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Single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal tool for exploring cellular landscapes across diverse species and tissues. Precise annotation of cell types is essential for understanding these landscapes, relying heavily on empirical knowledge and curated cell marker databases. In this study, we introduce MarkerGeneBERT, a natural language processing (NLP) system designed to extract critical information from the literature regarding species, tissues, cell types, and cell marker genes in the context of single-cell sequencing studies. Leveraging MarkerGeneBERT, we systematically parsed full-text articles from 3702 single-cell sequencing-related studies, yielding a comprehensive collection of 7901 cell markers representing 1606 cell types across 425 human tissues/subtissues, and 8223 cell markers representing 1674 cell types across 482 mouse tissues/subtissues. Comparative analysis against manually curated databases demonstrated that our approach achieved 76% completeness and 75% accuracy, while also unveiling 89 cell types and 183 marker genes absent from existing databases. Furthermore, we successfully applied the compiled brain tissue marker gene list from MarkerGeneBERT to annotate scRNA-seq data, yielding results consistent with original studies. Conclusions: Our findings underscore the efficacy of NLP-based methods in expediting and augmenting the annotation and interpretation of scRNA-seq data, providing a systematic demonstration of the transformative potential of this approach. The 27323 manual reviewed sentences for training MarkerGeneBERT and the source code are hosted at https://github.com/chengpeng1116/MarkerGeneBERT .

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BACKGROUND: Gastric cancer (GC) poses a significant global health challenge. This study is aimed at elucidating the role of the immune system, particularly T cells and their subtypes, in the pathogenesis and progression of intestinal-type gastric carcinoma (GC), and at evaluating the predictive utility of a T cell marker gene-based risk score for overall survival. METHODS: We performed an extensive analysis using single-cell RNA sequencing data to map the diversity of immune cells and identify specific T cell marker genes within GC. Pseudotime trajectory analysis was employed to observe the expression patterns of tumor-related pathways and transcription factors (TFs) at various disease stages. We developed a risk score using data from The Cancer Genome Atlas (TCGA) as a training set and validated it with the GSE15459 dataset. RESULTS: Our analysis revealed distinct patterns of T cell marker gene expression associated with different stages of GC. The risk score, based on these markers, successfully stratified patients into high-risk and low-risk groups with significantly different overall survival prospects. High-risk patients exhibited poorer survival outcomes compared to low-risk patients (p < 0.05). Additionally, the risk score was capable of identifying patients across a spectrum from chronic atrophic gastritis to early GC. CONCLUSION: The findings enhance the understanding of the tumor immune microenvironment in GC and propose new immunotherapeutic targets. The T cell marker gene-based risk score offers a potential tool for gastroenterologists to tailor treatment plans more precisely according to the cancer's severity.

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The extracellular matrix (ECM) of cancer tissues is rich in dense collagen, contributing to the stiffening of these tissues. Increased stiffness has been reported to promote cancer cell proliferation, invasion, metastasis, and prevent drug delivery. Replicating the structure and mechanical properties of cancer tissue in vitro is essential for developing cancer treatment drugs that target these properties. In this study, we recreated specific characteristics of cancer tissue, such as collagen density and high elastic modulus, using a colorectal cancer cell line as a model. Using our original material, collagen microfibers (CMFs), and a constructed three-dimensional (3D) cancer-stromal tissue model, we successfully reproduced an ECM highly similar to in vivo conditions. Furthermore, our research demonstrated that cancer stem cell markers expressed in the 3D cancer-stromal tissue model more closely mimic in vivo conditions than traditional two-dimensional cell cultures. We also found that CMFs might affect an impact on how cancer cells express these markers. Our 3D CMF-based model holds promise for enhancing our understanding of colorectal cancer and advancing therapeutic approaches. STATEMENT OF SIGNIFICANCE: Reproducing the collagen content and stiffness of cancer tissue is crucial in comprehending the properties of cancer and advancing anticancer drug development. Nonetheless, the use of collagen as a scaffold material has posed challenges due to its poor solubility, hindering the replication of a cancer microenvironment. In this study, we have successfully recreated cancer tissue-specific characteristics such as collagen density, stiffness, and the expression of cancer stem cell markers in three-dimensional (3D) colorectal cancer stromal tissue, utilizing a proprietary material known as collagen microfiber (CMF). CMF proves to be an ideal scaffold material for replicating cancer stromal tissue, and these 3D tissues constructed with CMFs hold promise in contributing to our understanding of cancer and the development of therapeutic drugs.

