Immune Infiltration Levels Research Articles

Exploring the Role of DNA Methylation Located in Cuproptosis-Related Genes: Implications for Prognosis and Immune Landscape in Hepatocellular Carcinoma.

Copper dysregulation has been linked to liver disease, cardiac dysfunction, neuropathy, and anemia. Previous investigations have been undertaken to demonstrate the impact of cuproptosis-related genes (CRGs) on the poor prognosis of hepatocellular carcinoma (HCC), while the prognostic significance and beneath molecular basis of DNA-methylation sites located in CRGs remain unknown. This study aims to identify CRG-located DNA-methylation sites linked to patient prognosis and establish a novel prognostic biomarkers combination for CRG-located DNA-methylation signature. The prognostic biomarkers combination was established through multivariate-Cox-regression after CRG-located DNA-methylation sites tied to the outcome of patients emerged by univariate-Cox-regression. The correlation between signature and immune cell infiltration levels, immune-checkpoint-associated genes was analyzed using spearman correlation and the difference was contrasted between different groups utilizing the Mann-Whitney-U test. Real-time quantitative methylation-specific polymerase chain reaction (RT-qMSP) was used to identify gene methylation. A novel prognostic biomarkers combination for CRG-located DNA-methylation signature was established. Subsequently, the independence of this methylation signature from clinical features and its correlation with immune infiltrative and immune checkpoints in HCC were also investigated. DNA methylation alterations can influence the onset, development, and treatment of various tumors by regulating the transcription of corresponding genes. Our analysis found that cg05706061 contained in prognosis signature was located in the promoter region of the cuproptosis-related gene SLC31A2. The DNA-methylation level of cg05706061 demonstrated significantly different between tumor and normal tissue, and significantly correlated with the expression of SLC31A2. We further investigated the promoter methylation status of SLC31A2 by qMSP, the result showed that the DNA-methylation level of SLC31A2 in HCC cell lines were significantly decreased compared with normal liver cells. Our findings reveal possible mechanisms of CRG-located DNA-methylation on the advancement of HCC and offers new perspectives for prognostic assessment and treatment options.

Abstract 6243: Rapid autopsy reveals the interplay of tumor heterogeneity at genomic, transcriptomic, and spatial dimensions

Abstract Incomplete understanding of tumor heterogeneity across primary and metastatic sites often lead to ineffective cancer treatments. To comprehensively interrogate tumor heterogeneity at genomic, transcriptomic, and spatial levels, we established a novel rapid autopsy protocol to obtain multiregional tri-omic data consisted of whole exome sequencing, mRNA-sequencing, and multiplexed immunohistochemistry on primary and metastatic tumors and associated microenvironments (TMEs). We applied this protocol to six patients with various primary tumor types: non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), urothelial cancer, and one mixed case with both NSCLC and SCLC. A total of 50 tumor samples with paired tri-omic data were obtained across 16 different primary and metastatic organs to interrogate heterogeneities underlying tumor evolution and treatment resistance mechanisms. We constructed mutation-based phylogeny trees to reveal structural differences in tumor evolution trajectories across patients and infer heterogeneous tumor antigen landscape. We uncovered highly entropic phylogenetic structures having unique variants gained at each metastasis as well as low entropic structures with high sharing across sites. The mixed case had a clonal sweep structure with two metastases developed prior to the rest of the metastasis. In this case, older clones were shown to have strong association with APOBEC mutation signatures, enhanced subclonal mutation burden and loss of human leukocyte antigen heterozygosity as mechanisms of immune evasion. Integrating transcriptome and spatial profiles, we revealed that tumor intrinsic expressions and extrinsic immune infiltration levels covary with tissue types rather than with mutation-based phylogenetic clusters. Across patients, samples that had high similarities in mutational landscape varied in their pro-tumor expression programs and the cellular composition of TMEs. In particular, lung samples across patients had the highest effector T cell population and major histocompatibility complex (MHC) class I, II presentations compared to other tissue types. Contrasting small cell carcinoma (SCC) and adenocarcinoma (Adeno) samples from lung tissues further revealed that SCC had more immunosuppressive TMEs with downregulation of MHC class II. Liver metastasis samples were further characterized to have the most aggressive phenotypes enriched in genomic instability measured by aneuploidy compared to other metastatic sites. In sum, we present a novel technology and computational framework that enables a three-dimensional profiling of tumor heterogeneity. This work suggests that single-site biopsies are insufficient to capture tumor heterogeneity and calls for multi-modal integrations to design treatments that can overcome tumor-specific resistance mechanisms. Citation Format: Ning Yao, Rami Vanguri, Juber Patel, Jayon Lihm, Andrew Fisher, Irene Jarchum, Shigeaki Umeda, Hao Li, Scott Ely, Travis Hollmann, Christine Iacobuzio-Donahue, Benjamin Greenbaum. Rapid autopsy reveals the interplay of tumor heterogeneity at genomic, transcriptomic, and spatial dimensions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6243.

