Immune Infiltration Research Articles

CTPAD: an interactive web application for comprehensive transcriptomic profiling in allergic diseases

BackgroundAllergic diseases are systemic chronic inflammatory diseases associated with multiorgan damage and complex pathogenesis. Several studies have revealed the association of gene expression abnormalities with the development of allergic diseases, but the biomedical field still lacks a public platform for comprehensive analysis and visualization of transcriptomic data of allergic diseases.ObjectiveThe aim of the study is to provide a comprehensive web tool for multiple analysis in allergic diseases.MethodsWe retrieved and downloaded human and mouse gene expression profile data associated with allergic diseases from the Gene Expression Omnibus (GEO) database and standardized the data uniformly. We used gene sets obtained from the MSigDB database for pathway enrichment analysis and multiple immune infiltration algorithms for the estimation of immune cell proportion. The basic construction of the web pages was based on the Shiny framework. Additionally, more convenient features were added to the server to improve the efficiency of the web pages, such as jQuery plugins and a comment box to collect user feedback.ResultsWe developed CTPAD, an interactive R Shiny application that integrates public databases and multiple algorithms to explore allergic disease-related datasets and implement rich transcriptomic visualization capabilities, including gene expression analysis, pathway enrichment analysis, immune infiltration analysis, correlation analysis, and single-cell RNA sequencing analysis. All functional modules offer customization options and can be downloaded in PDF format with high-resolution images.ConclusionsCTPAD largely facilitates the work of researchers without bioinformatics background to enable them to better explore the transcriptomic features associated with allergic diseases. CTPAD is available at https://smuonco.shinyapps.io/CTPAD/.

Integrated Analysis of Serum and Tissue microRNA Transcriptome for Biomarker Discovery in Gastric Cancer.

Gastric cancer (GC) poses a significant global health challenge, demanding a detailed exploration of its molecular landscape. Studies suggest that exposure to environmental pollutants can lead to changes in microRNA (miRNA) expression patterns, which may contribute to the development and progression of GC. MiRNAs have emerged as crucial regulators implicated in GC pathogenesis. The largest GC serum miRNA dataset to date, comprising 1417 non-cancer controls and 1417 GC samples was used. We conducted a comprehensive analysis of miRNA expression profiles. Differential expression analysis, co-expression network construction, and machine learning models were employed to identify key serum miRNAs and their association with clinical parameters. Weighted Gene Co-expression Network Analysis (WGCNA) and immune infiltration analysis were used to validate the importance of the key miRNA. A total of 1766 differentially expressed miRNAs were identified, with miR-1290, miR-1246, and miR-451a among the top up-regulated, and miR-6875-5p, miR-6784-5p, miR-1228-5p, and miR-6765-5p among the top down-regulated. WGCNA revealed that modules M1 and M5 were significantly associated with GC subtypes and disease status. MiRNA-target gene network analysis identified prognostically significant genes TP53, EMCN, CBX8, and ALDH1A3. Machine learning models LASSO, SVM, randomforest, and XGBOOST demonstrated the diagnostic potential of miRNA profiles. Tissue and serum miR-187 emerged as an independent prognostic factor, influencing patient survival across clinical parameters. Gene expression and immune cell infiltration were different in tissues stratified by miR-187 expression. In summary, the integration of differential gene expression, co-expression analysis, and immune cell profiling provided insights into the molecular intricacies of GC progression.

Spatial multiomics reveal intratumoral immune heterogeneity with distinct cytokine networks in lung cancer brain metastases.

