim of this study was to initially investigate and explore the mechanisms by which low-dose radiation may contribute to atherosclerosis through a multi-omics joint comprehensive basic experiment. METHODS: We used WGCNA and differential analyses to identify shared genes and potential pathways between radiation injury and atherosclerosis sequencing datasets, as well as tissue transcriptome immune infiltration level extrapolation and single-cell transcriptome data correction using the CIBERSORT deconvolution algorithm. Animal models were constructed by combining a high-fat diet with 5 Gy γ-ray whole-body low-dose ionizing radiation. The detection of NETs release was validated by enzyme-linked immunosorbent assay. RESULTS: Analysis reveals shared genes in both datasets of post-irradiation and atherosclerosis, suggesting that immune system neutrophils may be a key node connecting radiation to atherosclerosis. NETs released by neutrophil death can influence the development of atherosclerosis. Animal experiments showed that the number of neutrophils decreased (P < 0.05) and the concentration of NETs reduced after low-dose radiation compared with the control group, and the concentration of NETs significantly increased (P < 0.05) in the HF group. Endothelial plaques were significantly increased in the high-fat feed group and significantly decreased in the low-dose radiation group compared with the control group. CONCLUSIONS: Long-term low-dose ionizing radiation exposure stimulates neutrophils and inhibits their production of NETs, resulting in inhibition of atherosclerosis.

Exploring the role of pyroptosis and immune infiltration in sepsis based on bioinformatic analysis.

PURPOSE: Sepsis is a disease that is typically treated in intensive care units with high mortality and morbidity. Pyroptosis is a newly identified type of programmed cell death and is characterized by inflammatory cytokine secretion. However, the role of pyroptosis in sepsis remains unclear. METHODS: GSE28750 and GSE134347 datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed pyroptosis genes (DEPGs) were identified between sepsis and healthy controls. Machine learning was used to further narrow the gene range. Receiver operating curves (ROC) were generated to estimate the diagnostic efficacy. Immune infiltration levels were estimated via single-sample gene set enrichment analysis (ssGSEA). A network database was used to predict the upstream transcription factors and miRNAs of DEPGs. Finally, the expression of the genes was validated by qRT-PCR between sepsis patients and healthy controls. RESULTS: We found that the pyroptosis pathway was enriched and activated in sepsis. 8 DEPGs were identified. A heatmap showed that the genes, NLRC4, NAIP, IL-18, AIM2 and ELANE, were abundant in the sepsis samples, and the genes, NLRP1, CHMP7 and TP53, were abundant in the healthy control samples. The ssGSEA results showed that the abundances of activated dendritic cells, MDSC, macrophage, plasmacytoid dendritic cells, regulatory T-cells, and Th17-cells were significantly higher, while the activated B-cell, activated CD8 T-cell, CD56 dim tural killer cell, immature B-cell, monocyte, and T follicular helper cell abundances were lower in sepsis samples compared to healthy controls. The qRT-PCR results showed that the expression levels of NAIP, IL-18, TP53, CHMP7, NLRC4, ELANE and NLRP1 were consistant with the bioinformatic analyses, while the expression level of AIM2 has no significant difference. CONCLUSION: Our study identified seven potential pyroptosis-related genes, NAIP, IL-18, TP53, CHMP7, NLRC4, ELANE and NLRP1. This study revealed that pyroptosis may promote sepsis development by activating the immune response.

ETS1-mediated Regulation of SOAT1 Enhances the Malignant Phenotype of Oral Squamous Cell Carcinoma and Induces Tumor-associated Macrophages M2-like Polarization.