Identification and validation of DHCR7 as a diagnostic biomarker involved in the proliferation and mitochondrial function of breast cancer.

Energy metabolism has a complex intersection with pathogenesis and development of breast cancer (BC). This allows for the possibility of identifying energy-metabolism-related genes (EMRGs) as novel prognostic biomarkers for BC. 7-dehydrocholesterol reductase (DHCR7) is a key enzyme of cholesterol biosynthesis involved in many cancers, and in this paper, we investigate the effects of DHCR7 on the proliferation and mitochondrial function of BC. EMRGs were identified from the Gene Expression Omnibus (GEO) and MSigDB databases using bioinformatics methods. Key EMRGs of BC were then identified and validated by functional enrichment analysis, interaction analysis, weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, Cox analysis, and immune infiltration. Western blot, qRT-PCR, immunohistochemistry (IHC), MTT assay, colony formation assay and flow cytometry assay were then used to analyze DHCR7 expression and its biological effects on BC cells. We identified 31 EMRGs in BC. These 31 EMRGs and related transcription factors (TFs), miRNAs, and drugs were enriched in glycerophospholipid metabolism, glycoprotein metabolic process, breast cancer, and cell cycle. Crucially, DHCR7 was a key EMRG in BC identified and validated by WGCNA, LASSO regression and receiver operating characteristic (ROC) curve analysis. High DHCR7 expression was significantly associated with tumor immune infiltration level, pathological M, and poor prognosis in BC. In addition, DHCR7 knockdown inhibited cell proliferation, induced apoptosis and affected mitochondrial function in BC cells. DHCR7 was found to be a key EMRG up-regulated in BC cells. This study is the first to our knowledge to report that DHCR7 acts as an oncogene in BC, which might become a novel therapeutic target for BC patients.

A clinical prognostic model of oxidative stress-related genes linked to tumor immune cell infiltration and the prognosis of ovarian cancer patients

BackgroundAccording to statistics, ovarian cancer (OV) is the most prevalent type of gynecologic malignancy and has the highest mortality rate of all gynecologic tumors. Although several studies have shown that oxidative stress (OS) contributes significantly to the onset and progression of cancer, the role of OS in OV needs to be investigated further. Thus, it is critical to comprehend the function of OS-related genes in OV. MethodsIn this study, all data related to the transcriptome and clinical status of the patients were retrieved from “The Cancer Genome Atlas” (TCGA) and “Gene Expression Omnibus” (GEO) databases. Using the unsupervised cluster analysis technique, all patients with OV were classified into two different subtypes (categories) based on the OS gene. All hub genes were screened using the weighted gene co-expression network analysis (WGCNA). Since the hub genes and the differentially expressed genes (DEGs) in both categories were found to intersect, the univariate Cox regression analysis was implemented. A multivariate Cox analysis was also performed to construct a novel clinical prognosis model, which was validated using data from the GEO cohort. In addition, the relationship between risk score and immune cell infiltration level was evaluated using CIBERSORT. Finally, qRT-PCR was used to confirm the expression of the genes used to construct the model. ResultsTwo subtypes of OS were obtained. The findings indicated that OS-C1 had a better survival outcome than OS-C2. The results of WGCNA yielded 112 hub genes. For univariate COX regression analyses, 49 OS-related trait genes were obtained. Finally, a clinical prognostic model containing two genes was constructed. This model could differentiate between patients with OV having varying years of survival in the TCGA and GEO cohorts. The model risk score was verified as an independent prognostic indicator. According to the results of CIBERSORT, many tumor-infiltrating immune cells were found to be significantly related to the risk score. Furthermore, the results revealed that patients with low-risk OV in the CTLA4 treatment group had a high likelihood of benefiting from immunotherapy. qRT-PCR results also showed that the expression of MARVELD1 and VSIG4 was high in the OV samples. ConclusionsAnalysis of the results suggested that the newly developed model, which contained two characteristic OS-related genes, could successfully predict the survival outcomes of all patients with OV. The findings of this study could offer valuable information and insights into the refinement of personalized therapy and immunotherapy for OV in the future.

CELSR3 is a prognostic marker in HNSCC and correlates with immune cell infiltration in the tumor microenvironment