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The NK cell is an important component of the tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC), also plays a significant role in PDAC development. This study aimed to explore the relationship between NK cell marker genes and prognosis, immune response of PDAC patients. By scRNA-seq data, we found the proportion of NK cells were significantly downregulated in PDAC and 373 NK cell marker genes were screened out. By TCGA database, we enrolled 7 NK cell marker genes to construct the signature for predicting prognosis in PDAC patients. Cox analysis identified the signature as an independent factor for pancreatic cancer. Subsequently, the predictive power of signature was validated by 6 GEO datasets and had an excellent evaluation. Our analysis of relationship between the signature and patients' immune status revealed that the signature has a strong correlation with immunocyte infiltration, inflammatory reaction, immune checkpoint inhibitors (ICIs) response. The NK cell marker genes are closely related to the prognosis and immune capacity of PDAC patients, and they have potential value as a therapeutic target.

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Background: Topoisomerase IIα (TOP2A), is an enzyme involved in DNA replication, transcription, recombination, and chromatin remodeling and is found in a variety of cancers. However, the role of TOP2A regulation in oral cancer progression is not fully explained. We investigated the effect of TOP2A inhibition on cell survival, metabolism, and cancer stem cell self-renewal function in oral cancer cells. Methods: Oral carcinoma cell line SCC25 was cultured in complete DMEM/F12 media and treated with 5µM of Etoposide (Topoisomerase II inhibitor) for 48h. The critical parameters of cellular metabolism, including extracellular acidification rate (ECAR) and mitochondrial oxidative phosphorylation based on the oxygen consumption rate of cancer cells were assessed using Seahorse assay. Western blotting was performed to assess the proteins that are associated with proliferation (Survivin, IL-6) and cancer stem cell function (Oct4, Sox2) in cell lysates prepared from control and etoposide treated groups. Statistical analysis was performed using One-way ANOVA with Dunnett's multiple comparisons test. Results: The protein expression of TOP2A was significantly (P<0.05) inhibited by etoposide. Additionally, TOP2A inhibition decreased the mitochondrial respiratory parameters including basal respiration, maximal respiration and ATP production. However, TOP2A inhibition has no impact on glycolytic function. Moreover, the proliferative marker survivin and IL-6 showed a significant (P<0.05) decrease after TOP2A inhibition. Conversely, the protein expression of cancer stem cell markers Oct-4 and Sox 2 were not altered. Conclusion: These results indicate that inhibition of TOP2A is more efficacious by decreasing the mitochondrial metabolic reprogramming and thereby downregulating the key anti-apoptotic and pro-survival mediators. Thus, TOP2A represents an ideal therapeutic target and offers a potential treatment strategy for OSCC.

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BACKGROUND: The current understanding of the prognostic significance of B cells and their role in the tumor microenvironment (TME) in esophageal carcinoma (ESCA) is limited. METHODS: We conducted a screening for B-cell-related genes through the analysis of single-cell transcriptome data. Subsequently, we developed a B-cell-related gene signature (BRGrisk) using LASSO regression analysis. Patients from The Cancer Genome Atlas cohort were divided into a training cohort and a test cohort. Patients were categorized into high- and low-risk groups based on their median BRGrisk scores. The overall survival was assessed using the Kaplan-Meier method, and a nomogram based on BRGrisk was constructed. Immune infiltration profiles between the risk groups were also compared. RESULTS: The BRGrisk prognostic model indicated significantly worse outcomes for patients with high BRGrisk scores (p < 0.001). The BRGrisk-based nomogram exhibited good prognostic performance. Analysis of immune infiltration revealed that patients in the high-BRGrisk group had notably higher levels of immune cell infiltration and were more likely to be in an immunoresponsive state. Enrichment analysis showed a strong correlation between the prognostic gene signature and cancer-related pathways. IC50 results indicated that patients in the low-BRGrisk group were more responsive to common drugs compared to those in the high-BRGrisk group. CONCLUSIONS: This study presents a novel BRGrisk that can be used to stratify the prognosis of ESCA patients and may offer guidance for personalized treatment strategies aimed at improving prognosis.