The tumor microenvironment of brain metastases has become a focus in the development of immunotherapeutic drugs. However, countless brain metastasis patients have not experienced clinical benefit. Thus, understanding the immune cell composition within brain metastases, and how the immune cells interact with each other and other microenvironmental cell types, may be critical for optimizing immunotherapy. We applied spatial whole transcriptomic profiling with extensive multiregional sampling (19-30 regions per sample) and multiplex immunohistochemistry on formalin-fixed, paraffin-embedded lung cancer brain metastasis samples. We performed deconvolution of gene expression data to infer the abundances of immune cell populations and inferred spatial relationships from the multiplex immunohistochemistry data. We also described cytokine networks between immune and tumor cells and used a protein language model to predict drug-target interactions. Finally, we performed deconvolution of bulk RNA data to assess the prognostic significance of immune-metastatic tumor cellular networks. We show that immune cell infiltration has a negative prognostic role in lung cancer brain metastases. Our in-depth multiomics analyses further reveal recurring intratumoral immune heterogeneity and the segregation of myeloid and lymphoid cells into distinct compartments that may be influenced by distinct cytokine networks. By employing computational modeling, we identify drugs that may target genes expressed in both tumor core and regions bordering immune infiltrates. Finally, we illustrate the potential negative prognostic role of our immune-metastatic tumor cellular networks. Our findings advocate for a paradigm shift from focusing on individual genes or cell types, towards targeting networks of immune and tumor cells.

Integrated machine learning and Mendelian randomization reveal PALMD as a prognostic biomarker for nonspecific orbital inflammation

Background: Nonspecific Orbital Inflammation (NSOI) remains a perplexing enigma among proliferative inflammatory disorders. Its etiology is idiopathic, characterized by distinctive and polymorphous lymphoid infiltration within the orbital region. Preliminary investigations suggest that PALMD localizes within the cytosol, potentially playing a crucial role in cellular processes, including plasma membrane dynamics and myogenic differentiation. The potential of PALMD as a biomarker for NSOI warrants meticulous exploration. Methods: PALMD was identified through the intersection analysis of common DEGs from datasets GSE58331 and GSE105149 from the GEO database, alongside immune-related gene lists from the ImmPort database, using Lasso regression and SVM-RFE analysis. GSEA and GSVA were conducted with gene sets co-expressed with PALMD. To further investigate the correlation between PALMD and immune-related biological processes, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate immune microenvironment characteristics of each sample. The expression levels of PALMD were subsequently validated using GSE105149. Results: Among the 314 DEGs identified, several showed significant differences. Lasso and SVM-RFE algorithms pinpointed 15 hub genes. Functional analysis of PALMD emphasized its involvement in cell-cell adhesion, leukocyte migration, and leukocyte-mediated immunity. Enrichment analysis revealed that gene sets positively correlated with PALMD were enriched in immune-related pathways. Immune infiltration analysis indicated that resting dendritic cells, resting mast cells, activated NK cells, and plasma cells positively associate with PALMD expression. Conversely, naive B cells, activated dendritic cells, M0 and M1 macrophages, activated mast cells, activated CD4 memory T cells, and naive CD4 T cells showed a negative correlation with PALMD expression. PALMD demonstrated significant diagnostic potential in differentiating NSOI. Conclusions: This study identifies PALMD as a potential biomarker linked to NSOI, providing insights into its pathogenesis and offering new avenues for tracking disease progression.

Non-homogenous intratumor ionizing radiation doses synergize with PD1 and CXCR2 blockade

The efficacy and side effects of radiotherapy (RT) depend on parameters like dose and the volume of irradiated tissue. RT induces modulations of the tumor immune microenvironment (TIME) that are dependent on the dose. Low dose RT (LDRT, i.e., single doses of 0.5–2 Gy) has been shown to promote immune infiltration into the tumor. Here we hypothesize that partial tumor irradiation combining the immunostimulatory/non-lethal properties of LDRT with cell killing/shrinkage properties of high dose RT (HDRT) within the same tumor mass could enhance anti-tumor responses when combined with immunomodulators. In models of colorectal and breast cancer in immunocompetent female mice, partial irradiation (PI) with millimetric precision to deliver LDRT (2 Gy) and HDRT (16 Gy) within the same tumor induces substantial tumor control when combined with anti-PD1. Using flow cytometry, cytokine profiling and single-cell RNA sequencing, we identify a crosstalk between the TIME of the differentially irradiated tumor volumes. PI reshapes tumor-infiltrating CD8+ T cells into more cytotoxic and interferon-activated phenotypes but also increases the infiltration of pro-tumor neutrophils driven by CXCR2. The combination of the CXCR2 antagonist SB225002 with PD1 blockade and PI improves tumor control and mouse survival. Our results suggest a strategy to reduce RT toxicity and improve the therapeutic index of RT and immune checkpoint combinations.

