Patterns Of Immune Cell Infiltration Research Articles

Development of a prognostic model related to homologous recombination deficiency in glioma based on multiple machine learning

BackgroundDespite advances in neuro-oncology, treatments of glioma and tools for predicting the outcome of patients remain limited. The objective of this research is to construct a prognostic model for glioma using the Homologous Recombination Deficiency (HRD) score and validate its predictive capability for glioma.MethodsWe consolidated glioma datasets from TCGA, various cancer types for pan-cancer HRD analysis, and two additional glioma RNAseq datasets from GEO and CGGA databases. HRD scores, mutation data, and other genomic indices were calculated. Using machine learning algorithms, we identified signature genes and constructed an HRD-related prognostic risk model. The model’s performance was validated across multiple cohorts. We also assessed immune infiltration and conducted molecular docking to identify potential therapeutic agents.ResultsOur analysis established a correlation between higher HRD scores and genomic instability in gliomas. The model, based on machine learning algorithms, identified seven key genes, significantly predicting patient prognosis. Moreover, the HRD score prognostic model surpassed other models in terms of prediction efficacy across different cancers. Differential immune cell infiltration patterns were observed between HRD risk groups, with potential implications for immunotherapy. Molecular docking highlighted several compounds, notably Panobinostat, as promising for high-risk patients.ConclusionsThe prognostic model based on the HRD score threshold and associated genes in glioma offers new insights into the genomic and immunological landscapes, potentially guiding therapeutic strategies. The differential immune profiles associated with HRD-risk groups could inform immunotherapeutic interventions, with our findings paving the way for personalized medicine in glioma treatment.

Exploring genetic and immune cell dynamics in systemic lupus erythematosus patients with Epstein-Barr virus infection via machine learning.

Epstein-Barr Virus (EBV) is a widespread virus implicated in various diseases, including Systemic Lupus Erythematosus (SLE). However, the specific genes and pathways altered in SLE patients with EBV infection remain unclear. We aimed to identify key genes and immune cells in SLE patients with EBV infection. The datasets of SLE (GSE50772 and GSE81622) or EBV infection (GSE85599 and GSE45918) were obtained from the Gene Expression Omnibus (GEO) database. Next, differential gene expression (DEGs) analysis were conducted to identify overlapping DEGs and then enrichment analysis was performed. Machine learning was applied to identify key genes. Validation was conducted using ROC curve analysis and expression level verification in test datasets and single-cell RNA sequencing. Immune cell infiltration patterns were analyzed using CIBERSORTx, and clinical data were reviewed for SLE patients. We identified 58 overlapping DEGs enriched in interferon-related pathways. Five overlapping DEGs (IFI27, TXK, RAPGEF6, PIK3IP1, PSENEN) were selected as key genes by machine learning algorithms, with IFI27 showing the highest diagnostic performance. The expression level of IFI27 was found higher in CD4 CTL, CD8 naïve and various B cell subsets of SLE patients with EBV infection. IFI27 showed significant correlation with B intermediate and CD4 CT. Clinical data showed lower CD4 T cell proportions in SLE patients with EBV infection. This study identifies IFI27 as a key gene for SLE patients with EBV infection, influencing CD4 CTL and B cell subtypes. These findings enhance the understanding of the molecular mechanisms linking SLE and EBV infection, providing potential targets for diagnostic and therapeutic strategies.

Novel prognostic model of complement and coagulation cascade-related genes correlates with immune environment and drug sensitivity in hepatocellular carcinoma

IntroductionHepatocellular carcinoma (HCC) is an immunogenic cancer characterized by high morbidity and mortality rates. The complement and coagulation systems are traditionally associated with the incidence of thrombotic complications and complement activation in cancer. However, the prognostic value of complement and coagulation-related factors (CCCR) in HCC remains undetermined. This study aims to construct a prognostic model based on the complement and coagulation cascades to evaluate its potential for immunotherapy and its relationship with drug sensitivity. Materials and MethodsWe comprehensively investigated the expression profiles of CCCR genes using the TCGA, ICGC, and GTEx databases. Cox proportional hazards regression models were employed to assess prognostic value. ResultsThis study presents a novel prognostic model derived from the comprehensive analysis of nine CCCR genes (C1S, C6, C7, F11, F13B, F7, SERPINE1, SERPINF2, and SERPING1) to elucidate their correlation with the tumor immune environment and drug sensitivity in patients with HCC. Our model stratified patients into high- and low-risk groups based on distinct survival outcomes. The area under the curve (AUC) values of the risk score for one-, two-, and three-year survival rates were all greater than 0.660. Additionally, we analyzed immune cell infiltration patterns, revealing a strong correlation between CCCR gene expression and the immune microenvironment, including T cell and macrophage activity. Our findings also identified potential therapeutic targets, demonstrating differential drug sensitivity profiles between the risk groups. JAK1_8709_1718 was found to be more suitable for patients with low-risk HCC. ConclusionOur findings provide promising insights into the clinical relevance of CCCR genes as prognostic markers and therapeutic targets. This study underscores the significance of CCCR in HCC and paves the way for improved therapeutic strategies.