Oral squamous cell carcinoma (OSCC) is an aggressive cancer that poses a substantial threat to human life and quality of life globally. Lipid metabolism reprogramming significantly influences tumor development, affecting not only tumor cells but also tumor-associated macrophages (TAMs) infiltration. SOAT1, a critical enzyme in lipid metabolism, holds high prognostic value in various cancers. This study revealed that SOAT1 is highly expressed in OSCC tissues and positively correlated with M2 TAMs infiltration. Increased SOAT1 expression enhanced the capabilities of cell proliferation, tumor sphere formation, migration, and invasion in OSCC cells, upregulated the SREBP1-regulated adipogenic pathway, activated the PI3K/AKT/mTOR pathway and promoted M2-like polarization of TAMs, thereby contributing to OSCC growth both in vitro and in vivo. Additionally, we explored the upstream transcription factors that regulate SOAT1 and discovered that ETS1 positively regulates SOAT1 expression levels. Knockdown of ETS1 effectively inhibited the malignant phenotype of OSCC cells, whereas restoring SOAT1 expression significantly mitigated this suppression. Based on these findings, we suggest that SOAT1 is regulated by ETS1 and plays a pivotal role in the development of OSCC by facilitating lipid metabolism and M2-like polarization of TAMs. We propose that SOAT1 is a promising target for OSCC therapy with tremendous potential.

Complement factor I knockdown inhibits colon cancer development by affecting Wnt/ß-catenin/c-Myc signaling pathway and glycolysis.

BACKGROUND: Colon cancer (CC) occurrence and progression are considerably influenced by the tumor microenvironment. However, the exact underlying regulatory mechanisms remain unclear. AIM: To investigate immune infiltration-related differentially expressed genes (DEGs) in CC and specifically explored the role and potential molecular mechanisms of complement factor I (CFI). METHODS: Immune infiltration-associated DEGs were screened for CC using bioinformatics. Quantitative reverse transcription polymerase chain reaction was used to examine hub DEGs expression in the CC cell lines. Stable CFI-knockdown HT29 and HCT116 cell lines were constructed, and the diverse roles of CFI in vitro were assessed using CCK-8, 5-ethynyl-2'-deoxyuridine, wound healing, and transwell assays. Hematoxylin and eosin staining and immunohistochemistry staining were employed to evaluate the influence of CFI on the tumorigenesis of CC xenograft models constructed using BALB/c male nude mice. Key proteins associated with glycolysis and the Wnt pathway were measured using western blotting. RESULTS: Six key immune infiltration-related DEGs were screened, among which the expression of CFI, complement factor B, lymphoid enhancer binding factor 1, and SRY-related high-mobility-group box 4 was upregulated, whereas that of fatty acid-binding protein 1, and bone morphogenic protein-2 was downregulated. Furthermore, CFI could be used as a diagnostic biomarker for CC. Functionally, CFI silencing inhibited CC cell proliferation, migration, invasion, and tumor growth. Mechanistically, CFI knockdown downregulated the expression of key glycolysis-related proteins (glucose transporter type 1, hexokinase 2, lactate dehydrogenase A, and pyruvate kinase M2) and the Wnt pathway-related proteins (ß-catenin and c-Myc). Further investigation indicated that CFI knockdown inhibited glycolysis in CC by blocking the Wnt/ß-catenin/c-Myc pathway. CONCLUSION: The findings of the present study demonstrate that CFI plays a crucial role in CC development by influencing glycolysis and the Wnt/ß-catenin/c-Myc pathway, indicating that it could serve as a promising target for therapeutic intervention in CC.

Identification and validation of cuproptosis-related genes in acetaminophen-induced liver injury using bioinformatics analysis and machine learning.