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Aldehyde dehydrogenase 1 (ALDH1), a crucial aldehyde metabolizing enzyme, has six family members. The ALDH1 family is expressed in various tissues, with a significant presence in the liver. It plays a momentous role in several pathophysiological processes, including aldehyde detoxification, oxidative stress, and lipid peroxidation. Acetaldehyde detoxification is the fundamental function of the ALDH1 family in participating in vital pathological mechanisms. The ALDH1 family can catalyze retinal to retinoic acid (RA) that is a hormone-signaling molecule and plays a vital role in the development and adult tissues. Furthermore, there is a need for further and broader research on the role of the ALDH1 family as a signaling molecule. The ALDH1 family is widely recognized as a cancer stem cell (CSC) marker and plays a significant role in the proliferation, invasion, metastasis, prognosis, and drug resistance of cancer. The ALDH1 family also participates in other human diseases, such as neurodegenerative diseases, osteoarthritis, diabetes, and atherosclerosis. It can inhibit disease progression by inhibiting/promoting the expression/activity of the ALDH1 family. In this review, we comprehensively analyze the tissue distribution, and functions of the ALDH1 family. Additionally, we review the involvement of the ALDH1 family in diseases, focusing on the underlying pathological mechanisms and briefly talk about the current status and development of ALDH1 family inhibitors. The ALDH1 family presents new possibilities for treating diseases, with both its upstream and downstream pathways serving as promising targets for therapeutic intervention. This offers fresh perspectives for drug development in the field of disease research.

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BACKGROUND: Neoadjuvant immunotherapy has evolved as an effective option to treat non-small cell lung cancer (NSCLC). B cells play essential roles in the immune system as well as cancer progression. However, the repertoire of B cells and its association with clinical outcomes remains unclear in NSCLC patients receiving neoadjuvant immunotherapy. METHODS: Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data for LUAD samples were accessed from the TCGA and GEO databases. LUAD-related B cell marker genes were confirmed based on comprehensive analysis of scRNA-seq data. We then constructed the B cell marker gene signature (BCMGS) and validated it. In addition, we evaluated the association of BCGMS with tumor immune microenvironment (TIME) characteristics. Furthermore, we validated the efficacy of BCGMS in a cohort of NSCLC patients receiving neoadjuvant immunotherapy. RESULTS: A BCMGS was constructed based on the TCGA cohort and further validated in three independent GSE cohorts. In addition, the BCMGS was proven to be significantly associated with TIME characteristics. Moreover, a relatively higher risk score indicated poor clinical outcomes and a worse immune response among NSCLC patients receiving neoadjuvant immunotherapy. CONCLUSIONS: We constructed an 18-gene prognostic signature derived from B cell marker genes based on scRNA-seq data, which had the potential to predict the prognosis and immune response of NSCLC patients receiving neoadjuvant immunotherapy.

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Flow cytometry-based immunophenotyping is a mainstay of diagnostics in acute myeloid leukaemia (AML). Aberrant CD56 and T-cell antigen expression is observed in a fraction subset of AML cases, but the clinical relevance remains incompletely understood. Here, we retrospectively investigated the association of CD56 and T-cell marker expression with disease-specific characteristics and outcome of 324 AML patients who received intensive induction therapy at our centre between 2011 and 2019. We found that CD2 expression was associated with abnormal non-complex karyotype, NPM1 wild-type status and TP53 mutation. CD2 also correlated with a lower complete remission (CR) rate (47.8% vs. 71.6%, p = 0.03). CyTdT and CD2 were associated with inferior 3-year event-free-survival (EFS) (5.3% vs. 33.5%, p = 0.003 and 17.4% vs. 33.1%, p = 0.02, respectively). CyTdT expression was also correlated with inferior relapse-free survival (27.3% vs. 48.8%, p = 0.04). In multivariable analyses CD2 positivity was an independent adverse factor for EFS (HR 1.72, p = 0.03). These results indicate a biological relevance of aberrant T-cell marker expression in AML and provide a rationale to further characterise the molecular origin in T-lineage-associated AML.