PurposeTo look at the diagnostic value of the CELSR receptor 3 (CELSR3) gene in head and neck squamous cell carcinoma (HNSCC) and its effect on tumor immune invasion, which is important for enhancing HNSCC treatment.MethodsSeveral bioinformatics tools were employed to investigate CELSR3’s putative oncogenic pathway in HNSCC, and datasets from The Tumor Genome Atlas (TCGA), Tumor Immune Estimation Resource (TIMER), Gene Expression Profile Interaction Analysis (GEPIA) and LinkedOmics were extracted and evaluated. CELSR3 has been linked to tumor immune cell infiltration, immunological checkpoints, and immune-related genes. CELSR3’s putative roles were investigated using Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and pathway enrichment analysis. The expression level of CELSR3 in HNSCC tissues and cells was detected by RT-qPCR. The effects of CELSR3 on proliferation of HNSCC cells were detected by CCK-8 assay.ResultsCELSR3 was shown to be expressed differently in different types of cancer and normal tissues. CELSR3 gene expression was linked to pN-stage and pM-stage. Patients with high CELSR3 expression also have a well prognosis. CELSR3 expression was found to be an independent predictive factor for HNSCC in both univariate and multivariate Cox regression analyses. We discovered the functional network of CELSR3 in HNSCC using GO and KEGG analysis. CELSR3 expression levels were found to be favorably associated with immune cell infiltration levels. Furthermore, CELSR3 expression levels were significantly correlated with the expression levels of many immune molecules, such as MHC genes, immune activation genes, chemokine receptors, and chemokines. CELSR3 is highly expressed in HNSCC tissues and cells. CELSR3 overexpression significantly inhibited the proliferation of HNSCC cells. CELSR3 expression may affect the immune microenvironment and, as a result, the prognosis of HNSCC.ConclusionCELSR3 expression is elevated in HNSCC tumor tissues, and high CELSR3 expression is associated with well prognosis, which inhibited the proliferation of NHSCC cells. CELSR3 has the potential to influence tumor formation by controlling tumor-infiltrating cells in the tumor microenvironment (TME). As a result, CELSR3 may have diagnostic significance in HNSCC.

Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma.

Previous studies have largely neglected the role of ADCC in LUAD, and no study has systematically compiled ADCC-associated genes to create prognostic signatures. In this study, 1564 LUAD patients, 2057 NSCLC patients, and more than 5000 patients with various cancer types from diverse cohorts were included. R package ConsensusClusterPlus was utilized to classify patients into different subtypes. A number of machine-learning algorithms were used to construct the ADCCRS. GSVA and ClusterProfiler were used for enrichment analyses, and IOBR was used to quantify immune cell infiltration level. GISTIC2.0 and maftools were used to analyze the CNV and SNV data. The Oncopredict package was used to predict drug information based on the GDSC1. Three immunotherapy cohorts were used to evaluate patient response to immunotherapy. The Seurat package was used to process single-cell data, the AUCell package was used to calculate cells' geneset activity scores, and the Scissor algorithm was used to identify ADCCRS-associated cells. Through unsupervised clustering, two distinct subtypes of LUAD were identified, each exhibiting distinct clinical characteristics. The ADCCRS, consisted of 16 genes, was constructed by integrated machine-learning methods. The prognostic power of ADCCRS was validated in 28 independent datasets. Further, ADCCRS shows better predictive abilities than 102 previously published signatures in predicting LUAD patients' survival. A nomogram incorporating ADCCRS and clinical features was constructed, demonstrating high predictive performance. ADCCRS positively correlates with patients' gene mutation, and integrated analysis of bulk and single-cell transcriptome data revealed the association of ADCCRS with TME modulators. Cells representing high-ADCCRS phenotype exhibited more malignant features. LUAD patients with high ADCCRS levels exhibited sensitivity to chemotherapy and targeted therapy, while displaying resistance to immunotherapy. In pan-cancer analysis, ADCCRS still exhibited significant prognostic value and was found to be a risk factor for most cancer patients. ADCCRS offers a critical prognostic insight for patients with LUAD, shedding light on the tumor microenvironment and forecasting treatment responsiveness.

Paeoniflorin ameliorates oxaliplatin-induced peripheral neuropathy via inhibiting neuroinflammation through influence on gut microbiota

Oxaliplatin (OXA)-induced peripheral neuropathy (OIPN) is a severe side effect that greatly limits OXA clinical use and threatens patients' life and health. Paeoniflorin exhibits extensive anti-inflammatory and neuroprotective effects, but whether it can protect against OIPN and the underlying mechanisms remain unclear. This study aimed to investigate the effects of paeoniflorin on OIPN and probe into the underlying mechanisms. The OIPN model was established through oxaliplatin injection in rats. The ameliorative effects of paeoniflorin on OIPN was assessed by nociceptive hypersensitivities through pain behavioral methods. Neuroinflammation were examined by measuring the levels of inflammatory cytokines and immune cells infiltration. The signaling pathway of TLR4/MyD88/NF-κB was evaluated by Western blotting. Gut microbial changes were detected by 16S rDNA sequencing technology. In addition, antibiotics-induced microbiota eradication and fecal microbial transplantation (FMT) were applied for exploring the function of gut microbiota in the protective effects of paeoniflorin. The results revealed that paeoniflorin significantly alleviated mechanical and cold hypersensitivity, mitigated neuroinflammation and influenced gut microbial composition in OIPN rats. Fecal microbiota transplantation further verified that gut microbiota was required for paeoniflorin ameliorating OIPN and that the underlying mechanism involved downregulation of TLR4/MyD88/NF-κB signaling. Specifically, Akkermansia, Dubosiella and Corynebacterium might serve as crucial genera regulated by paeoniflorin in the treatment of OIPN. In summary, our investigations delineate paeoniflorin's ameliorative effects on OIPN by alleviating neuroinflammation through regulations of gut microbiota. This suggests that paeoniflorin may serve as a new potential strategy for treatment of OIPN in clinical practice.