Background: Acetaminophen (APAP) is commonly used as an antipyretic analgesic. However, acetaminophen overdose may contribute to liver injury and even liver failure. Acetaminophen-induced liver injury (AILI) is closely related to mitochondrial oxidative stress and dysfunction, which play critical roles in cuproptosis. Here, we explored the potential role of cuproptosis-related genes (CRGs) in AILI. Methods: The gene expression profiles were obtained from the Gene Expression Omnibus database. The differential expression of CRGs was determined between the AILI and control samples. Protein protein interaction, correlation, and functional enrichment analyses were performed. Machine learning was used to identify hub genes. Immune infiltration was evaluated. The AILI mouse model was established by intraperitoneal injection of APAP solution. Quantitative real-time PCR and western blotting were used to validate hub gene expression in the AILI mouse model. The copper content in the mouse liver samples and AML12 cells were quantified using a colorimetric assay kit. Ammonium tetrathiomolybdate (ATTM), was administered to mouse models and AML12 cells in order to investigate the effects of copper chelator on AILI. Results: The analysis identified 7,809 differentially expressed genes, 4,245 of which were downregulated and 3,564 of which were upregulated. Four optimal feature genes (OFGs; SDHB, PDHA1, NDUFB2, and NDUFB6) were identified through the intersection of two machine learning algorithms. Further nomogram, decision curve, and calibration curve analyses confirmed the diagnostic predictive efficacy of the four OFGs. Enrichment analysis indicated that the OFGs were involved in multiple pathways, such as IL-17 pathway and chemokine signaling pathway, that are related to AILI progression. Immune infiltration analysis revealed that macrophages were more abundant in AILI than in control samples, whereas eosinophils and endothelial cells were less abundant. Subsequently, the AILI mouse model was successfully established, and histopathological analysis using hematoxylin-eosin staining along with liver function tests revealed a significant induction of liver injury in the APAP group. Consistent with expectations, both mRNA and protein levels of the four OFGs exhibited a substantial decrease. The administration of ATTAM effectively mitigates copper elevation induced by APAP in both mouse model and AML12 cells. However, systemic administration of ATTM did not significantly alleviate AILI in the mouse model. Conclusion: This study first revealed the potential role of CRGs in the pathological process of AILI and offered novel insights into its underlying pathogenesis.

Diagnostic value of selenoprotein changes in renal tissues for acute rejection of kidney transplantation as revealed by transcriptomics.

BACKGROUND: There seems to be a close link between the changing levels of selenoproteins, which are important for maintaining redox homeostasis in the body, and acute rejection of kidney transplants. The aim of this study was to explore the diagnostic value of selenoprotein change characteristics in renal tissues for acute rejection of kidney transplantation. METHODS: We first explored the potential biological functions of 25 selenoproteins in the human body by enrichment analysis and used the HPA database to clarify the expression levels of selenoproteins in kidney tissues; We then constructed a diagnostic model using "Logistic regression analysis" and "Nomogram model"; Calibration curves and ROC curves were used to evaluate the diagnostic models, and clinical decision curves (DCA) were used to assess the diagnostic value of selenoprotein changes to the clinic; Single-gene GSEA enrichment analysis to further explore the potential regulatory mechanisms of selenoproteins; The Cibersort algorithm explores the level of immune cell infiltration and uses correlation analysis to clarify the correlation between selenoproteins and immune cells; We further assessed the diagnostic value of selenoproteins in kidney transplantation ABMR and TCMR, respectively. Finally, we validated the expression level of selenoproteins in kidney tissues by constructing a rat model of acute rejection of kidney transplantation using transcriptome sequencing. RESULTS: Our enrichment analysis revealed that selenoproteins are mainly closely associated with biological functions such as oxidative stress, inflammation, and immune regulation (P<0.05); The HPA database suggests that a total of 23 selenoproteins can be expressed in kidney tissue. We constructed a diagnostic model using these 23 selenoproteins, and both calibration curves and ROC curves proved that their change levels have good diagnostic value for acute rejection of kidney transplantation, and DCA curves proved the role of selenoproteins in clinical decision-making; Single-gene GSEA enrichment analysis revealed that selenoproteins are closely associated with immune regulation-related pathways (P<0.05); The Cibersort algorithm identified 10 immune cell infiltration levels that were significantly altered during acute rejection of kidney transplantation (P<0.05), while correlation analyses indicated that selenoproteins correlate with multiple immune cell infiltrations; In ABMR and TCMR, we again verified the diagnostic value of selenoprotein changes in acute rejection of kidney transplantation. Finally, we found significant differences in the expression levels of nine selenoproteins in a rat model of acute rejection of kidney transplantation (P<0.05). CONCLUSION: Changes in selenoproteins in renal tissues have good diagnostic value for acute rejection of kidneyl transplantation, and selenoproteins may be able to be a potential target for alleviating acute rejection of kidney transplantation.