Identification of necroptosis-related gene expression and the immune response in polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is a common reproductive and endocrine disorder; however, the understanding of the pathogenesis of PCOS remains unclear. Necroptosis is a newly discovered mechanism of cell death, and it is closely related to reproductive endocrine-related diseases. This study aimed to investigate the hub necroptosis-related genes in PCOS patients and its correlation with immune cell infiltration by bioinformatics methods. The gene expression chip result matrix and the annotation matrix files of the GSE34526, GSE8157, and GSE5090 datasets were downloaded from the GEO database. We analyzed the expression and correlation of the necroptosis-related genes in all samples, constructed a diagnostic model based on all necroptosis-related genes and genes with significant differences, performed unsupervised clustering of samples and gene enrichment analysis, and evaluated the correlations between the hub gene and immune cell infiltration levels by the R packages GSVA and CIBERSORT. Finally, PPI networks were constructed using the Cytoscape software GeneMANIA plug-in, and the miRNA, transcription factors, RBP, and drugs were predicted. Necroptosis-related genes have important relationships in the development of PCOS and are potentially associated with immune infiltration in PCOS patients.

SPECC1 as a pan-cancer biomarker: unraveling its role in drug sensitivity and resistance mechanisms

Previous studies have shown a relationship between SPECC1 and the prognosis of breast cancer, indicating a potential function for SPECC1 in the initiation and progression of cancer. However, the role played by SPECC1 in other tumors is not yet known. Therefore, we used bioinformatics techniques to conduct a thorough investigation into the possible mechanism of SPECC1 in pan-cancer, analyzing data reported in the literature as well as databases such as GTEx and CCLE, cBioportal, TCGA, and UCSC XENA. Comparing the results with matching normal tissues, the majority of cancers, including pancreatic adenocarcinoma (PAAD) and breast invasive carcinoma (BRCA), exhibited higher levels of SPECC1, while hepatocellular carcinoma (HCC) showed lower expression levels. SPECC1 was also found to be genetically mutated in endometrial cancer, sarcoma, and esophageal cancer. The prognosis of lung adenocarcinoma, kidney papillary cell carcinoma, and breast cancer is highly correlated with dysregulation of SPECC1 expression. This work helps guide clinical therapy by highlighting the sensitivity of tumor-treating medicines and the prognostic importance of SPECC1 in various malignancies. KEGG pathway enrichment analysis revealed focused adhesion, collagen-containing extracellular matrix (collagen), and the primary enrichment domains for SPECC1-related genes. These findings were obtained through gene annotation (GO) examination of SPECC1 expression. Primary mediators of the cytokine-cytokine receptor interaction include PICOC1-associated genes, cell-substrate junction genes, and extracellular matrix containing collagen. PICOC1-associated genes primarily mediate the PI3K-AKT signaling pathway. Drug sensitivity assay showed that SPECC1 high-expressing cell lines were more sensitive to docetaxel, doxorubicin, etc. In conclusion, the current study shows how SPECC1 is expressed in different cancers and how this expression relates to the prognosis of the tumor. It also revealed the mutations and copy number variations of SPECC1 in various tumors and its potential involvement in cellular pathway regulatory networks and cytological processes. This study examines the relationship between immune genes, cellular infiltration, and immunological scores in the tumor microenvironment, which explain the severity of the disease. This study looks at the response of SPEC1 expression to anticancer therapy. Explains the prognostic significance and drug response of SPECC-1.