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Objective @#To investigate the role of lncSIL in transforming growth factor-β1(TGF-β1)-induced alveo- lar epithelial interstitial transformation (EMT) and its related signaling pathways .@*Methods @#Western blot was used to detect the effect of lncSIL silencing on the expression of E-cadherin ( E-cad) , alpha-smooth muscle actin ( α- SMA) and Collagen I (Col I) in the process of EMT induced by TGF-β1 . LncSIL interacting proteins were ana- lyzed by RNA pulldown . Western blot was used to detect the effect of overexpression or silencing of lncSIL on the expression of its target gene enhancer of zeste homolog 2 (EZH2) and its downstream factors P21 and cyclin-de- pendent kinase 6 (CDK6) . Flow cytometry was used to analyze the effect of lncSIL on cell cycle progression .@*Results@#After lncSIL silencing , the expression of α-SMA and Col I increased , the expression of E-cad decreased . RNA pulldown assay showed that EZH2 was the target protein that interacted with lncSIL , and the expression of EZH2 increased after silencing lncSIL , the expression of EZH2 downstream gene P21 decreased , CDK6 increased . Flow cytometry showed that the number of cells in S phase significantly increased . When lncSIL was overexpressed , the expression of EZH2 and CDK6 was down-regulated , the expression of P21 was up-regulated , and the number of S phase cells significantly decreased .@*Conclusion @#LncSIL inhibits TGF-β1-induced alveolar epithelial cell mesen- chymal transition by negatively regulating EZH2/P21 /CDK6 signaling pathway to inhibit cell cycle progression .

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@#Objective To investigate the expression,prognostic value of acetyl-coa carboxylase 1(ACACA)gene in liver cancer,its correlation with immune cells,and to construct a prognostic model.Methods Integrating genotype-tissue expression(GTEx)and the cancer genome atlas(TCGA)data to analyze ACACA expression in cancers and adjacent tissues,and perform prognosis analysis.Examine the correlation between ACACA and immune cells.Use GSE156625 cell RNA seq(scRNA-seq)data to study ACACA expression in dendritic cells(DCs).Construct an hepatocellular carcinoma(HCC)prognosis model based on apolipoprotein c-Ⅰ(APOC1)and apolipoprotein c-Ⅲ(APOC3),using Kaplan-Meier survival curves and time-dependent receiver operator characteristic(ROC)curves to evaluate prognostic capability,and analyze the effect of traditional Chinese medicine components on APOC1 and APOC3 through molecular docking.Results ACACA shows significant differential expression in various cancers and is associated with the prognosis of liver cancer.High expression of ACACA reduces the content of dendritic cells.APOC1 and APOC3,the major DCs marker genes,were positively correlated with ACACA expression.Using Kaplan-Meier curves,we predicted the 1-year,3-year,and 5-year overall survival(OS)probabilities for HCC patients in the TCGA cohort,and confirmed the reliability through calibration curve analysis.Salvianolic acid B,Asiaticoside,and Neohesperidin may have potential effects on APOC1 and APOC3.Conclusion ACACA is closely related to HCC prognosis,and the prognostic model based on APOC1 and APOC3 can serve as a predictive indicator.Some traditional Chinese medicine components may hold therapeutic potential for HCC treatment.