Significance of Aneuploidy in Predicting Prognosis and Treatment Response of Uveal Melanoma.

This study aimed to improve personalized treatment strategies and predict survival outcomes for patients with uveal melanoma (UM). Copy number aberrations (CNAs) have been considered as a main feature of metastatic UM. This study was designed to explore the feasibility of using copy number variation (CNV) in UM classification, prognosis stratification and treatment response. The CNV data in the TCGA-UVM cohort were used to classify the samples. The differentially expressed genes (DEGs) between subtypes were screened by the "Limma" package. The module and hub genes related to aneuploidy score were identified by performing weighted gene co-expression network analysis (WGCNA) on the DEGs. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to train the hub genes for developing a prognosis model for UM. Finally, the expression levels of the screened prognostic key genes were verified in UM cells, and the cell migration and invasion abilities were detected using real-time quantitative PCR (qRT-PCR) and transwell assay. The UM samples were divided into 3 CNV subtypes, which differed significantly in overall survival (OS) and disease-specific survival (DSS). C1 had the shortest OS and DSS and the highest level of immune infiltration. A total of 2036 DEGs were obtained from the three subtypes. Eighty hub genes with the closest correlation with aneuploidy scores were selected by WGCNA. Univariate Cox and LASSO regression-based analyses finally determined eight genes as the key prognostic genes, including HES6, RNASEH2C, NQO1, NUDT14, TTYH3, GJC1, FKBP10, and MRPL24. A prognostic model was developed using the eight genes, demonstrating a strong prediction power. Differences in the response to immunotherapy among patients in different risk groups were significant. We found that high-risk patients were more sensitive to two drugs (Palbociclib_ 1054 and Ribociclib_1632), while low-risk patients were more sensitive to AZD1208_1449, ERK_2440_1713, Mirin_1048, and Selumetinib_1736. UM in this study was divided into three CNV subtypes, and a model based on eight aneuploidy score-related genes was established to evaluate the prognosis and drug treatment efficacy of UM patients. The current results may have the potential to help the clinical decision-making process for UM management.

Comprehensive investigation into the influence of glycosylation on head and neck squamous cell carcinoma and development of a prognostic model for risk assessment and anticipating immunotherapy.

It has been well established that glycosylation plays a pivotal role in initiation, progression, and therapy resistance of several cancers. However, the correlations between glycosylation and head and neck squamous cell carcinoma (HNSCC) have not been elucidated in detail. The paramount genes governing glycosylation were discerned via the utilization of the Protein-Protein Interaction (PPI) network and correlation analysis, coupled with single-cell RNA sequencing (scRNA-seq) analysis. To construct risk models exhibiting heightened predictive efficacy, cox- and lasso-regression methodologies were employed, and the veracity of these models was substantiated across both internal and external datasets. Subsequently, an exploration into the distinctions within the tumor microenvironment (TME), immunotherapy responses, and enriched pathways among disparate risk cohorts ensued. Ultimately, cell experiments were conducted to validate the consequential impact of SMS in Head and Neck Squamous Cell Carcinoma (HNSCC). A total of 184 genes orchestrating glycosylation were delineated for subsequent scrutiny. Employing cox- and lasso-regression methodologies, we fashioned a 3-gene signature, proficient in prognosticating the outcomes for patients afflicted with HNSCC. Noteworthy observations encompassed distinctions in the Tumor Microenvironment (TME), levels of immune cell infiltration, and the presence of immune checkpoint markers among divergent risk cohorts, holding potentially consequential implications for the clinical management of HNSCC patients. The prognosis of HNSCC can be proficiently anticipated through risk signatures based on Glycosylation-related genes (GRGs). A thorough delineation of the GRGs signature in HNSCC holds the potential to facilitate the interpretation of HNSCC's responsiveness to immunotherapy and provide innovative strategies for cancer treatment.

Differential genetic mutations and immune cell infiltration in high- and low-risk STAD: Implications for prognosis and immunotherapy efficacy.