Combining bioinformatics and machine learning to identify diagnostic biomarkers of TB associated with immune cell infiltration

ObjectiveThe asymptomatic nature of tuberculosis (TB) during its latent phase, combined with limitations in current diagnostic methods, makes accurate diagnosis challenging. This study aims to identify TB diagnostic biomarkers by integrating gene expression screening with machine learning, evaluating their diagnostic potential and correlation with immune cell infiltration. MethodsWe analyzed GSE19435, GSE19444, and GSE54992 datasets to identify differentially expressed genes (DEGs). GO and KEGG enrichment characterized gene functions. Three machine learning algorithms identified potential biomarkers, validated with GSE83456, GSE62525, and RT-qPCR on clinical samples. Immune cell infiltration was analyzed and verified with blood data. Results249 DEGs were identified, with PDE7A and DOK3 emerging as potential biomarkers. RT-qPCR confirmed their expression, showing AUCs above 0.75 and a combined AUC of 0.926 for TB diagnosis. Immune infiltration analysis revealed strong correlations between PDE7A, DOK3, and immune cells. ConclusionPDE7A and DOK3 show strong diagnostic potential for TB, closely linked to immune cell infiltration, and may serve as promising biomarkers and therapeutic targets.

Investigating PPT2’s role in ovarian cancer prognosis and immunotherapy outcomes

Ovarian cancer (OC) remains the primary cause of mortality among gynecological malignancies, and the identification of reliable molecular biomarkers to prognosticate OC outcomes is yet to be achieved. The gene palmitoyl protein thioesterase 2 (PPT2), which has been sparsely studied in OC, was closely associated with metabolism. This study aimed to determine the association between PPT2 expression, prognosis, immune infiltration, and potential molecular mechanisms in OC. We obtained the RNA-seq and clinical data from The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO) databases, then Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, nomogram, and calibration were conducted to assess and verify the role of PPT2. Gene set enrichment analysis (GSEA) was used to figure out the closely correlated pathways with PPT2. Overexpression experiment was performed to explore the function of PPT2. Our findings showed that PPT2 mRNA expression was apparent down-regulation in OC tissue compared to normal ovarian tissues in TCGA, GTEx datasets, and GEO datasets. This differential expression was also confirmed in our in-house datasets at both the mRNA and protein levels. Decreased PPT2 expression correlated with lower survival rates in TCGA, several GEO datasets, and our in-house datasets. Multivariate analysis revealed that PPT2 was an independent factor in predicting better outcomes for OC patients in TCGA and GEO. A negative correlation was revealed between immune infiltration and PPT2 expression through Single-sample GSEA (ssGSEA). Additionally, PPT2 was negatively correlated with an up-regulated immune score, stromal score, and estimate score, suggesting that patients with low PPT2 expression might benefit more from immunotherapy. Numerous chemical agents showed lower IC50 in patients with high PPT2 expression. In single-cell RNA sequencing (scRNA-seq) analysis of several OC datasets, we found PPT2 was mainly expressed in endothelial cells. Furthermore, we found that PPT2 inhibited OC cell proliferation in vitro. Our results demonstrated that PPT2 was considered a favorable prognostic biomarker for OC and may be vital in predicting response to immunotherapy and chemotherapy. Further research was needed to fully understand the relationship between PPT2 and immunotherapy efficacy in OC patients.

EBNA1BP2 identified as potential prognostic biomarker for multiple tumor types in pan-cancer analysis

BackgroundEpstein-Barr virus (EBV) infection has been closely linked to the development of various types of cancer. EB nuclear antigen 1 binding protein 2 (EBNA1BP2) is a crucial molecule for stable isolation of EBV in latent infection. However, the role of EBNA1BP2 in multiple tumor types is remains unclear. In this study, we comprehensively analyzed the functional characteristics of EBNA1BP2 and investigate its potential as a prognostic biomarker in pan-cancer.MethodsWe utilized data from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) databases and employed various bioinformatics analysis tools, including TIMER2.0, HPA, GEPIA2.0, PrognoScan, cBioPortal, CancerSEA, and BioGRID to explore the expression pattern, prognostic value, immune infiltration, and methylation level of EBNA1BP2 in pan-cancer. Additionally, we conducted enrichment analysis of genes associated with EBNA1BP2 to identify potential biological functions and pathways.ResultsOur analysis revealed that EBNA1BP2 expression was significantly higher in tumor tissues compared to tumor-adjacent tissues. We observed that lower expression of EBNA1BP2 in adrenocortical carcinoma (ACC), brain lower grade glioma (LGG), sarcoma (SARC), and uterine carcinosarcoma (UCS) was significantly associated with improved overall survival (OS) and disease-free survival (DFS). Furthermore, the promoter methylation level of EBNA1BP2 was downregulated in the majority of cancer types. At the single-cell level, EBNA1BP2 was found to be positively correlated with cell cycle and DNA repair processes, while negatively correlated with hypoxia. Additionally, EBNA1BP2 was associated with the infiltration of immune cells such as B cells, cancer-associated fibroblast cells, and CD8+ T cells. Gene enrichment analysis indicated that EBNA1BP2 was mainly involved in nucleoplasm and RNA binding pathways.ConclusionOur findings suggest that EBNA1BP2 may serve as a potential prognostic biomarker for survival in pan-cancer. Further experimental studies are needed to validate these findings and explore the underlying mechanisms by which EBNA1BP2 contributes to tumorigenesis.