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Objective: This study amied to investigate the prognostic characteristics of triple negative breast cancer (TNBC) patients by analyzing B cell marker genes based on single-cell and bulk RNA sequencing. Methods: Utilizing single-cell sequencing data from TNBC patients, we examined tumor-associated B cell marker genes. Transcriptomic data from The Cancer Genome Atlas (TCGA) database were used as the foundation for predictive modeling. Independent validation set was conducted using the GSE58812 dataset. Immune cell infiltration into the tumor was assessed through various, including XCELL, TIMER, QUANTISEQ, CIBERSORT, CIBERSORT-ABS, and ssGSEA. The TIDE score was utilized to predict immunotherapy outcomes. Additional investigations were conducted on the immune checkpoint blockade gene, tumor mutational load, and the GSEA enrichment analysis. Results: Our analysis encompassed 22,106 cells and 20,556 genes in cancerous tissue samples from four TNBC patients, resulting in the identification of 116 B cell marker genes. A B cell marker gene score (BCMG score) involving nine B cell marker genes (ZBP1, SEL1L3, CCND2, TNFRSF13C, HSPA6, PLPP5, CXCR4, GZMB, and CCDC50) was developed using TCGA transcriptomic data, revealing statistically significant differences in survival analysis (P<0.05). Functional analysis demonstrated that marker genes were predominantly associated with immune-related pathways. Notably, substantial differences between the higher and lower- BCMG score groups were observed in terms of immune cell infiltration, immune cell activity, tumor mutational burden, TIDE score, and the expression of immune checkpoint blockade genes. Conclusion: This study has established a robust model based on B-cell marker genes in TNBC, which holds significant potential for predicting prognosis and response to immunotherapy in TNBC patients.

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Recent studies have shown that B cells and the associated tertiary lymphoid structures (TLS) correlate with the response of patients to immune checkpoint inhibitors (ICIs) and predict overall survival (OS) in cancer patients. We screened 145 B cell marker genes (BCMG) by a comprehensive analysis of single-cell RNA-sequencing (scRNA-seq) data of head and neck squamous cell carcinoma (HNSC) from the Gene Expression Omnibus (GEO) database. The BCMG signature (BCMGS) was established using The Cancer Genome Atlas (TCGA) dataset of HNSC and verified in four independent datasets. The multivariate Cox regression analysis identified the signature as an independent prognostic factor. A prognostic nomogram was constructed with independent prognostic factors using the TCGA dataset. GO and KEGG analysis revealed the underlying signaling pathways related to this signature. Study of immune profiles showed that patients in the low-risk group presented discriminative immune-cell infiltrations. Furthermore, the low-risk group was featured by higher TCR and BCR diversity, which suggested that low-risk patients may be more sensitive to ICIs. Immunohistochemistry was performed, and we found that high expression of FTH1 was significantly correlated with poor OS (P = 0.025). The expression of TIM-3, LAG-3 and PD-1 was positively correlated and associated with better OS in HNSC. However, there was no statistically significant difference between PD-L1, PD-L2, CTLA-4, TIGIT and prognosis. The BCMGS was a promising prognostic biomarker in HNSC, which may help to interpret the responses to immunotherapy and provide a new perspective for future research on the treatment in HNSC.

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Tuberculosis (TB) remains a very significant infectious disease worldwide. New vaccines and therapies are needed, even more crucially with the increase of multi-drug resistant Mycobacterium tuberculosis strains. Preclinical animal models are very valuable for the development of these new disease control strategies. Guinea pigs are one of the best models of TB, sharing many features with the pathology observed in human TB. Here we describe the development of TB lesions in a guinea pig model of infection. We characterise the granulomatous lesions in four developmental stages (I-IV), using histopathological analysis and immunohistochemical (IHC) techniques to study macrophages, T cells, B cells and granulocytes. The granulomas in the guinea pigs start as aggregations of macrophages and few heterophils, evolving to larger lesions showing central caseous necrosis with mineralisation and abundant acid-fast bacilli, surrounded by a rim of macrophages and lymphocytes in the outer layers of the granuloma. Multinucleated giant cells are very rare and fibrotic capsules are not formed in this animal model.