Identification and immunological characterization of genes associated with ferroptosis in Alzheimer's disease and experimental demonstration.

 The incidence of Alzheimer's disease (AD) is rising globally, yet its treatment and prediction of this condition remain challenging due to the complex pathophysiological mechanisms associated with it. Consequently, the objective of the present study was to analyze and characterize the molecular mechanisms underlying ferroptosis­related genes (FEGs) in the pathogenesis of AD, as well as to construct a prognostic model. The findings will provide new insights for the future diagnosis and treatment of AD. First, the AD dataset GSE33000 from the Gene Expression Omnibus database and the FEGs from FerrDB were obtained. Next, unsupervised cluster analysis was used to obtain the FEGs that were most relevant to AD. Subsequently, enrichment analyses were performed on the FEGs to explore biological functions. Subsequently, the role of these genes in the immune microenvironment was elucidated through CIBERSORT. Then, the optimal machine learning was selected by comparing the performance of different machine learning models. To validate the prediction efficiency, the models were validated using nomograms, calibration curves, decision curve analysis and external datasets. Furthermore, the expression of FEGs between different groups was verified using reverse transcription quantitative PCR and western blot analysis. In AD, alterations in the expression of FEGs affect the aggregation and infiltration of certain immune cells. This indicated that the occurrence of AD is strongly associated with immune infiltration. Finally, the most appropriate machine learning models were selected, and AD diagnostic models and nomograms were built. The present study provided novel insights that enhance understanding with regard to the molecular mechanism of action of FEGs in AD. Moreover, the present study provided biomarkers that may facilitate the diagnosis of AD.

An analysis of the role of GAB2 in pan-cancer from a multidimensional perspective.

BACKGROUND: To explore the role of GAB2 in pan-cancer based on bioinformatics analysis. METHODS: Based on TCGA and GTEx databases, we used TIMER2.0 online analysis tool and R language to analyze the expression of GAB2 in pan-cancer. We used Kaplan-Meier Plotter to analyze the relationship between GAB2 and OS and RFS in pan-cancer. We utilized the CPTAC database to examine the expression of phosphorylated GAB2 in pan-cancer. We investigated the effects of mutation features on the occurrence and development of human cancers by cBioPortal and COSMIC. Using the database, we conducted an analysis of molecular compounds that have the potential to interact with GAB2 through molecular docking. Moreover, we use the TIMER to explore the relationship between GAB2 and immune cell infiltration, and draw relevant heatmaps by R language. RESULTS: GAB2 was abnormally expressed in various tumors and was associated with prognosis. There were differences in the expression of GAB2 phosphorylation in tumor tissues and corresponding normal tissues among different types of tumors. GAB2 interacts with Docetaxel and was associated with immune cell infiltration in various tumors. CONCLUSION: GAB2 participates in regulating immune infiltration and affects the prognosis of patients. GAB2 may serve as a potential tumor marker.

Stratification of Lung Adenocarcinoma Patients Based on In Silico and Immunohistochemistry Analyses of Oxidative Stress-Related Genes.