Identification of neutrophil extracellular traps genes as potential biomarkers in psoriasis based on bioinformatics analysis

The epidermal infiltration of neutrophils is a hallmark of psoriasis (PSO) and its activation leads to the release of neutrophil extracellular traps (NETs). However, the molecular mechanism of NETs-related genes (NETRGs) has not been extensively studied in PSO. To define NETs-related-biomarkers for PSO. The GSE13355 and GSE78097 datasets, and NETRGs gene set were included in this study. The datasets used in this study were all microarray data. The weighted gene co-expression network analysis (WGCNA) and machine learning algorithms were used to mine key genes. Later on, single-gene gene set enrichment analysis (GSEA) and immune infiltration analysis were implemented. Finally, the expression of key genes was verified using quantitative real-time fluorescence PCR (qRT-PCR). A total of 3 key genes (S100A9, CLEC7A, and CXCR4) were derived, and they all had excellent diagnostic performance. The single-gene GSEA enrichment results indicated that the key genes were mainly enriched in the chemokine signaling pathway and humoral immune response in the high-expression group, while focal adhesion was enriched in the low-expression group. The correlation analysis indicated that all key genes were strongly negatively correlated with resting mast cells and TGF-β family member receptor, while they were strongly positively correlated with activated CD4 memory T cells and antigen processing and presentation. Lastly, the experimental results showed that the expression trends of key genes were consistent with public database. In this study, we successfully screened three potential PSO diagnostic genes (S100A9, CLEC7A and CXCR4) that were closely related to NETs, and these findings not only provided new molecular marker candidates for the precise diagnosis of PSO patients, but also revealed possible future therapeutic targets. However, further in-depth research and validation were necessary.

Multi-Omics Profiling Unveils the Complexity and Dynamics of Immune Infiltrates in Intrahepatic Cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (ICC) is a highly heterogeneous malignancy. The reasons behind the global rise in the incidence of ICC remain unclear, and there exists limited knowledge regarding the immune cells within the tumor microenvironment (TME). In this study, a more comprehensive analysis of multi-omics data was performed using machine learning methods. The study found that the immunoactivity of B cells, macrophages, and T cells in the infiltrating immune cells of ICC exhibits a significantly higher level of immunoactivity in comparison to other immune cells. During the immune sensing and response, the effect of antigen-presenting cells (APCs) such as B cells and macrophages on activating NK cells was weakened, while the effect of activating T cells became stronger. Simultaneously, four distinct subpopulations, namely BLp, MacrophagesLp, BHn, and THn, have been identified from the infiltrating immune cells, and their corresponding immune-related marker genes have been identified. The immune sensing and response model of ICC has been revised and constructed based on our current comprehension. This study not only helps to deepen the understanding the heterogeneity of infiltrating immune cells in ICC, but also may provide valuable insights into the diagnosis, evaluation, treatment, and prognosis of ICC.