Single-nucleus sequencing unveils heterogeneity in renal cell carcinomas microenvironment: Insights into pathogenic origins and treatment-responsive cellular subgroups

BackgroundDifferent individuals with renal cell carcinoma (RCC) exhibit substantial heterogeneity in histomorphology, genetic alterations in the proteome, immune cell infiltration patterns, and clinical behavior. ObjectivesThis study aims to use single-nucleus sequencing on ten samples (four normal, three clear cell renal cell carcinoma (ccRCC), and three chromophobe renal cell carcinoma (chRCC)) to uncover pathogenic origins and prognostic characteristics in patients with RCC. MethodsBy using two algorithms, inferCNV and k-means, the study explores malignant cells and compares them with the normal group to reveal their origins. Furthermore, we explore the pathogenic factors at the gene level through Summary-data-based Mendelian Randomization and co-localization methods. Based on the relevant malignant markers, a total of 212 machine-learning combinations were compared to develop a prognostic signature with high precision and stability. Finally, the study correlates with clinical data to investigate which cell subtypes may impact patients’ prognosis. Results & conclusionTwo main origin tumor cells were identified: Proximal tubule cell B and Intercalated cell type A, which were highly differentiated in epithelial cells, and three gene loci were determined as potential pathogenic genes. The best malignant signature among the 212 prognostic models demonstrated high predictive power in ccRCC: (AUC: 0.920 (1-year), 0.920 (3-year) and 0.930 (5-year) in the training dataset; 0.756 (1-year), 0.828 (3-year), and 0.832 (5-year) in the testing dataset. In addition, we confirmed that LYVE1+ tissue-resident macrophage and TOX+ CD8 significantly impact the prognosis of ccRCC patients, while monocytes play a crucial role in the prognosis of chRCC patients.

Identification and validation of inflammatory subtypes in intrahepatic cholangiocellular carcinoma

BackgroundInflammation plays a critical role in tumor development. Inflammatory cell infiltration and inflammatory mediator synthesis cause changes in the tumor microenvironment (TME) in several cancers, especially in intrahepatic cholangiocellular carcinoma (ICC). However, methods to ascertain the inflammatory state of patients using reliable biomarkers are still being explored.MethodWe retrieved the RNA sequencing and somatic mutation analyses results and the clinical characteristics of 244 patients with ICC from published studies. We performed consensus clustering to identify the molecular subtypes associated with inflammation. We compared the prognostic patterns, clinical characteristics, somatic mutation profiles, and immune cell infiltration patterns across inflammatory subtypes. We performed quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) to confirm gene expression. We performed logistic regression analyses to construct a nomogram predicting the inflammatory status of patients with ICC.ResultsOur results confirmed that ICC can be categorized into an inflammation-high subtype (IHS) and an inflammation-low subtype (ILS). Patients from each group had distinct prognosis, clinical characteristics, and TME composition. Patients with ICC in the IHS group showed poorer prognosis owing to the immunosuppressive microenvironment and high frequency of KRAS and TP53 mutations. Cancer-associated fibroblast (CAF)-derived COLEC11 reduced myeloid inflammatory cell infiltration and attenuated inflammatory responses. The results of qRT-PCR and IHC experiments confirmed that COLEC11 expression levels were significantly reduced in tumor tissues compared to those in paracancerous tissues. Patients with ICC in the IHS group were more likely to respond to treatment with immune checkpoint inhibitors (ICIs) owing to their higher tumor mutational burden (TMB) scores, tumor neoantigen burden (TNB) scores, neoantigen counts, and immune checkpoint expression levels. Finally, we developed a nomogram to effectively predict the inflammatory status of patients with ICC based on their clinical characteristics and inflammatory gene expression levels. We evaluated the calibration, discrimination potential, and clinical utility of the nomogram.ConclusionThe inflammatory response in IHS is primarily induced by myeloid cells. COLEC11 can reduce the infiltration level of this group of cells, and myeloid inflammatory cells may be a novel target for ICC treatment. We developed a novel nomogram that could effectively predict the inflammatory state of patients with ICC, which will be useful for guiding individualized treatment plans.