Background: Lung adenocarcinoma (LUAD) remains heterogeneous in the prognosis of patients; oxidative stress (OS) has been widely linked to cancer progression. Therefore, it is necessary to explore the prognostic value of the OS-associated genes in LUAD. Methods: An OS-associated prognostic signature was developed using the Cox regression and random forest model in The Cancer Genome Atlas-LUAD dataset. Kaplan-Meier (K-M) survival curve and time-dependent receiver operating characteristic (tROC) curves were applied to evaluate and validate the predictive accuracy of this signature among the training and testing cohorts. A nomogram was constructed and also verified by the concordance index (C-index), calibration curves, and tROC curves, respectively. ESTIMATE algorithm and CIBERSORT algorithms were conducted to explore the signature's immune characteristics. Core target genes of the prognostic signature were identified in the protein-protein interaction network. Results: A six OS-associated prognostic gene signature (CDC25C, ERO1A, GRIA1, TERT, CAV1, BDNF) was developed. The tROC and K-M survival curves in the training and testing cohorts revealed that the signature had good and robust predictive capability to predict the overall survival of LUAD patients. Meanwhile, the risk score was an independent prognostic factor influencing patients' overall survival. The results of the C-index (0.714), calibration curves, and the 1-, 2-, and 3-year tROC curves (area under the curve = 0.703, 0.737, and 0.723, respectively) suggested that the nomogram had good predictive efficacy and prognostic value for LUAD. Then, the authors found that the high-risk group may be depletion or loss of antitumor function of immune cells. Finally, 10 core genes of the signature were predicted. Conclusion: Their study may provide a novel understanding for the identification of prognostic stratification in LUAD patients, as well as the regulation of OS-associated genes in LUAD progression.

CDK1 Acts as a Prognostic Biomarker Associated with Immune Infiltration in Pan-Cancer, Especially in Gastrointestinal Tumors.

OBJECTIVE: Cyclin-dependent kinase 1 (CDK1) regulates the cell cycle and is highly expressed in most tumors. CDK1 expression has been associated with poor disease prognosis. This study aimed to identify the prognostic value of CDK1 in pan-cancer and investigate the association between CDK1 expression and immune cell infiltration. METHODS: CDK1 expression and its correlation with prognosis in pan-cancer were analyzed using online databases. Immune infiltration was assessed by ESTIMATE and CIBERSORT algorithms. We then evaluated the relationship between CDK1 expression and tumor mutational burden (TMB), microsatellite instability (MSI), or tumor-infiltrating immune cells. In addition, we performed the co-expression analysis of immune-related genes and GO analysis with CDK1 expression in pan-cancer. Finally, we compared the CDK1 expression profile with the immune-related genes in 30 pairs of clinical gastrointestinal tumor samples. RESULTS: Our analysis demonstrated overexpression of CDK1 in most tumor tissues, especially in gastrointestinal tumors. The high expression of CDK1 was associated with poor overall survival, disease-specific survival, disease-free interval, and progression-free interval in kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), pancreatic adenocarcinoma (PAAD), prostate adenocarcinoma (PRAD), and sarcoma (SARC). Besides, CDK1 expression was significantly associated with TMB in 22 cancer types and MSI in 8 cancer types as well as greater frequencies of MSI-high (MSI-H) status and high tumor mutational burden (TMB-H) in uterine corpus endometrial carcinoma (UCEC), stomach adenocarcinoma (STAD), sarcoma (SARC), rectum adenocarcinoma (READ), mesothelioma (MESO), head and neck squamous cell carcinoma (HNSC), and colon adenocarcinoma (COAD). In addition, CDK1 expression correlated with immune cell infiltrating levels, such as M0, M1, or M2 macrophages, memory CD4 T cells, T follicular helper cells, and naive B cells. Our data showed that CDK1 was remarkably correlated with 47 immune-related and immune checkpoint genes in many cancer types. Furthermore, CDK1 was up-regulated in gastrointestinal tumor samples, especially in gastric cancer and intestinal cancer. CDK1 was positively correlated with IDO1 in gastric cancer and PD-1 in intestinal cancer. CONCLUSION: Taken together, our data demonstrated the roles of CDK1 in oncogenesis and metastasis in pan-cancer. Thus, CDK1 is a potential prognostic biomarker and a target for tumor immunotherapy.

Necroptosis and immune infiltration in hypertrophic cardiomyopathy: novel insights from bioinformatics analyses.