A comprehensive analysis of CEBPA on prognosis and function in uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is one of the most common tumours of the female reproductive system. CCAAT enhancer-binding protein alpha (CEBPA) is a member of the transcription factor family involved in regulating processes such as cell proliferation, differentiation, metabolism, and the immune response. However, the role of CEBPA in UCEC has not been clarified. Here, we performed a comprehensive analysis to explore the expression level, prognostic value, immune infiltration and biological function of CEBPA in UCEC. In this study, we found that CEBPA expression was upregulated and associated with poor prognosis in UCEC patients. KEGG and GO analyses revealed that the genes positively correlated with CEBPA were enriched primarily in immune regulation and oxidative phosphorylation. Immune infiltration analysis revealed that CEBPA is strongly correlated with immune cell infiltration in UCEC. RT-qPCR indicated that CEBPA may regulate the OXPHOS level in Ishikawa cells. CCK-8, cell cycle, Transwell and scratch wound healing assays revealed that CEBPA promoted Ishikawa cell proliferation, invasion and migration. In addition, PPI and survival analyses suggested that CEBPG may be a potential target of CEBPA in UCEC. These results demonstrated that CEBPA may be a potential therapeutic target in UCEC.

Integrated bioinformatics analysis and experimental validation on malignant progression and immune cell infiltration of LTBP2 in gliomas

BackgroundGliomas are the highly aggressive brain tumor and also the most devastating human tumors. The latent TGF binding proteins (LTBP) had been found to be involved in malignant biological process and could be used as potent biomarkers in several solid tumors. While the role of LTBP family in human glioma remain to be elucidated.MethodsNormalized gene expression and corresponding clinical data of 2407 gliomas samples in public datasets were downloaded from Gliovis. Kaplan–Meier methods and Cox regression analysis was used for survival analyses.Western blot (WB) and Immunohistochemical (IHC) testing were employed to test LTBPs protein level in 154 gliomas samples. Correlation between LTBP2 expression and immune infiltration was evaluated by immunofluorescence (IF) and IHC in glioma tissues. CCK8 and flow cytometric analysis were used to detect the effect of LTBP2 on glioma cells. Orthotopic glioma- mouse models were utilized to evaluate effects in vivo.ResultsLTBP2 mRNA level was dramatically higher in glioma samples compared with non-tumor brain tissues in XENA-TCGA_GTEx, Gill and Gravendeel datasets (all P < 0.01), and its expression positively correlated with glioma WHO grade, IDH1/2 wildtype and mesenchymal subtypes. These results were confirmed by In-house cohort which was detected by WB and IHC. We found that gliomas patients with high LTBP2 level had shorter OS than those with low LTBP2 level. LTBP2 expression significantly associated with glioma immune score (Spearman r = 0.68, P < 0.01)) and strongly correlated with infiltration degreee of macrophages both in lower grade gliomas (LGG) and GBM. Knocking down LTBP2 obviously reduced proliferation and enhanced sensitivity to temozolomide in U87 and U251 cells. Nude mice with lower expression of LTBP2 had slower tumor growth, and accompanied by less tumor-associated macrophages (TAMs) infiltration detected by IHC staining in vivo. Finally, low LTBP2 expression glioma patients who received chemotherapy survived longer than patients with high LTBP2 expression.ConclusionLTBP2 could be used as a prognostic marker, and high LTBP2 expression related to abundant TAMs infiltration and with a worse response to chemotherapy.

Genes related to microglia polarization and immune infiltration in Alzheimer's Disease.

Alzheimer's Disease (AD) remains a significant challenge due to its complex etiology and socio-economic burden. In this study, we investigated the roles of macrophage polarization-related hub genes in AD pathology, focusing on their impact on immune infiltration and gene regulation in distinct brain regions. Using Gene Expression Omnibus (GEO) datasets GSE110226 (choroid plexus) and GSE1297 (hippocampal CA1), we identified key genes-EDN1, HHLA2, KL, TREM2, and WWTR1-associated with AD mechanisms and immune responses. Based on these findings, we developed a diagnostic model demonstrating favorable calibration and clinical applicability. Furthermore, we explored molecular interactions within mRNA-transcription factor and mRNA-miRNA regulatory networks, providing deeper insights into AD progression and identifying potential therapeutic targets. The novel identification of WWTR1 and HHLA2 as biomarkers expands the diagnostic toolkit for AD, offering new perspectives on the disease's underlying immune dynamics. However, external dataset validation and further in vitro and in vivo studies are required to confirm these results and their clinical relevance.