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Background: Natural killer (NK) cells are characterized by their antitumor efficacy without previous sensitization, which have attracted attention in tumor immunotherapy. The heterogeneity of osteosarcoma (OS) has hindered therapeutic application of NK cell-based immunotherapy. The authors aimed to construct a novel NK cell-based signature to identify certain OS patients more responsive to immunotherapy. Materials and Methods: A total of eight publicly available datasets derived from patients with OS were enrolled in this study. Single-cell RNA sequencing data obtained from the Gene Expression Omnibus (GEO) database were analyzed to screen NK cell marker genes. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis was used to construct an NK cell-based prognostic signature in the TARGET-OS dataset. The differences in immune cell infiltration, immune system-related metagenes, and immunotherapy response were evaluated among risk subgroups. Furthermore, this prognostic signature was experimentally validated by reverse transcription-quantitative real-time PCR (RT-qPCR). Results: With differentially expressed NK cell marker genes screened out, a five-gene NK cell-based prognostic signature was constructed. The prognostic predictive accuracy of the signature was validated through internal clinical subgroups and external GEO datasets. Low-risk OS patients contained higher abundances of infiltrated immune cells, especially CD8 T cells and naive CD4 T cells, indicating that T cell exhaustion states were present in the high-risk OS patients. As indicated from correlation analysis, immune system-related metagenes displayed a negative correlation with risk scores, suggesting the existence of immunosuppressive microenvironment in OS. In addition, based on responses to immune checkpoint inhibitor therapy in two immunotherapy datasets, the signature helped predict the response of OS patients to anti-programmed cell death protein 1 (PD-1) or anti-programmed cell death ligand 1 (PD-L1) therapy. RT-qPCR results demonstrated the roughly consistent relationship of these five gene expressions with predicting outcomes. Conclusions: The NK cell-based signature is likely to be available for the survival prediction and the evaluation of immunotherapy response of OS patients, which may shed light on subsequent immunotherapy choices for OS patients. In addition, the authors revealed a potential link between immunosuppressive microenvironment and OS.

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Biomarkers for resistance in Glioblastoma multiforme (GBM) are lacking, and progress in the clinic has been slow to arrive. CD133 (prominin-1) is a membrane-bound glycoprotein on the surface of cancer stem cells (CSCs) that has been associated with poor prognosis, therapy resistance, and tumor recurrence in GBM. Due to its connection to CSCs, to which tumor resistance and recurrence have been partially attributed in GBM, there is a growing field of research revolving around the potential role of CD133 in each of these processes. However, despite encouraging results in vitro and in vivo, the biological interplay of CD133 with these components is still unclear, causing a lack of clinical application. In parallel, omic data from biospecimens that include CD133 are beginning to emerge, increasing the importance of understanding CD133 for the effective use of these highly dimensional data sets. Given the significant mechanistic overlap, prioritization of the most robust findings is necessary to optimize the transition of CD133 to clinical applications using patient-derived biospecimens. As a result, this review aims to compile and analyze the current research regarding CD133 as a functional unit in GBM, exploring its connections to prognosis, the tumor microenvironment, tumor resistance, and tumor recurrence.

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Background: Tumour tissue contains not only tumour cells but also some stromal cells and immune cells. This is one composition of the immune microenvironment of the tumour and causes a significant effect on the prognostic factors and recurrence of malignant tumor. Methods: In this research, single-cell RNA data from triple-negative breast cancers (TNBCs) were comprehensively analyzed and 1,527 marker genes expressed in immune cells were identified. Subsequently, RNA sequencing and clinical data from 360 patients in the Triple Negative Breast Cancer database at the Fudan University Shanghai Cancer Center (FUSCC) were divided into two groups in a 1:1 ratio, the training group and the validation group. An eight-gene Immune Cell-Associated Predictive Gene (ICAPG) model for predicting breast cancer (BC) recurrence was developed using mRNA data from the training group combined with immune cell marker genes. Based on this model, subjects were divided into two different risk level groups. The predictive power of the model was fully validated using the validation group and The Cancer Genome Atlas (TCGA) database. The localization and expression of these eight genes were then confirmed in a single-cell database. ssGSEA and CIBERSORT algorithms were used to characterize the differences in immune cell infiltration between the two different risk groups. Results: The eight-gene ICAPG model was proven to be effective in the validation group. The low-risk group patients presented higher criterion of infiltration of CD8+ T cells and higher levels of tumour-infiltrating lymphocytes (TILs). In addition, the relationship between predictive models and homologous recombination deficiency (HRD) was explored and it was revealed that subjects from the high-risk group tended to have higher HRD values. Conclusions: This research established a new predictive model on the basis of immune cell marker genes that might effectively predict relapse in TNBC patients.