This study investigates genetic mutations and immune cell dynamics in stomach adenocarcinoma (STAD), focusing on identifying prognostic markers and therapeutic targets. Analysis of TCGA-STAD samples revealed C > A as the most common single nucleotide variant (SNV) in both high and low-risk groups. Key mutated driver genes included TTN, TP53 and MUC16, with frame-shift mutations more prevalent in the low-risk group and missense mutations in the high-risk group. Interaction analysis of hub genes such as C1QA and CD68 showed significant correlations, impacting immune cell infiltration patterns. Using ssGSEA, we found higher immune cell infiltration (B cells, CD4+ T cells, CD8+ T cells, DC cells, NK cells) in the high-risk group, correlated with increased risk scores. xCell algorithm results indicated distinct immune infiltration levels between the groups. The study's risk scoring model proved effective in prognosis prediction and immunotherapy efficacy assessment. Key molecules like CD28, CD27 and SLAMF7 correlated significantly with risk scores, suggesting potential targets for high-risk STAD patients. Drug sensitivity analysis showed a negative correlation between risk scores and sensitivity to certain treatments, indicating potential therapeutic options for high-risk STAD patients. We also validated the carcinogenic role of RPL14 in gastric cancer through phenotypic experiments, demonstrating its influence on cancer cell proliferation, invasion and migration. Overall, this research provides crucial insights into the genetic and immune aspects of STAD, highlighting the importance of a risk scoring model for personalized treatment strategies and clinical decision-making in gastric cancer management.

Development of an immune-related diagnostic predictive model for oral lichen planus.

Oral lichen planus (OLP) was a chronic inflammatory disease of unknown etiology with a 1.4% chance of progressing to malignancy. However, it has been suggested in several studies that immune system disorders played a dominant role in the onset and progression of OLP. Therefore, this experiment aimed to develop a diagnostic prediction model for OLP based on immunopathogenesis to achieve early diagnosis and treatment and prevent cancer. In this study, 2 publicly available OLP datasets from the gene expression omnibus database were filtered. In the experimental group (GSE52130), the level of immune cell infiltration was assessed using MCPcounter and ssGSEA algorithms. Subsequently, differential expression analysis and gene set enrichment analysis were performed between the OLP and control groups. The resulting differentially expressed genes were intersected with immunologically relevant genes provided on the immunology database and analysis portal database (ImmPort) website to obtain differentially expressed immunologically relevant genes (DEIRGs). Furthermore, the gene ontology and kyoto encyclopedia of genes and genomes analyses were carried out. Finally, protein-protein interaction network and least absolute shrinkage and selection operator regression analyses constructed a model for OLP. Receiver operating characteristic curves for the experimental and validation datasets (GSE38616) were plotted separately to validate the model's credibility. In addition, real-time quantitative PCR experiment was performed to verify the expression level of the diagnostic genes. Immune cell infiltration analysis revealed a more significant degree of inflammatory infiltration in the OLP group compared to the control group. In addition, the gene set enrichment analysis results were mainly associated with keratinization, antibacterial and immune responses, etc. A total of 774 differentially expressed genes was obtained according to the screening criteria, of which 65 were differentially expressed immunologically relevant genes. Ultimately, an immune-related diagnostic prediction model for OLP, which was composed of 5 hub genes (BST2, RNASEL, PI3, DEFB4A, CX3CL1), was identified. The verification results showed that the model has good diagnostic ability. There was a significant correlation between the 5 hub diagnostic biomarkers and immune infiltrating cells. The development of this model gave a novel insight into the early diagnosis of OLP.

Establishment of Golgi apparatus-related genes signature to predict the prognosis and immunotherapy response in gastric cancer patients.

The Golgi apparatus plays a crucial role in intracellular protein transportation, processing, and sorting. Dysfunctions of the Golgi apparatus have been implicated in tumorigenesis and drug resistance. This study aimed to investigate the prognostic and treatment response assessment value of Golgi apparatus-related gene (GARGs) features in gastric cancer patients. Transcriptome data and clinical information of gastric cancer patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Cox regression analysis was employed to assess the prognostic significance of GARGs and construct risk features. The immune landscape, drug sensitivity, immune therapy response, gene expression patterns, and somatic mutation characteristics were analyzed between different risk groups. A nomogram model for predicting gastric cancer prognosis was developed and evaluated. Among 1643 GARGs examined, 365 showed significant associations with gastric cancer prognosis. Five independent prognostic GARGs (NGF, ABCG1, CHAC1, GBA2, PCSK7) were selected to construct risk features for gastric cancer patients. These risk features effectively stratified patients into high-risk and low-risk groups, with the former exhibiting worse prognosis than the latter. Patients in the high-risk group displayed higher levels of immune cell infiltration, while the expression levels of NGF, CHAC1, GBA2, PCSK7 were significantly correlated with immune cell infiltration. Notably, the low-risk group exhibited higher sensitivity to epothilone.B, metformin, and tipifarnib compared to the high-risk group. Moreover, patients in the low-risk group demonstrated greater responsiveness to immune therapy than those in the high-risk group. In terms of biological processes and KEGG pathways related to immunity regulation, significant suppression was observed in the high-risk group compared to the low-risk group; meanwhile cell cycle pathways exhibited significant activation in the high-risk group. Furthermore, the low-risk group exhibited a higher tumor mutation burden compared to the high-risk group. The risk features derived from GARGs, in conjunction with age, were identified as independent risk factors for gastric cancer. The nomogram incorporating these factors demonstrated improved performance in predicting gastric cancer prognosis. Our study established risk features derived from GARGs that hold potential clinical utility in prognostic assessment and immune therapy response evaluation of gastric cancer patients.