Exploring MPC1 as a potential ferroptosis-linked biomarker in the cervical cancer tumor microenvironment: a comprehensive analysis

BackgroundThe increasing problems of drug and radiotherapy resistance in cervical cancer underscores the need for novel methods for its management. Reports indicate that the expression of MPC1 may be associated with the tumor microenvironment and the occurrence of ferroptosis in cervical cancer. The objective of this study was to visually illustrate the prognostic significance and immunological characterization of MPC1 in cervical cancer.MethodsThe expression profile and prognostic significance of MPC1 were analyzed using various databases, including UALCAN, TIMER2, GEPIA2, and Kaplan–Meier Plotter. TISIDB, TIMER2, and immunohistochemical analysis were used to investigate the correlation between MPC1 expression and immune infiltration. GO enrichment analysis, KEGG analysis, Reactome analysis, ConsensusPathDB, and GeneMANIA were used to visualize the functional enrichment of MPC1 and signaling pathways related to MPC1. The correlation analysis was carried out to examine the relationship between MPC1 and Ferroptosis gene in TIMER 2.0, ncFO, GEPIA Database and Kaplan–Meier Plotter.ResultsWe demonstrated that the expression levels of MPC1 in cervical cancer tissues were lower than those in normal cervical tissues. Kaplan–Meier survival curves showed shorter overall survival in cervical cancer patients with low levels of MPC1 expression. The expression of MPC1 was related to the infiltrating levels of tumor-infiltrating immune cells in cervical cancer. Moreover, MPC1 expression was associated with the iron-mediated cell death pathway, and several important ferroptosis genes were upregulated in cervical cancer cells. Furthermore, after knocking down MPC1 in HeLa cells, the expression of these genes decreased.ConclusionThese findings indicate that MPC1 functions as a prognostic indicator and plays a role in the regulation of the ferroptosis pathway in cervical cancer.

Unveiling ficolins: diagnostic and prognostic biomarkers linked to the Tumor Microenvironment in Lung Cancer

BackgroundFicolins (FCNs) are a family of proteins, comprising FCN1, FCN2 and FCN3, and integral to the immune system which have been implicated in the onset and progression of tumors. Despite their recognized roles, a comprehensive analysis of FCNs in lung cancer remains elusive.MethodsWe employed a variety of bioinformatics tools, including UCSC, SangerBox, Ualcan, cBioPortal, String, Metascape, GeneMANIA, TIDE, CTD, and CAMP databases to investigate the differential expression, diagnostic and prognostic significance, genetic alterations, functional enrichment, immune infiltration, and potential immunotherapeutic implications of FCN1, FCN2, and FCN3 in lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). Additionally, RT-qPCR and immunohistochemistry were utilized to validate the expressions of FCNs at the mRNA and protein levels in LUSC and LUAD.ResultsOur comprehensive bioinformatic analysis, supported by RT-qPCR and immunohistochemistry, revealed that the expressions of FCN1, FCN2 and FCN3 were consistently downregulated in both LUSC and LUAD tumor tissues. FCNs demonstrated significant diagnostic potential for LUSC and LUAD, with the area under the receiver operating characteristic curve (AUC) for FCN1 and FCN3 exceeding 0.90. Furthermore, FCN2 and FCN3 showed a strong negative correlation with overall survival (OS) in LUSC, whereas FCN1 and FCN2 were positively correlated with OS in LUAD, suggesting their prognostic value in lung cancer. Gene enrichment analysis indicated that FCNs were predominantly associated with the complement system and complement activation pathways. Immune infiltration analysis further revealed a significant positive correlation between FCNs and the presence of neutrophils and resting mast cells. Our analysis of immunotherapy outcomes revealed a significant disparity in the immunophenoscore (IPS) among lung cancer patients treated with immune checkpoint inhibitors (ICIs), distinguishing those with high FCN expression from those with low FCN expression. Additionally, we identified small molecule compounds related to FCNs and drugs pertinent to LUSC and LUAD.ConclusionFCNs held promise as diagnostic and prognostic biomarkers for LUSC and LUAD. This study also elucidated the relationship of FCNs with the tumor microenvironment, offering novel insights into the immunotherapeutic landscape for LUSC and LUAD.

