Patterns Of Immune Cell Infiltration Research Articles

Multiomics sequencing and immune microenvironment characteristics define three subtypes of small cell neuroendocrine carcinoma of the cervix.

Small cell cervical carcinoma (SCCC) is the most common neuroendocrine tumor in the female genital tract, with an unfavorable prognosis and lacking an evidence-based therapeutic approach. Until now, the distinct subtypes and immune characteristics of SCCC combined with genome and transcriptome have not been described. We performed genomic (n = 18), HPV integration (n = 18), and transcriptomic sequencing (n = 19) of SCCC samples. We assessed differences in immune characteristics between SCCC and conventional cervical cancer, and other small cell neuroendocrine carcinomas, through bioinformatics analysis and immunohistochemical assays. We stratified SCCC patients through non-negative matrix factorization and described the characteristics of these distinct types. We further validated it using multiplex immunofluorescence (n = 77) and investigated its clinical prognostic effect. We confirmed a high frequency of PIK3CA and TP53 alterations and HPV18 integrations in SCCC. SCCC and other small cell carcinoma had similar expression signatures and immune cell infiltration patterns. Comparing patients with SCCC to those with conventional cervical cancer, the former presented immune excluded or 'desert' infiltration. The number of CD8+ cells in the invasion margin of SCCC patients predicted favorable clinical outcomes. We identified three transcriptome subtypes: an inflamed phenotype with high-level expression of genes related to the MHC-II complex (CD74) and IFN-α/β (SCCC-I), and two neuroendocrine subtypes with high-level expression of ASCL1 or NEUROD1, respectively. Combined with multiple technologies, we found that the neuroendocrine groups had more TP53 mutations and SCCC-I had more PIK3CA mutations. Multiplex immunofluorescence validated these subtypes and SCCC-I was an independent prognostic factor of overall survival. These results provide insights into SCCC tumor heterogeneity and potential therapies. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Characterization of the tumor microenvironment in the mouse oral cancer (MOC1) model after orthotopic implantation in the buccal mucosa.

Preclinical models are invaluable for studies of head and neck cancer. There is growing interest in the use of orthotopic syngeneic models, wherein cell lines are injected into the oral cavity of immunocompetent mice. In this brief report, we describe injection of mouse oral cancer 1 (MOC1) cells into the buccal mucosa and illustrate the tumor growth pattern, lymph node response, and changes in the tumor immune microenvironment over time. MOC1 cells were injected into the buccal mucosa of C57BL6 mice. Animals were sacrificed at 7, 14, 21, or 27 days. Tumors and lymph nodes were analyzed by flow cytometry. All mice developed tumors by day 7 and required euthanasia for tumor burden and/or weight loss by day 27. Lymph node mapping showed that these tumors reliably drain to a submandibular lymph node. The proportion of intratumoral CD8+ T cells decreased over time, while neutrophilic myeloid cells increased dramatically. Growth of orthotopic MOC2 and MOC22 also showed similar growth patterns versus published data in flank tumors. When used orthotopically in the buccal mucosa, the MOC1 model induces a robust lymph node response and distinct pattern of immune cell infiltration, with peak immune infiltration by day 14.

Transcriptome sequencing reveals novel biomarkers and immune cell infiltration in esophageal tumorigenesis.

Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies worldwide, and its development comprises a multistep process from intraepithelial neoplasia (IN) to carcinoma (CA). However, the critical regulators and underlying molecular mechanisms remain largely unknown. To explore the genes and infiltrating immune cells in the microenvironment that are associated with the multistage progression of ESCC to facilitate diagnosis and early intervention. A mouse model mimicking the multistage development of ESCC was established by providing warter containing 4-nitroquinoline 1-oxide (4NQO) to C57BL/6 mice. Moreover, we established a control group without 4NQO treatment of mice. Then, transcriptome sequencing was performed for esophageal tissues from patients with different pathological statuses, including low-grade IN (LGIN), high-grade IN (HGIN), and CA, and controlled normal tissue (NOR) samples. Differentially expressed genes (DEGs) were identified in the LGIN, HGIN, and CA groups, and the biological functions of the DEGs were analyzed via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The CIBERSORT algorithm was used to detect the pattern of immune cell infiltration. Immunohistochemistry (IHC) was also conducted to validate our results. Finally, the Luminex multiplex cytokine analysis was utilized to measure the serum cytokine levels in the mice. Compared with those in the NOR group, a total of 681541, and 840 DEGs were obtained in the LGIN, HGIN, and CA groups, respectively. Using the intersection of the three sets of DEGs, we identified 86 genes as key genes involved in the development of ESCC. Enrichment analysis revealed that these genes were enriched mainly in the keratinization, epidermal cell differentiation, and interleukin (IL)-17 signaling pathways. CIBERSORT analysis revealed that, compared with those in the NOR group, M0 and M1 macrophages in the 4NQO group showed stronger infiltration, which was validated by IHC. Serum cytokine analysis revealed that, compared with those in the NOR group, IL-1β and IL-6 were upregulated, while IL-10 was downregulated in the LGIN, HGIN, and CA groups. Moreover, the expression of the representative key genes, such as S100a8 and Krt6b, was verified in external human samples, and the results of immunohistochemical staining were consistent with the findings in mice. We identified a set of key genes represented by S100a8 and Krt6b and investigated their potential biological functions. In addition, we found that macrophage infiltration and abnormal alterations in the levels of inflammation-associated cytokines, such as IL-1β, IL-6, and IL-10, in the peripheral blood may be closely associated with the development of ESCC.

Integrating bulk and single-cell sequencing data to construct a Scissor+ dendritic cells prognostic model for predicting prognosis and immune responses in ESCC

Esophageal squamous cell carcinoma (ESCC) is characterized by molecular heterogeneity with various immune cell infiltration patterns, which have been associated with therapeutic sensitivity and resistance. In particular, dendritic cells (DCs) are recently discovered to be associated with prognosis and survival in cancer. However, how DCs differ among ESCC patients has not been fully comprehended. Recently, the advance of single-cell RNA sequencing (scRNA-seq) enables us to profile the cell types, states, and lineages in the heterogeneous ESCC tissues. Here, we dissect the ESCC tumor microenvironment at high resolution by integrating 192,078 single cells from 60 patients, including 4379 DCs. We then used Scissor, a method that identifies cell subpopulations from single-cell data that are associated bulk samples with genomic and clinical information, to stratify DCs into Scissorhi and Scissorlow subtypes. We applied the Scissorhi gene signature to stratify ESCC scRNAseq patient, and we found that PD-L1, TIGIT, PVR and IL6 ligand-receptor-mediated cell interactions existed mainly in Scissorhi patients. Finally, based on the Scissor results, we successfully developed a validated prognostic risk model for ESCC and further validated the reliability of the risk prediction model by recruiting 40 ESCC clinical patients. This information highlights the importance of these genes in assessing patient prognosis and may help in the development of targeted or personalized therapies for ESCC.

T cell-mediated tumor killing sensitivity gene signature-based prognostic score for acute myeloid leukemia.

Acute myeloid leukemia (AML) is an aggressive, heterogenous hematopoetic malignancies with poor long-term prognosis. T-cell mediated tumor killing plays a key role in tumor immunity. Here, we explored the prognostic performance and functional significance of a T-cell mediated tumor killing sensitivity gene (GSTTK)-based prognostic score (TTKPI). Publicly available transcriptomic data for AML were obtained from TCGA and NCBI-GEO. GSTTK were identified from the TISIDB database. Signature GSTTK for AML were identified by differential expression analysis, COX proportional hazards and LASSO regression analysis and a comprehensive TTKPI score was constructed. Prognostic performance of the TTKPI was examined using Kaplan-Meier survival analysis, Receiver operating curves, and nomogram analysis. Association of TTKPI with clinical phenotypes, tumor immune cell infiltration patterns, checkpoint expression patterns were analysed. Drug docking was used to identify important candidate drugs based on the TTKPI-component genes. From 401 differentially expressed GSTTK in AML, 24 genes were identified as signature genes and used to construct the TTKPI score. High-TTKPI risk score predicted worse survival and good prognostic accuracy with AUC values ranging from 75 to 96%. Higher TTKPI scores were associated with older age and cancer stage, which showed improved prognostic performance when combined with TTKPI. High TTKPI was associated with lower naïve CD4 T cell and follicular helper T cell infiltrates and higher M2 macrophages/monocyte infiltration. Distinct patterns of immune checkpoint expression corresponded with TTKPI score groups. Three agents; DB11791 (Capmatinib), DB12886 (GSK-1521498) and DB14773 (Lifirafenib) were identified as candidates for AML. A T-cell mediated killing sensitivity gene-based prognostic score TTKPI showed good accuracy in predicting survival in AML. TTKPI corresponded to functional and immunological features of the tumor microenvironment including checkpoint expression patterns and should be investigated for precision medicine approaches.