Nuclear protein NOP2 serves as a poor-prognosis predictor of LUAD and aggravates the malignancy of lung adenocarcinoma cells.

Recent studies have shown that NOP2, a nucleolar protein, is up-regulated in various cancers, suggesting a potential link to tumor aggressiveness and unfavorable outcomes. This study examines NOP2's role in lung adenocarcinoma (LUAD), a context where its implications remain unclear. Utilizing bioinformatics, we assessed 513 LUAD and 59 normal tissue samples from The Cancer Genome Atlas (TCGA) to explore NOP2's diagnostic and prognostic significance in LUAD. Additionally, in vitro experiments compared NOP2 expression between Beas-2b and A549 cells. Advanced databases and analytical tools, including LINKEDOMICS, STRING, and TISIDB, were employed to further elucidate NOP2's association with LUAD. Our findings indicate a significantly higher expression of NOP2 mRNA and protein in A549 cells compared to Beas-2b cells (P < 0.001). In LUAD, elevated NOP2 levels were linked to decreased Overall Survival (OS) and advanced clinical stages. Univariate Cox analysis revealed that high NOP2 expression correlated with poorer OS in LUAD (P < 0.01), a finding independently supported by multivariate Cox analysis (P < 0.05). The relationship between NOP2 expression and LUAD risk was presented via a Nomogram. Additionally, Gene Set Enrichment Analysis (GSEA) identified seven NOP2-related signaling pathways. A focal point of our research was the interplay between NOP2 and tumor-immune interactions. Notably, a negative correlation was observed between NOP2 expression and the immune infiltration levels of macrophages, neutrophils, mast cells, Natural Killer (NK) cells, and CD8 + T cells in LUAD. Moreover, the expression of NOP2 was related to the sensitivity of various chemotherapeutic drugs. In vitro, we found that downregulating NOP2 can decrease the proliferation, migration and invasion of A549 cells. Furthermore, NOP2 can regulate Caspase3-mediated apoptosis. Collectively, particularly regarding prognosis, immune infiltration and vitro experiments, these findings suggest NOP2's potential of serving as a poor-prognostic biomarker for LUAD and aggravating the malignancy of lung adenocarcinoma cells.

Exploring the role of mitochondrial-associated and peripheral neuropathy genes in the pathogenesis of diabetic peripheral neuropathy

BackgroundDiabetic peripheral neuropathy (DPN) is a prevalent and serious complication of diabetes mellitus, impacting the nerves in the limbs and leading to symptoms like pain, numbness, and diminished function. While the exact molecular and immune mechanisms underlying DPN remain incompletely understood, recent findings indicate that mitochondrial dysfunction may play a role in the advancement of this diabetic condition.MethodsTwo RNA transcriptome datasets (codes: GSE185011 and GSE95849), comprising samples from diabetic peripheral neuropathy (DPN) patients and healthy controls (HC), were retrieved from the Gene Expression Omnibus (GEO) database hosted by the National Center for Biotechnology Information (NCBI). Subsequently, differential expression analysis and gene set enrichment analysis were performed. Protein–protein interaction (PPI) networks were constructed to pinpoint key hub genes associated with DPN, with a specific emphasis on genes related to mitochondria and peripheral neuropathy disease (PND) that displayed differential expression. Additionally, the study estimated the levels of immune cell infiltration in both the HC and DPN samples. To validate the findings, quantitative polymerase chain reaction (qPCR) was employed to confirm the differential expression of selected genes in the DPN samples.ResultsThis research identifies four hub genes associated mitochondria or PN. Furthermore, the analysis revealed increased immune cell infiltration in DPN tissues, particularly notable for macrophages and T cells. Additionally, our investigation identified potential drug candidates capable of regulating the expression of the four hub genes. These findings were corroborated by qPCR results, reinforcing the credibility of our bioinformatics analysis.ConclusionsThis study provides a comprehensive overview of the molecular and immunological characteristics of DPN, based on both bioinformatics and experimental methods.

Novel biomarkers of inflammation-associated immunity in cervical cancer.