Immunological tolerance in breast cancer: some reasons for development

Immunological tolerance is one of the reasons for the development and progression of malignant tumors. The tumor immune cycle regulates the normal antitumor immune response, and it’s disruption is responsible for the development of immunological tolerance. This article provides a review of russian and foreign literature published in databases such as PubMed, Medline, and Cochrane, eLibrary in the last 5 years, focusing on the emergence of immunological tolerance in breast cancer from the perspective of disrupted regulation of tumor immune cycle phases: expression of antigens on the surface of tumor cells, cancer antigen presentation, priming and activation T cells, immune infiltration of the tumor site, recognition, and elimination of tumor cells. Understanding the mechanisms underlying tumor immune cycle disruption is important for identifying new immunopathogenetic links in the development of breast cancer, as well as identifying targets to improve the effectiveness of therapy for advanced breast cancer.

Hepatocellular Carcinoma Immune Microenvironment Analysis: A Comprehensive Assessment with Computational and Classical Pathology.

The spatial variability and clinical relevance of the tumour immune microenvironment (TIME) are still poorly understood for hepatocellular carcinoma (HCC). Here we aim to develop a deep learning (DL)-based image analysis model for the spatial analysis of immune cell biomarkers, and microscopically evaluate the distribution of immune infiltration. Ninety-two HCC surgical liver resections and 51 matched needle biopsies were histologically classified according to their immunophenotypes: inflamed, immune-excluded, and immune-desert. To characterise the TIME on immunohistochemistry (IHC)-stained slides, we designed a multi-stage DL algorithm, IHC-TIME, to automatically detect immune cells and their localisation in TIME in tumour-stromal, centre-border segments. Two models were trained to detect and localise the immune cells on IHC-stained slides. The framework models, i.e. immune cell detection models and tumour-stroma segmentation, reached 98% and 91% accuracy, respectively. Patients with inflamed tumours showed better recurrence-free survival than those with immune-excluded or immune desert tumours. Needle biopsies were found to be 75% accurate in representing the immunophenotypes of the main tumour. Finally, we developed an algorithm that defines immunophenotypes automatically based on the IHC-TIME analysis, achieving an accuracy of 80%. Our DL-based tool can accurately analyse and quantify immune cells on IHC-stained slides of HCC. The microscopical classification of the TIME can stratify HCCs according to the patient prognosis. Needle biopsies can provide valuable insights for TIME-related prognostic prediction, albeit with specific constraints. The computational pathology tool provides a new way to study the HCC TIME.

Integrative multi-omics analysis unveils the connection between transcriptomic characteristics associated with mitochondria and the tumor immune microenvironment in lower-grade gliomas

Lower-grade gliomas (LGGs) exhibit diverse clinical behaviors and varying immune infiltration levels. Mitochondria have been implicated in numerous cancer pathogenesis and development, including LGGs. However, the precise biological functions of mitochondrial genes in shaping the immune landscape and the prognostic significance of LGGs remain elusive. Utilizing the Mito-Carta3.0 database, we curated a total of 1136 genes implicated in mitochondrial functions. By leveraging the expression profiles of 1136 genes related to mitochondria, we successfully categorized LGGs into four distinctive mitochondria-related transcriptome (MRT) subtypes. Our thorough analysis conclusively demonstrated that these subtypes exhibited marked disparities. To enable a personalized and integrated evaluation of LGG patients, we developed a prognostic signature known as MRT-related prognostic signature (MTRS). MTRS demonstrated correlation with mitochondria-related transcriptome (MRT) subtypes, allowing the assessment of patients’ prognosis and immune microenvironment. We conducted a detailed exploration of the single-cell distribution of MTRS in lower-grade gliomas and verified the core genes of MTRS within the spatial transcriptome of these tumors. Furthermore, our study pinpointed MGME1 as the pivotal gene in the model, functioning as an oncogene that exerts influence on cell proliferation and migration capabilities. Our research highlights the importance of mitochondrial transcriptomic features in LGGs, offering paths for tailored therapies.