Prognostic value of CD8+T cells related genes and exhaustion regulation of Notch signaling pathway in hepatocellular carcinoma.

Immunotherapy has emerged as the primary treatment modality for patients with advanced Hepatocellular carcinoma (HCC). However, its clinical efficacy remains limited, benefiting only a subset of patients, while most exhibit immune tolerance and face a grim prognosis. The infiltration of immune cells plays a pivotal role in tumor initiation and progression. In this study, we conducted an analysis of immune cell infiltration patterns in HCC patients and observed a substantial proportion of CD8+T cells. Leveraging the weighted gene co-expression network analysis (WGCNA), we identified 235 genes associated with CD8+T cell and constructed a risk prediction model. In this model, HCC patients were stratified into a high-risk and low-risk group. Patients in the high-risk group exhibited a lower survival rate, predominantly presented with intermediate to advanced stages of cancer, displayed compromised immune function, showed limited responsiveness to immunotherapy, and demonstrated elevated expression levels of the Notch signaling pathway. Further examination of clinical samples demonstrated an upregulation of the Notch1+CD8+T cell exhaustion phenotype accompanied by impaired cytotoxicity and cytokine secretion functions that worsened with increasing Notch activation levels. Our study not only presents a prognostic model but also highlights the crucial involvement of the Notch pathway in CD8+T cell exhaustion-a potential target for future immunotherapeutic interventions.

Abstract LB314: Organ-specific roles of CD83+ macrophages in breast cancer

Abstract The purpose of this study is to understand how heterogeneity in organ-specific immune infiltrates can lead to Triple Negative Breast Cancer (TNBC) progression and immunotherapies resistance in metastasis. Both innate and adaptive immune responses are essential for breast cancer progression. Using single-cell multiplexing technology on human and murine models of primary and metastatic breast cancers, we investigated the pattern of immune cell infiltration in bone and lung metastases in a temporal manner. The parallel profiling of metastasis sites revealed a strong involvement of the innate immunity in restraining tumor progression with an acute increase in Mrc1+ Cd83+ tumor-associated macrophages at the early stage of lung colonization. Interestingly, the increase was modest in bone metastasis and remained consistent overtime despite bone metastasis progression. In contrast, we saw an increase in the number of Ly6G+ Lrg1+ granulocytes. Profiling of paired TNBC patient samples from the primary and metastatic sites have shown an immunologically quiescent tumor microenvironment in metastasis as compared to the primary tumors. Hence, to further explore the role of Mrc1+ Cd83+ macrophages on therapeutic resistance, we profiled the orthotopic claudin-low TNBC models, T11 and T12, following their exposure to chemotherapy. While responsive tumors displayed a decreased infiltration of Mrc1+ Cd83+ macrophages, the opposite phenotype was observed in non-responders, suggesting their involvement in tumor progression. Overall, our results suggest differential responses in untreated lung/bone metastases and primary tumors indicating divergent roles for Cd83+ TAMs in restraining or promoting tumor progression, while also indicating some crosstalk between neutrophils and stromal cells that aid in bone metastatic progression. These site-specific differences in immune cell phenotype indicate that there is a great need to thoroughly characterize the tumor immune compartment in both the primary and the metastatic setting to improve treatment responses. Considering the inhibitory effect of macrophages on T cells in these models, this study highlights a tissue-specific reprogramming of immune cells in breast cancer metastasis. Citation Format: Swarnima Singh, Igor Bado. Organ-specific roles of CD83+ macrophages in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB314.

Identification of diagnostic biomarkers and immune cell infiltration in coronary artery disease by machine learning, nomogram, and molecular docking.