Identification and experimental validation of KMO as a critical immune-associated mitochondrial gene in unstable atherosclerotic plaque

BackgroundThe heightened risk of cardiovascular and cerebrovascular events is associated with the increased instability of atherosclerotic plaques. However, the lack of effective diagnostic biomarkers has impeded the assessment of plaque instability currently. This study was aimed to investigate and identify hub genes associated with unstable plaques through the integration of various bioinformatics tools, providing novel insights into the detection and treatment of this condition.MethodsWeighted Gene Co-expression Network Analysis (WGCNA) combined with two machine learning methods were used to identify hub genes strongly associated with plaque instability. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was utilized to assess immune cell infiltration patterns in atherosclerosis patients. Additionally, Gene Set Variation Analysis (GSVA) was conducted to investigate the potential biological functions, pathways, and mechanisms of hub genes associated with unstable plaques. To further validate the diagnostic efficiency and expression of the hub genes, immunohistochemistry (IHC), quantitative real-time polymerase chain reaction (RT-qPCR), and enzyme-linked immunosorbent assay (ELISA) were performed on collected human carotid plaque and blood samples. Immunofluorescence co-staining was also utilized to confirm the association between hub genes and immune cells, as well as their colocalization with mitochondria.ResultsThe CIBERSORT analysis demonstrated a significant decrease in the infiltration of CD8 T cells and an obvious increase in the infiltration of M0 macrophages in patients with atherosclerosis. Subsequently, two highly relevant modules (blue and green) strongly associated with atherosclerotic plaque instability were identified. Through intersection with mitochondria-related genes, 50 crucial genes were identified. Further analysis employing least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algorithms revealed six hub genes significantly associated with plaque instability. Among them, NT5DC3, ACADL, SLC25A4, ALDH1B1, and MAOB exhibited positive correlations with CD8 T cells and negative correlations with M0 macrophages, while kynurenine 3-monooxygenas (KMO) demonstrated a positive correlation with M0 macrophages and a negative correlation with CD8 T cells. IHC and RT-qPCR analyses of human carotid plaque samples, as well as ELISA analyses of blood samples, revealed significant upregulation of KMO and MAOB expression, along with decreased ALDH1B1 expression, in both stable and unstable samples compared to the control samples. However, among the three key genes mentioned above, only KMO showed a significant increase in expression in unstable plaque samples compared to stable plaque samples. Furthermore, the expression patterns of KMO in human carotid unstable plaque tissues and cultured mouse macrophage cell lines were assessed using immunofluorescence co-staining techniques. Finally, lentivirus-mediated KMO silencing was successfully transduced into the aortas of high-fat-fed ApoE-/- mice, with results indicating that KMO silencing attenuated plaque formation and promoted plaque stability in ApoE-/- mice.ConclusionsThe results suggest that KMO, a mitochondria-targeted gene associated with macrophage cells, holds promise as a valuable diagnostic biomarker for assessing the instability of atherosclerotic plaques.

Characteristics of the immune environment in prostate cancer as an adjunct to immunotherapy.

The tumor microenvironment (TME) exerts an important role in carcinogenesis and progression. Several investigations have suggested that immune cell infiltration (ICI) is of high prognostic importance for tumor progression and patient survival in many tumors, particularly prostate cancer. The pattern of immune infiltration of PCa, on the other hand, has not been thoroughly understood. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets on PCa were obtained, and several datasets were merged into one data set using the "ComBat" algorithm. The ICI profiles of PCa patients were then to be uncovered by two computer techniques. The unsupervised clustering method was utilized to identify three ICI patterns in tumor samples, and Principal Component Analysis (PCA) was conducted to estimate the ICI score. Three different clusters of three ICIs were identified in 1341 PCa samples, which also correlated with different clinical features/characteristics and biological pathways. Patients with PCa are classified into high and low subtypes based on the ICI scores extracted from immune-associated signature genes. High ICI score subtypes are associated with a worse prognosis, which may intrigue the activation of cancer-related and immune-related pathways such as pathways involving Toll-like receptors, T-cell receptors, JAK-STAT, and natural killer cells. The ICI score was linked to tumor mutation load and immune/cancer-relevant signaling pathways, which explain prostate cancer's poor prognosis. The findings of this study not only advanced our knowledge of the mechanism of immune response in the prostate tumor microenvironment but also provided a novel biomarker, that is, the ICI score, for disease prognosis and guiding precision immunotherapy.

Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation

BackgroundLiver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15–20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the transcriptome profiles of patients with recurrent HCC to identify differentially expressed genes (DEGs), the involved pathways, biological functions, and potential gene signatures of recurrent HCC post-transplant using deep machine learning (ML) methodology.Materials and methodsWe analyzed the transcriptomic profiles of primary and recurrent tumor samples from 7 pairs of patients who underwent LT. Following differential gene expression analysis, we performed pathway enrichment, gene ontology (GO) analyses and protein-protein interactions (PPIs) with top 10 hub gene networks. We also predicted the landscape of infiltrating immune cells using Cibersortx. We next develop pathway and GO term-based deep learning models leveraging primary tissue gene expression data from The Cancer Genome Atlas (TCGA) to identify gene signatures in recurrent HCC.ResultsThe PI3K/Akt signaling pathway and cytokine-mediated signaling pathway were particularly activated in HCC recurrence. The recurrent tumors exhibited upregulation of an immune-escape related gene, CD274, in the top 10 hub gene analysis. Significantly higher infiltration of monocytes and lower M1 macrophages were found in recurrent HCC tumors. Our deep learning approach identified a 20-gene signature in recurrent HCC. Amongst the 20 genes, through multiple analysis, IL6 was found to be significantly associated with HCC recurrence.ConclusionOur deep learning approach identified PI3K/Akt signaling as potentially regulating cytokine-mediated functions and the expression of immune escape genes, leading to alterations in the pattern of immune cell infiltration. In conclusion, IL6 was identified to play an important role in HCC recurrence.

Genomic Insights into the Role of TOP Gene Family in Soft-Tissue Sarcomas: Implications for Prognosis and Therapy.

This study focuses on the role of topoisomerases (TOPs) in sarcomas (SARCs), highlighting TOPs' influence on sarcoma prognosis through mRNA expression, genetic mutations, immune infiltration, and DNA methylation analysis using transcriptase sequencing and other techniques. The findings indicate that TOP gene mutations correlate with increased inflammation, immune cell infiltration, DNA repair abnormalities, and mitochondrial fusion genes alterations, all of which negatively affect sarcoma prognosis. Abnormal TOP expression may independently affect sarcoma patients' survival. Cutting-edge genomic tools such as Oncomine, gene expression profiling interactive analysis (GEPIA), and cBio Cancer Genomics Portal (cBioPortal) are utilizedto explore the TOP gene family (TOP1/1MT/2A/2B/3A/3B) in soft-tissue sarcomas (STSs). This in-depth analysis reveals a notable upregulation of TOP mRNA in STS patients arcoss various SARC subtypes, French Federation Nationale des Centres de Lutte Contre le Cancer classification (FNCLCC) grades, and specific molecular profiles correlating with poorer clinical outcomes. Furthermore, this investigation identifies distinct patterns of immune cell infiltration, genetic mutations, and somatic copy number variations linked to TOP genes that inversely affect patient survival rates. These findings underscore the diagnostic and therapeutic relevance of the TOP gene suite in STSs.

Clinicopathologic and molecular landscape of invasive mucinous adenocarcinoma of the lung.