Background: Hypertrophic Cardiomyopathy (HCM), a widespread genetic heart disorder, is largely associated with sudden cardiac fatality. Necroptosis, an emerging type of programmed cell death, plays a fundamental role in several cardiovascular diseases. Aim: This research utilized bioinformatics analysis to investigate necroptosis's implication in HCM. Methods: The study retrieved RNA sequencing datasets GSE130036 and GSE141910 from the Gene Expression Omnibus (GEO) database. It detected necroptosis-linked differentially expressed genes (NRDEGs) by reviewing both the gene set for necroptosis and the differently expressed genes (DEGs). The enriched signaling pathway of HCM was assessed using GSEA, while common DEGs were studied through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Concurrently, the Protein-Protein Interaction network (PPI) proved useful for identifying central genes. CIBERSORT facilitated evaluating the correlation between distinct immune cell-type prevalence and NRDEGs by analyzing immune infiltration patterns. Lastly, GSE141910 dataset validated the expression ranks of NRDEGs and immune-cell penetration. Results: The investigation disclosed significant enrichment and activation of the necroptosis pathway in HCM specimens. Seventeen diverse genes, including CYBB, BCL2, and JAK2 among others, were identified in the process. PPI network scrutiny classified nine of these genes as central genes. Results from GO and KEGG enrichment analyses showed substantial connections of these genes to pathways pertaining to the HIF-1 signaling track, necroptosis, and NOD-like receptor signaling process. Moreover, an imbalance in M2 macrophage cells in HCM samples was observed. Finally, CYBB, BCL2, and JAK2 emerged as vital genes and were validated using the GSE141910 dataset. Conclusion: These results indicate necroptosis as a probable underlying factor in HCM, with immune cell infiltration playing a part. Additionally, CYBB, BCL2, JAK2 could act as potential biomarkers for recognizing HCM. This information forms crucial insights into the basic mechanisms of HCM and could enhance its diagnosis and management.

Acting mechanism and clinical significance of hsa_circ_0005927 in the invasion and metastasis of gastric cancer.

Background: An increasing number of studies have demonstrated that differentially expressed circular RNAs (circRNAs) play critical roles in carcinogenesis. However, the biological function and clinical significance of hsa_circ_0005927 during gastric carcinogenesis remain unclear. The aim of this study was to investigate the acting mechanism and clinical significance of hsa_circ_0005927 in the invasion and metastasis of gastric cancer (GC). Methods: Hsa_circ_0005927 was detected in GC tissues, plasma and gastric juice from patients with GC, and its correlations with clinicopathological parameters were investigated. Receiver operating characteristic curves, Kaplan-Meier survival curves and a prognostic nomogram model were generated to analyze the diagnostic and prognostic value. Real-time cell analyzer, plate colony formation, and Transwell migration and invasion assays were utilized to assess GC cell proliferation, migration and invasion, respectively. Nucleoplasmic separation was applied to determine the distribution of hsa_circ_0005927 in cells. TargetScan and miRanda software were used for target microRNA (miRNA) prediction. Transcriptome sequencing and bioinformatics analysis were performed to annotate the functions of hsa_circ_0005927 in gastric carcinogenesis and metastasis from an RNomic perspective. Key target genes and immune cell infiltrations were analysed. Results: Hsa_circ_0005927 was found downregulated in high-grade intraepithelial neoplasia (HGIEN) tissues and GC tissues. Hsa_circ_0005927 levels in GC tissues were negatively correlated not only with lymphatic metastasis and distal metastasis but also with overall survival and disease-free survival. As a screening biomarker for GC, plasma hsa_circ_0005927 levels significantly increased in the early stages of GC, with a sensitivity and specificity of 52.38% and 76.19%, respectively. Hsa_circ_0005927 was mainly distributed in the cytoplasm, and structurally, it possesses multiple miRNA response elements (MREs) that interact with five miRNAs. A total of 421 downstream target genes of hsa_circ_0005927 were identified by transcriptome sequencing; and bioinformatics analysis suggested that these genes were involved mainly in the negative regulation of the T-cell apoptotic process, the interleukin-27-mediated signaling pathway, growth factor activity, guanylate cyclase activity, transcriptional misregulation in cancer, the cGMP-PKG signaling pathway, and the GnRH signaling pathway during gastric carcinogenesis and metastasis. GUCY1A2 and STK32A are key target genes significantly associated with immune infiltration. Conclusion: Our study revealed that hsa_circ_0005927 is a new player related to the invasion and metastasis of GC and is a potential indicator for early GC screening.