Coronary artery disease (CAD) is still a lethal disease worldwide. This study aims to identify clinically relevant diagnostic biomarker in CAD and explore the potential medications on CAD. GSE42148, GSE180081, and GSE12288 were downloaded as the training and validation cohorts to identify the candidate genes by constructing the weighted gene co-expression network analysis. Functional enrichment analysis was utilized to determine the functional roles of these genes. Machine learning algorithms determined the candidate biomarkers. Hub genes were then selected and validated by nomogram and the receiver operating curve. Using CIBERSORTx, the hub genes were further discovered in relation to immune cell infiltrability, and molecules associated with immune active families were analyzed by correlation analysis. Drug screening and molecular docking were used to determine medications that target the four genes. There were 191 and 230 key genes respectively identified by the weighted gene co-expression network analysis in two modules. A total of 421 key genes found enriched pathways by functional enrichment analysis. Candidate immune-related genes were then screened and identified by the random forest model and the eXtreme Gradient Boosting algorithm. Finally, four hub genes, namely, CSF3R, EED, HSPA1B, and IL17RA, were obtained and used to establish the nomogram model. The receiver operating curve, the area under curve, and the calibration curve were all used to validate the accuracy and usefulness of the diagnostic model. Immune cell infiltrating was examined, and CAD patients were then divided into high- and low-expression groups for further gene set enrichment analysis. Through targeting the hub genes, we also found potential drugs for anti-CAD treatment by using the molecular docking method. CSF3R, EED, HSPA1B, and IL17RA are potential diagnostic biomarkers for CAD. CAD pathogenesis is greatly influenced by patterns of immune cell infiltration. Promising drugs offers new prospects for the development of CAD therapy.

Profiling of immune infiltration landscape of ruptured intracranial aneurysm.

Previous research has indicated that the rupture of intracranial aneurysm (IA) is a significant contributor to mortality from stroke. The objective of this present study was to examine the infiltration patterns in ruptured intracranial aneurysm (RIA), with the aim of generating insights that could inform the development of effective immunotherapeutic approaches. To achieve this, we obtained Gene Expression Omnibus datasets pertaining to ruptured aneurysms, encompassing a total of 19 unruptured intracranial aneurysms (UIA) and 27 RIA. Subsequently, we conducted differential gene analysis and immune cell analysis specifically for the RIA. According to the conducted studies, the analysis has identified 10 hub genes within key modules. Through the utilization of Kyoto Encyclopedia of Genes and Genomes pathway and gene ontology terms analyses, it has been established that genes exhibiting differential expression are associated with immune cell infiltration in the aneurysm wall. Furthermore, the implementation of the CIBERSORT algorithm has revealed that there are 22 distinct immune cells between RIA and tissues of UIA. IA samples contained a higher proportion of macrophages M1, mast cells resting, and CD4 naive T cells, while macrophages M0 and neutrophils were relatively lower in RIA compared with those in UIA. The current study initially identified highly conservative hub genes and immune cell infiltration patterns in IA. Data presented in the current study improved understanding of immune genes that drive IA which can be exploited in development of effective immunotherapies.

Multi-omics pan-cancer analyses identify MCM4 as a promising prognostic and diagnostic biomarker

Minichromosome Maintenance Complex Component 4 (MCM4) is a vital component of the mini-chromosome maintenance complex family, crucial for initiating the replication of eukaryotic genomes. Recently, there has been a growing interest in investigating the significance of MCM4 in different types of cancer. Despite the existing research on this topic, a comprehensive analysis of MCM4 across various cancer types has been lacking. This study aims to bridge this knowledge gap by presenting a thorough pan-cancer analysis of MCM4, shedding light on its functional implications and potential clinical applications. The study utilized multi-omics samples from various databases. Bioinformatic tools were employed to explore the expression profiles, genetic alterations, phosphorylation states, immune cell infiltration patterns, immune subtypes, functional enrichment, disease prognosis, as well as the diagnostic potential of MCM4 and its responsiveness to drugs in a range of cancers. Our research demonstrates that MCM4 is closely associated with the oncogenesis, prognosis and diagnosis of various tumors and proposes that MCM4 may function as a potential biomarker in pan-cancer, providing a deeper understanding of its potential role in cancer development and 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.