Cervical cancer (CC) is a highly malignant gynecological cancer with a direct causal link to inflammation, primarily resulting from persistent high-risk human papillomavirus (HPV) infection. Given the challenges in early detection and mid to late-stage treatment, our research aims to identify inflammation-associated immune biomarkers in CC. Using a bioinformatics approach combined with experimental validation, we integrated two CC datasets (GSE39001 and GSE63514) in the Gene Expression Omnibus (GEO) to eliminate batch effects. Immune-related inflammation differentially expressed genes (DGEs) were obtained by R language identification. This analysis identified 37 inflammation-related DEGs. Subsequently, we discussed the different levels of immune infiltration between CC cases and controls. Weighted gene co-expression network analysis (WGCNA) identified seven immune infiltration-related modules in CC. We identified 15 immune DEGs associated with inflammation at the intersection of these findings. In addition, we constructed a protein interaction network using the String database and screened five hub genes using "CytoHubba": CXC chemokine ligand 8 (CXCL8), CXC chemokine ligand 10 (CXCL10), CX3C chemokine receptor 1 (CX3CR1), Fc gamma receptors 3B (FCGR3B), and SELL. The expression of these five genes in CC was determined by PCR experiments. In addition, we assessed their diagnostic value and further analyzed the association of immune cells with them. Five inflammation- and immune-related genes were identified, aiming to provide new directions for early diagnosis and mid to late-stage treatment of CC from multiple perspectives.

Development of a prognostic model for lung adenocarcinoma polarity-related genes and analysis of immune landscape.

Despite the progress made in the management of lung adenocarcinoma (LUAD), the overall prognosis for LUAD individuals remains suboptimal. While the role of cell polarity in tumor invasion and metastasis is well established, its prognostic significance in LUAD is still unknown. Differential analysis was performed on the Cancer Genome Atlas (TCGA)-LUAD and normal lung tissue, and candidate genes were identified by intersecting differentially expressed genes with polarity-related genes (PRGs). A prognostic model was constructed using univariate and multivariate Cox regression and LASSO regression. To enhance the robustness of the analysis, an independent prognostic analysis was conducted by incorporating relevant clinical information. The accuracy and sensitivity of the model were validated using survival analysis and ROC curves. Finally, immune landscape, immune therapy, tumor mutation burden, and drug sensitivity analysis were carried out on high- and low-risk patients. Ten prognostic genes were screened to divide LUAD patients into different risk groups. Survival analysis, ROC curves, and univariate/multivariate Cox regression analyses collectively demonstrated the favorable predictive performance of the model, which could be an independent prognostic factor. The nomogram, in conjunction with the calibration curve, demonstrated the model's compelling predictive capacity in prognosticating the overall survival of LUAD individuals. Low-risk LUAD patients exhibited heightened levels of immune cell infiltration, immune scores, and immune checkpoint expression compared to high-risk individuals. So, they may have a greater likelihood of benefiting from immune therapy. The high-risk group demonstrated a remarkably higher tumor mutation burden (TMB) in contrast with the low-risk group. XAV-939, Fulvestrant, and SR16157 may have potential value in the clinical use of LUAD. We revealed the potential linkage between PRGs and LUAD prognosis, and the application of these prognostic factors in risk stratification and prognosis prediction of LUAD patients may be of great significance.

Comprehensive exosomal microRNA profile and construction of competing endogenous RNA network in autism spectrum disorder: A pilot study.

Exosomes have been demonstrated to exert momentous roles in autism spectrum disorder (ASD). However, few studies have reported a correlation between exosomal microRNAs (miRNAs) and ASD. To date, our understanding of crucial competing endogenous RNA (ceRNA) networks in ASD remains limited. Herein, the exosomal miRNA profile in the peripheral blood of children with ASD and healthy controls was investigated and the level of immune cell infiltration in ASD was evaluated to determine the distribution of immune cell subtypes. Exosomes were isolated from the peripheral blood of ten children with ASD and ten healthy controls, and further identified using transmission electron microscopy and western blot analysis. RNA sequencing was conducted to investigate exosomal miRNA profiles in patients with ASD. The mRNA and circular RNA (circRNA) expression profiles were acquired from the Gene Expression Omnibus (GEO) database. Differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and circRNAs (DEcircRNAs) were identified and ceRNA regulatory networks were constructed. Furthermore, the immune cell infiltration levels in patients with ASD were evaluated. Exosomes were spherical, approximately 100 nm in size, and were confirmed via western blot analysis using exosome-associated markers CD9, CD63, and CD81. Thirty-five DEmRNAs, 63 DEmiRNAs, and 494 DEcircRNAs were identified in patients with ASD. CeRNA regulatory networks, including 6 DEmRNAs, 14 DEmiRNAs, and 86 DEcircRNAs, were established. Correlation analysis indicated that leucine-rich glioma inactivated protein 1 (LGI1) expression was significantly positively correlated with the content of CD8+ T cells. Our findings may be conducive to offering novel insights into this disease and providing further evidence of transcriptomic abnormalities in ASD.

A lactate metabolism-related signature predicting patient prognosis and immune microenvironment in ovarian cancer.