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OBJECTIVE: This study aimed to characterize Sox-2 in sentinel lymph nodes and randomly obtained lymph nodes from endometriosis (EM) patients for the first time. STUDY DESIGN: This prospective study analyzed tissue samples from surgical specimens collected from May until December 2007 in the Endometriosis Center Charité, Berlin. Lymph node samples from 38 women aged between 22 and 49 years who underwent laparoscopy due to symptomatic EM were analyzed. The material was obtained either randomly or, in the case of deep infiltrating endometriosis, detected using 4 cc Patent Blue®, labeled intraoperatively, which made the sentinel lymph nodes available for histological examination. Together with hematoxylin and eosin staining, the sections were evaluated by immunohistochemistry with antibodies against estrogen and progesterone receptors and Sox-2. Using double-immunofluorescence microscopy, the colocalization of Sox-2 and estrogen receptors were evaluated. RESULTS: Sox-2-positive cells were identified in the lymph nodes' cortical and medullary zones, with a higher expression in the medullary layer. Occasionally, Sox-2 positive stained cell groups, called cell nests, could also be detected. The number of Sox-2 positive cells in the sentinel lymph nodes was almost three times higher than in the random lymph nodes (p = 0.031). A significant five-fold increase (p = 0.0013) in Sox-2 expression was seen in the estrogen and progesterone receptor (ER/PR) positive patient group compared to the progesterone receptor positive group or hormone receptor negative patients. Identical hormone-related Sox-2 expression was also detected separately for the sentinel lymph node group (p = 0.0174). Sox-2 showed pronounced colocalisation with estrogen receptors. CONCLUSION: The lymphatic involvement in EM is evidence of a systemic disease manifestation and provides evidence of an immune system failure. In recent years, many theories have been studied, but there is no single theory that could explain all aspects of EM. The future concept of EM is likely to incorporate the elements from all the pathogenetic theories already described. Through this study, stem cells and lymphatic metastasis theories were incorporated.

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Background: Accumulating evidence has highlighted the effects of natural killer (NK) cells on shaping anti-tumor immunity. This study aimed to construct an NK cell marker gene signature (NKMS) to predict prognosis and therapeutic response of clear cell renal cell carcinoma (ccRCC) patients. Methods: Publicly available single-cell and bulk RNA profiles with matched clinical information of ccRCC patients were collected from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), ArrayExpress, and International Cancer Genome Consortium (ICGC) databases. A novel NKMS was constructed, and its prognostic value, associated immunogenomic features and predictive capability to immune checkpoint inhibitors (ICIs) and anti-angiogenic therapies were evaluated in ccRCC patients. Results: We identified 52 NK cell marker genes by single-cell RNA-sequencing (scRNA-seq) analysis in GSE152938 and GSE159115. After least absolute shrinkage and selection operator (LASSO) and Cox regression, the most prognostic 7 genes (CLEC2B, PLAC8, CD7, SH3BGRL3, CALM1, KLRF1, and JAK1) composed NKMS using bulk transcriptome from TCGA. Survival and time-dependent receiver operating characteristic (ROC) analysis exhibited exceptional predictive capability of the signature in the training set and two independent validation cohorts (E-MTAB-1980 and RECA-EU cohorts). The seven-gene signature was able to identify patients within high Fuhrman grade (G3-G4) and American Joint Committee on Cancer (AJCC) stage (III-IV). Multivariate analysis confirmed the independent prognostic value of the signature, and a nomogram was built for clinical utility. The high-risk group was characterized by a higher tumor mutation burden (TMB) and greater infiltration of immunocytes, particularly CD8+ T cells, regulatory T (Treg) cells and follicular helper T (Tfh) cells, in parallel with higher expression of genes negatively regulating anti-tumor immunity. Moreover, high-risk tumors exhibited higher richness and diversity of T-cell receptor (TCR) repertoire. In two therapy cohorts of ccRCC patients (PMID32472114 and E-MTAB-3267), we demonstrated that high-risk group showed greater sensitivity to ICIs, whereas the low-risk group was more likely to benefit from anti-angiogenic therapy. Conclusions: We identified a novel signature that can be utilized as an independent predictive biomarker and a tool for selecting the individualized treatment for ccRCC patients.