Unveiling the mitophagy puzzle in non-alcoholic fatty liver disease (NAFLD): Six hub genes for early diagnosis and immune modulatory roles

BackgroundNon-alcoholic fatty liver disease (NAFLD) stands as a predominant chronic liver ailment globally, yet its pathogenesis remains elusive. This study aims to identify Hub mitophagy-related genes (MRGs), and explore the underlying pathological mechanisms through which these hub genes regulate NAFLD. MethodsA total of 3 datasets were acquired from the GEO database and integrated to identify differentially expressed genes (DEGs) in NAFLD and perform Gene Set Enrichment Analysis (GSEA). By intersecting DEGs with MRGs, mitophagy-related differentially expressed genes (MRDEGs) were obtained. Then, hub MRGs with diagnostic biomarker capability for NAFLD were screened and a diagnostic prediction model was constructed and assessed using Nomogram, Decision Curve Analysis (DCA), and ROC curves. Functional enrichment analysis was conducted on the identified hub genes to explore their biological significance. Additionally, regulatory networks were constructed using databases. NAFLD was stratified into high and low-risk groups based on the Riskscore from the diagnostic prediction model. Furthermore, single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithms were employed to analyze immune cell infiltration patterns and the relationship between Hub MRGs and immune cells. ResultsThe integrated dataset comprised 122 NAFLD samples and 31 control samples. After screening, 18 MRDEGs were identified. Subsequently, six hub MRGs (NR4A1, PPP2R2A, P4HA1, TUBB6, DUSP1, NAMPT) with diagnostic potential were selected through WGCNA, logistic regression, SVM, RF, and LASSO models, all significantly downregulated in NAFLD samples compared to the control group. A diagnostic prediction model based on these six genes demonstrated robust predictive performance. Functional enrichment analysis of the six hub genes revealed involvement in processes such as protein phosphorylation or dephosphorylation. Correlation analysis demonstrated a significant association between hub MRGs and infiltrating immune cells. ConclusionWe identified six hub MRGs in NAFLD and constructed a diagnostic prediction model based on these six genes, applicable for early NAFLD diagnosis. These genes may participate in regulating NAFLD progression through the modulation of mitophagy and immune activation. Our findings may contribute to subsequent clinical and basic research on NAFLD.

Single-cell integrated transcriptomics reveals the role of keratinocytes in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is a prevalent malignant tumor with significant morbidity and mortality. Understanding the molecular mechanisms of HNSCC and identifying prognostic markers and therapeutic targets are crucial for improving patient outcomes. In this study, we utilized single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data to comprehensively analyze HNSCC at the cellular level. We identified keratinocytes as the predominant cell type in tumor samples, suggesting their potential role in HNSCC development. Through hdWGCNA co-expression network analysis, we identified gene modules associated with HNSCC progression. Furthermore, we constructed a prognostic model based on specific genes and demonstrated its robust predictive performance in multiple datasets. The model exhibited strong correlations with immune cell infiltration patterns and signaling pathways related to tumor progression. Additionally, drug sensitivity analysis revealed potential chemotherapeutic targets for HNSCC treatment. Our findings provide valuable insights into the molecular characteristics and immune microenvironment of HNSCC, offering new perspectives for prognosis prediction and therapeutic interventions in clinical practice. Further research is warranted to validate and expand upon these findings, ultimately improving patient outcomes in HNSCC.

Cuproptosis-associated hub gene identification and immune cell infiltration patterns in silicosis

Recent research has hinted at a potential connection between silicosis, a fibrotic lung disease caused by exposure to crystalline silica particles, and cuproptosis. The aim of the study was to explore how cuproptosis-related genes (CRGs) may influence the development of silicosis and elucidate the underlying mechanisms. An analysis of genes associated with both silicosis and cuproptosis was conducted. Key gene identification was achieved through the application of two machine learning techniques. Additionally, the correlation between these key genes and immune cell populations was explored and the critical pathways were discerned. To corroborate our findings, the expression of key genes was verified in both a publicly available silica-induced mouse model and our own silicosis mouse model. A total of 12 differentially expressed CRGs associated with silicosis were identified. Further analysis resulted in the identification of 6 CRGs, namely LOX, SPARC, MOXD1, ALB, MT-CO2, and AOC2. Elevated immune cell infiltration of CD8 T cells, regulatory T cells, M0 macrophages, and neutrophils in silicosis patients compared to healthy controls was indicated. Validation in a silica-induced pulmonary fibrosis mouse model supported SPARC and MT-CO2 as potential signature genes for the prediction of silicosis. These findings highlight a strong association between silicosis and cuproptosis. Among CRGs, LOX, SPARC, MOXD1, ALB, MT-CO2, and AOC2 emerged as pivotal players in the context of silicosis by modulating CD8 T cells, regulatory T cells, M0 macrophages, and neutrophils.