ECT2 promotes the occurrence and is a prognostic biomarker of head and neck squamous cell carcinoma.

Background: Epithelial Cell Transforming Sequence 2 (ECT2) has been implicated in various tumorigenic processes, including proliferation, migration, and invasion. However, its specific role in head and neck squamous cell carcinoma (HNSCC) remains unclear. Methods: This study integrates transcriptomic and single-cell RNA sequencing (scRNA-seq) data to explore the potential role of ECT2 in HNSCC. Differential expression analysis, cell-based assays (including CCK-8 for proliferation, transwell for migration, invasion assays, and flow cytometry for apoptosis and cell cycle analysis), and enrichment analysis were employed to investigate ECT2 expression levels and its regulatory effects on cellular phenotypes. Additionally, Mendelian randomization analysis was utilized to identify genes causally related to HNSCC using publicly available Genome-Wide Association Study (GWAS) data. Results: ECT2 is highly expressed in HNSCC samples and its downregulation inhibits proliferation, migration, invasion, induces apoptosis, and affects the cell cycle transition in HSC-3 cells. Furthermore, differential analysis revealed significant differences in the immune microenvironment and drug sensitivity between high and low ECT2 expression groups. The pathways enriched in different groups include CCR and its related chemokines, as well as HLA in antigen presentation and immune response. There are also significant differences in the sensitivity to drugs such as bortezomib and dasatinib between the two groups. Prognostic models constructed from prognosis-related genes showed significant differences in prognosis between high and low-risk groups. Integration of scRNA-seq data identified Monocyte clusters as high-scoring cell clusters based on genes interacting with ECT2.Mendelian randomization analysis identified three genes (LGALS2, SLC11A1, and TKT) causally related to HNSCC within this cell cluster. Conclusion: The findings suggest that ECT2 overexpression is associated with the survival rate of HNSCC, indicating its potential as a prognostic biomarker for this malignancy.

Multidimensional analysis of TMEM132A in pan-cancer: unveiling its potential as a biomarker for treatment response prediction.

Background: TMEM132A is a transmembrane protein that regulates gastric cancer cell malignancy and overall survival in bladder cancer patients. However, while some studies have investigated the involvement of TMEM132A in specific cancers, further systematic studies are required to elucidate its specific mechanisms of action in different cancer types. Methods: We investigated the pan-cancer role of TMEM132A using several databases. We analyzed TMEM132A expression and its correlation with clinical survival, immune checkpoints, tumor stemness score, prognostic value, immunomodulators, genomic profiles, immunological characteristics, immunotherapy and functional enrichment. Results: First, it was observed that TMEM132A expression levels were higher in the majority of tumors compared to non-tumor tissues. In addition, high TMEM132A expression may have a higher prognostic value in some cancers. Furthermore, TMEM132A was significantly associated with immune checkpoints, immunomodulators, prognosis, immunomodulatory genes, tumor stemness score, cell function status and immune infiltration in most tumors. Further analysis of TMEM132A-related gene enrichment, mutation sites and types, RNA modification and genomic heterogeneity showed that the major mutations of TMEM132A were missense mutations and that TMEM132A plays a very important role in UCEC, LUAD and LIHC. Finally, these results suggest that high TMEM132A expression may be associated with a better response to specific immunotherapies. Conclusion: This comprehensive study uncovers an important function for TMEM132A in different types of cancer. It also has the potential to identify TMEM132A as a potential biomarker for predicting treatment response. This may help us to better understand how TMEM132A plays a role in cancer and provide valuable insights for developing personalised treatments.