8037 Background: Invasive mucinous adenocarcinoma (IMA), accounting for 2–10% of lung adenocarcinomas, often mimics pneumonia with multicentric opacities in multiple lobes and both lungs. Notable for its microscopic skip lesions leading to tumor spread through air spaces (STAS), IMA is still under-researched in terms of its comprehensive clinical and genetic characteristics in large, multicenter studies. Methods: Primary lung cancer cases were obtained from the Tempus database, categorized into either IMA or non-IMA, and compared by patient, clinical, biopsy, and molecular characteristics. The normalization of RNA-seq data involved computing transcripts per million (TPM), performing log2 transformations, and adjusting for assay and batch effects. Significantly up- and down-regulated genes were defined as false discovery rate q-values < 0.05 and |log2(fold change)| > 0.5. Results: The study analyzed 18,857 cases, with 655 (3.5%) in the IMA group and 18,202 (96.5%) in the non-IMA group. The IMA group predominantly consisted of older individuals (median age 70 vs. 68; p<0.001), had fewer smokers (63% vs. 73%; p<0.001), and included more individuals of Hispanic or Latino ethnicity (4.3% vs. 2.7%; p=0.007). This group was more likely to be diagnosed with early-stage cancer and less likely to have stage 4 cancer at diagnosis (stage 1: 23% vs. 14%, stage 2: 18% vs. 7.3%, stage 3: 23% vs. 17%, stage 4: 36% vs. 62%; p<0.001). They exhibited significantly lower tumor mutational and neoantigen burdens (median 3.1 vs. 4.6 mutations/Mb and 6 vs. 9 neoantigens/Mb, respectively; both p<0.001), fewer positive PD-L1 statuses (27% vs. 58%; p<0.001), and different immune cell infiltration patterns (Table). Gene expression analysis revealed 416 upregulated and 258 downregulated genes in the IMA group compared to the non-IMA group. Conclusions: This analysis underscores the unique clinicopathologic and molecular characteristics of IMAs. An improved understanding of IMA’s biological behavior could lead to the development of more effective, personalized treatment strategies. [Table: see text]

Characterization of DNA damage repair (DDR) alterations and the tumor immune microenvironment (TIME) in advanced non-small cell lung cancer (NSCLC).

8603 Background: Exploiting the DDR pathway with synthetic lethality agents such as PARP inhibitors has been clinically validated in several cancers. In NSCLC, novel agents leveraging defects in the DDR pathway are being studied alone and in combination with checkpoint inhibitors. To expand on combination approaches and inform biomarker selected strategies, we analyzed DDR alteration prevalence and impact on the TIME in metastatic NSCLC. Methods: We retrospectively analyzed de-identified next-generation sequencing data from patients (pts) with metastatic NSCLC in the Tempus Database. Tumors were sequenced with the Tempus xT DNA and xR RNA assays. DDR pathway genes were defined as previously described (PMID 29617664). Pts were stratified based on the presence (DDRmt) or absence (DDRwt) of a pathogenic somatic alteration or copy number deletion in a DDR pathway gene. The associations of DDR alteration with immune cell infiltration predicted from gene expression patterns, PD-L1 from IHC, TMB (TMB-H, >10 mut/Mb), and microsatellite instability (MSI) were examined. Wilcoxon rank-sum and Pearson’s Chi-squared/Fisher's exact tests were used to assess statistical significance (p<0.05, q<0.05 for false discovery rate correction for multiple testing). Results: Across 14,127 samples, 5,255 (37%) were DDRmt and 8,872 (63%) were DDRwt. The median age at diagnosis was 67 (IQR 60-74). Current/former smokers (83%; n=10,445) had a higher prevalence of DDRmt (86% vs. 82%; p<0.001). The most prevalent DDRmt was SMARCA4 (18%), followed by PTEN (13%), BRCA2 (11%), ATM (9.0%), ERCC3 (6.6%), PMS2 (6.0%), and MUTYH (5.1%). TMB-H was more common (18% [n=931] vs. 11% [n=1001]; p<0.001) and median TMB was higher (5.4 vs. 4.6; p<0.001) in DDRmt pts compared to DDRwt. MSI-H was more prevalent in the DDRmt group (1.1% [n=57] vs. <0.1% [n=7]; p<0.001), while PD-L1 positivity was similar in DDRmt and DDRwt pts (55% [n=1918] vs. 58% [n=3314]; p=0.071). Compared to DDRwt, pts with DDRmt exhibited modest changes in immune cell infiltration patterns (Table). Conclusions: In this large, real-world cohort of pts with metastatic NSCLC, DDRmt was associated with distinct TIME patterns including high TMB, MSI-H, and modest changes in immune cell infiltrates. These findings warrant further studies to understand how DDR pathway alterations mediate changes in the TIME to inform novel combinatorial immunotherapeutic approaches in NSCLC.[Table: see text]

Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

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.