The endoplasmic reticulum stress-related genes and molecular typing predicts prognosis and reveals characterization of tumor immune microenvironment in lung squamous cell carcinoma

BackgroundEndoplasmic reticulum stress (ERS) acts critical roles on cell growth, proliferation, and metastasis in various cancers. However, the relationship between ERs and lung squamous cell carcinoma (LUSC) prognoses still remains unclear.MethodsThe consensus clustering analysis of ERS-related genes and the differential expression analysis between clusters were investigated in LUSC based on TCGA database. Furthermore, ERS-related prognostic risk models were constructed by LASSO regression and Cox regression analyses. Then, the predictive effect of the risk model was evaluated by Kaplan–Meier, Cox regression, and ROC Curve analyses, as well as validated in the GEO cohort. According to the optimal threshold, patients with LUSC were divided into high- and low- risk groups, and somatic mutations, immune cell infiltration, chemotherapy response and immunotherapy effect were systematically analyzed.ResultsTwo ERS-related clusters were identified in patients with LUSC that had distinct patterns of immune cell infiltration. A 5-genes ERS-related prognostic risk model and nomogram were constructed and validated. Kaplan–Meier curves and Cox regression analysis showed that ERS risk score was an independent prognostic factor (p < 0.001, HR = 1.317, 95% CI = 1.159–1.496). Patients with low-risk scores presented significantly lower TIDE scores and significantly lower IC50 values for common chemotherapy drugs such as cisplatin and gemcitabine.ConclusionERS-related risk signature has certain prognostic value and may be a potential therapeutic target and prognostic biomarker for LUSC patients.

Identification of an immune-related eRNA prognostic signature for clear cell renal cell carcinoma.

Immune-related enhancer RNAs (eRNAs) have garnered significant attention in cancer metabolism research, yet their specific roles in ccRCC have remained elusive. We retrieved eRNA expression profiles from TCGA database and identified immune-related eRNAs (IREs) by assessing their co-expression with immune genes. Utilizing consensus clustering, we organized these IREs into two distinct clusters. The construction of an IREs signature was accomplished through the LASSO and multivariate Cox analysis. Furthermore, we performed Cell Counting Kit-8 and clonogenic assays to assess changes in the proliferative capacity of Caki-1 and 769-P cells. The existence of two clusters of immune-related eRNAs in ccRCC, each with distinctive prognostic and immunological attributes. Cluster B exhibited immunosuppressive properties and displayed a positive correlation with immunosuppressive cells. Functional enrichment analysis unveiled their involvement in several tumor-promoting pathways, metabolic pathways and immune pathways. The IREs signature demonstrated its potential to accurately predict patient immune and prognostic characteristics. AC003092.1, an eRNA strongly associated with patient survival, emerged as a potential oncogene significantly linked to adverse prognosis and the presence of immunosuppressive cells and checkpoints in ccRCC patients. Notably, AC003092.1 displayed marked upregulation in ccRCC tissues and cell lines, and its knockdown substantially inhibited the proliferation of Caki-1 and 769-P cells. We established a robust predictive model that played a vital role in determining the prognosis, clinicopathological characteristics and immune cell infiltration patterns of ccRCC patients. IRE, particularly AC003092.1, which was strongly associated with survival, hold promise as novel immunotherapeutic targets for ccRCC.

Improved overall survival in patients with high-grade serous ovarian cancer is associated with CD16a+ immunologic neighborhoods containing NK cells, T cells and macrophages.