Ovarian cancer (OV) is a highly lethal gynecological malignancy with a poor prognosis. Lactate metabolism is crucial for tumor cell survival, proliferation, and immune evasion. Our study aims to investigate the role of lactate metabolism-related genes (LMRGs) in OV and their potential as biomarkers for prognosis, immune microenvironment, and immunotherapy response. Ovarian samples were collected from the TCGA cohort. And 12 lactate-related pathways were identified from the MsigDB database. Differentially expressed genes within these pathways were designated as LMRGs, which undergo unsupervised clustering to identify distinct clusters based on LMRGs. Subsequently, we assessed survival outcomes, immune cell infiltration levels, Hallmaker pathway activation patterns, and chemotaxis among different subtypes. After conducting additional unsupervised clustering based on differentially expressed genes (DEGs), significant differences in the expression of LMRGs between the two clusters were observed. The differentially expressed genes were subjected to subsequent functional enrichment analysis. Furthermore, we construct a model incorporating LMRGs. Subsequently, the lactate score for each tumor sample was calculated based on this model, facilitating the classification of samples into high and low groups according to their respective lactate scores. Distinct groups examined disparities in survival prognosis, copy number variation (CNV), single nucleotide variation (SNV), and immune infiltration. The lactate score served as a quantitative measure of OV's lactate metabolism pattern and an independent prognostic factor. This study investigated the potential role of LMRGs in tumor microenvironment diversity and prognosis in OV, suggesting that LMRGs play a crucial role in OV progression and the tumor microenvironment, thus serving as novel indicators for prognosis, immune microenvironment status, and response to immunotherapy.

Systematic analysis of PANoptosis-related genes identifies XIAP as a functional oncogene in breast cancer

BackgroundBreast cancer (BC) is the most prevalent malignant disease affecting women globally. PANoptosis, a novel form of cell death combining features of pyroptosis, apoptosis, and necroptosis, has recently gained attention. However, its precise function in BC and the predictive values of PANoptosis-related genes remain unclear. MethodsWe used the expression data and clinical information of BC tissues or normal breast tissues from public databases, and then successfully developed and verified a BC PANoptosis-related risk model through a combination of univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and Kaplan-Meier (KM) analysis. A nomogram was constructed to estimate survival probability, and its accuracy was assessed using calibration curves. ResultsAmong 37 PANoptosis-related genes, we identified 4 differentially expressed genes related to overall survival (OS). Next, a risk model incorporating these four PANoptosis-related genes was established. Patients were stratified into low/high-risk groups based on the median risk score, with the low-risk group showing better prognoses and higher levels of immune infiltration. Utilizing the risk score and clinical features, we developed a nomogram to predict 1-, 3- and 5-year survival probability. X-linked inhibitor of apoptosis protein (XIAP) emerged as a potentially risky factor with the highest hazard ratio. In vitro experiments demonstrated that XIAP inhibition enhances the antitumor effect of doxorubicin through the PANoptosis pathway. ConclusionPANoptosis holds an important role in BC prognosis and treatment.

Multi-omics analysis elucidates the relationship between intratumor microbiome and host immune heterogeneity in breast cancer.

Research has indicated that intratumor microbiomes affect the occurrence, progression, and therapeutic response in many cancer types by influencing the immune system. We aim to evaluate the characteristics of immune-related intratumor microbiomes (IRIMs) in breast cancer (BC) and search for potential prognosis prediction factors and treatment targets. The clinical information, microbiome data, transcriptomics data of The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) patients were obtained from Kraken-TCGA-Raw-Data and TCGA portal. The core tumor-infiltrating immune cell was identified using univariate Cox regression analysis. Based on consensus clustering analysis, BC patients were categorized into two immune subtypes, referred to as immune-enriched and immune-deficient subtypes. The immune-enriched subtype, characterized by higher levels of immune infiltration of CD8+ T and macrophage M1 cells, demonstrated a more favorable prognosis. Furthermore, significant differences in alpha-diversity and beta-diversity were observed between the two immune subtypes, and the least discriminant analysis effect size method identified 33 types of IRIMs. An intratumor microbiome-based prognostic signature consisting of four prognostic IRIMs (Acidibacillus, Succinimonas, Lachnoclostridium, and Pseudogulbenkiania) was constructed using the Cox proportional-hazard model, and it had great prognostic value. The prognostic IRIMs were correlated with immune gene expression and the sensitivity of chemotherapy drugs, specifically tamoxifen and docetaxel. In conclusion, our research has successfully identified two distinct immune subtypes in BC, which exhibit contrasting prognoses and possess unique epigenetic and intratumor microbiomes. The critical IRIMs were correlated with prognosis, tumor-infiltrating immune cell abundance, and immunotherapeutic efficacy in BC. Consequently, this study has identified potential IRIMs as biomarkers, providing a novel therapeutic approach for treating BC.IMPORTANCERecent research has substantiated the presence of the intratumor microbiome in tumor immune microenvironment, which could influence tumor occurrence and progression, as well as provide new opportunities for cancer diagnosis and treatment. This study identified the critical immune-related intratumor microbiome (Acidibacillus, Succinimonas, Lachnoclostridium, and Pseudogulbenkiania), which were correlated with prognosis, tumor-infiltrating immune cell abundance, and immunotherapeutic efficacy in breast cancer and might be the novel target to regulate immunotherapy in BC.