For patients with high grade serous carcinoma of the ovary (HGSC), survival rates have remained static for the last half century. Despite the presence of tumor mutations and infiltration of immune cells, existing immunotherapies have achieved little success against HGSC. These observations highlight a gap in the understanding of how the immune system functions and interacts within HGSC tumors. We analyzed duplicate core samples from 939 patients with HGSC to understand patterns of immune cell infiltration, localization, and associations with clinical features. We used high-parameter immunohistochemical/Opal multiplex, digital pathology, computational biology, and multivariate analysis to identify immune cell subsets and their associations with HGSC tumors. We defined six patterns of cellular infiltration by spatially restricted unsupervised clustering of cell subsets. Each pattern was represented to some extent in most patient samples, but their specific distributions differed. Overall (OS) and progression-free survival (PFS) corresponded with higher infiltration of CD16a+ cells, and their co-localization with macrophages, T cells, NK cells, in one of six cellular neighborhoods that we defined with our spatial assessment. Immune cell neighborhoods containing CD16a+ cells are associated with improved OS and PFS for patients with HGSC. Patterns of immunologic neighborhoods differentiate patient outcomes, and could inform future, more precise approaches to treatment.

Mapping the single cell spatial immune landscapes of the melanoma microenvironment.

Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.

Disulfidptosis-related lncRNA signatures assess immune microenvironment and drug sensitivity in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is associated with a high mortality rate, where resistance to immunotherapy and chemotherapy plays a crucial role. A newly identified form of cell death called disulfidptosis shows promise, but its biological mechanism in HCC remains uncertain. In this study, a prognostic model was developed for Disulfidptosis-related long non-coding RNAs (DRLs) from 370 HCC patients sourced from TCGA-LIHC, utilizing five key features: AC026356.1, AC073254.1, PXN-AS1 expression, AC026412.3, and AC099066.2. High-risk HCC patients had lower survival, CD4+ T cell infiltration, and elevated immune checkpoint gene expression. Furthermore, based on the features of DRLs, HCC was classified into three subtypes. Notably, patients belonging to different subtypes demonstrated varying overall survival rates, immune cell infiltration patterns, and sensitivity to immune therapy. Moreover, the novel DRL AC026412.3 (HR = 40.207) emerged as the most significant prognostic factor, exhibiting high expression across all HCC cells. Elevated expression of AC026412.3 promoted HCC cell proliferation and induced resistance to gefitinib. In conclusion, we have discovered five DRLs and constructed a prognostic risk model. Our findings validate the correlation between DRL-related prognostic models, tumor subtypes, and the HCC immune microenvironment along with its implications for immunotherapy. Moreover, further investigation into the molecular mechanisms of key biomarkers like AC026412.3 in the future will contribute significantly to advancing our comprehension of HCC's pathogenesis and drug resistance mechanisms.

M6A plays a potential role in carotid atherosclerosis by modulating immune cell modification and regulating aging-related genes

RNA N6-methyladenosine (m6A) regulators play essential roles in diverse biological processes, including immune responses. Mounting evidence suggests that their dysregulation is intricately linked to numerous diseases. However, the role of m6A-associated genes in carotid atherosclerosis and their relationship with aging and immune cells remain unclear. Analyze the expression profiles of m6A-related genes in carotid atherosclerosis-related datasets. Based on the expression patterns of m6A-related genes, perform consistent clustering analysis of carotid atherosclerosis samples and investigate associated immune cell infiltration patterns and aging characteristics. Develop an m6A prediction model specific to carotid atherosclerosis and analyze the relationships between immune cells infiltration and aging features. The m6A methylation modification level exhibited a substantial decrease in early-stage carotid atherosclerosis samples compared to late-stage carotid atherosclerosis samples. Subsequently, two distinct m6A subtypes were defined through consensus clustering analysis, with the lower m6A modification level group showing associations with heightened immune cell infiltration and increased expression of aging-related genes. A model composed of five m6A-related genes was formulated, and the results indicated that this model possesses effective predictive and therapeutic capabilities for carotid atherosclerosis. Furthermore, the downregulation of YTHDC1 expression resulted in elevated expression of inflammatory factors and a decrease in the expression of the aging-related gene RGN. Single-cell data analysis suggests that the reduced expression of YTHDC1 may decrease the degradation of inflammation-related factors in macrophages, leading to a highly inflammatory state in the carotid artery wall. Furthermore, the sustained release of inflammatory factors may increase the expression of the aging-related gene RGN in vascular smooth muscle cells, further exacerbating the progression of atherosclerosis. A reduced level of m6A methylation modification could enhance inflammation and expedite cellular aging, thereby contributing to the development of carotid atherosclerosis.