The causal effect of air pollution on the risk of essential hypertension: a Mendelian randomization study.

Background: Air pollution poses a major threat to human health by causing various illnesses, such as cardiovascular diseases. While plenty of research indicates a correlation between air pollution and hypertension, a definitive answer has yet to be found. Methods: Our analyses were performed using the Genome-wide association study (GWAS) of exposure to air pollutants from UKB (PM2.5, PM10, NO2, and NOX; n = 423,796 to 456,380), essential hypertension from FinnGen (42,857 cases and 162,837 controls) and from UKB (54,358 cases and 408,652 controls) as a validated cohort. Univariable and multivariable Mendelian randomization (MR) were conducted to investigate the causal relationship between air pollutants and essential hypertension. Body mass index (BMI), alcohol intake frequency, and the number of cigarettes previously smoked daily were included in multivariable MRs (MVMRs) as potential mediators/confounders. Results: Our findings suggested that higher levels of both PM2.5 (OR [95%CI] per 1 SD increase in predicted exposure = 1.24 [1.02-1.53], p = 3.46E-02 from Finn; OR [95%CI] = 1.04 [1.02-1.06], p = 7.58E-05 from UKB) and PM10 (OR [95%CI] = 1.24 [1.02-1.53], p = 3.46E-02 from Finn; OR [95%CI] = 1.04 [1.02-1.06], p = 7.58E-05 from UKB) were linked to an increased risk for essential hypertension. Even though we used MVMR to adjust for the impacts of smoking and drinking on the relationship between PM2.5 exposure and essential hypertension risks, our findings suggested that although there was a direct positive connection between them, it is not present after adjusting BMI (OR [95%CI] = 1.05 [0.87-1.27], p = 6.17E-01). Based on the study, higher exposure to PM2.5 and PM10 increases the chances of developing essential hypertension, and this influence could occur through mediation by BMI. Conclusion: Exposure to both PM2.5 and PM10 is thought to have a causal relationship with essential hypertension. Those impacted by substantial levels of air pollution require more significant consideration for their cardiovascular health.

Comprehensive molecular classification predicted microenvironment profiles and therapy response for hepatocellular carcinoma.

BACKGROUND AND AIMS: Tumor microenvironment (TME) heterogeneity leads to a discrepancy in survival prognosis and clinical treatment response for hepatocellular carcinoma (HCC) patients. The clinical applications of documented molecular subtypes are constrained by several issues. APPROACH AND RESULTS: We integrated three single-cell datasets to describe the TME landscape and identified six prognosis-related cell subclusters. Unsupervised clustering of subcluster-specific markers was performed to generate transcriptomic subtypes. The predictive value of these molecular subtypes for prognosis and treatment response was explored in multiple external HCC cohorts and the Xiangya HCC cohort. TME features were estimated using single-cell immune repertoire sequencing, mass cytometry and multiplex immunofluorescence. The prognosis-related score (PRS) was constructed based on machine learning algorithm. Comprehensive single-cell analysis described TME heterogeneity in HCC. The five transcriptomic subtypes possessed different clinical prognoses, stemness characteristics, immune landscapes and therapeutic responses. Class 1 exhibited an inflamed phenotype with better clinical outcomes, while Classes 2 and 4 were characterized by a lack of T cell infiltration. Classes 5 and 3 indicated an inhibitory tumor immune microenvironment. Analysis of multiple therapeutic cohorts suggested that Classes 5 and 3 were sensitive to ICB and targeted therapy, whereas Classes 1 and 2 were more responsive to transcatheter arterial chemoembolization treatment. Class 4 displayed resistant to all conventional HCC therapies. Three potential therapeutic agents and four targets were further identified for high-PRS HCC patients. CONCLUSION: Our study generated a clinically valid molecular classification to guide precision medicine in patients with HCC.

The tertiary lymphoid structure-related signature identified PTGDS in regulating PD-L1 and promoting the proliferation and migration of glioblastoma.

Background: Tertiary lymphoid structure (TLS) is a unique organ that carries out tumor cell elimination at tumor sites. It is continuously stimulated by inflammatory tumor signals and has been found to augment immunotherapy response. However, the detailed mechanisms behind it still need to be defined. Methods: To explore and grasp the whole picture of TLS from a pan-cancer view, we collected nine TLS-related genes from previous studies. We performed a comprehensive analysis of 9637 samples across 33 tumor types accessed from The Cancer Genome Atlas (TCGA) database. EdU, Transwell, and flow cytometry were performed on the feature gene PTGDS in U251 cells. The regulatory role of PTGDS on PD-L1 expression and macrophage polarization was verified. Results: Alteration analysis showed that mutations of TLS-related genes were widespread and relatively high. Clustering analysis based on the expression of these nine genes obtained two distinct clusters, with high EIF1AY and PTGDS in cluster 2 and better overall survival in cluster 1. To distinguish the two clusters, we utilized six machine learning algorithms and filtrated EIF1AY, PTGDS, SKAP1, and RBP5 as the characteristic genes, among which the former two genes were proved to be hazardous. PTGDS was found to regulate PD-L1 expression and also promoted the proliferation and migration of U251 cells. The knockdown of PTGDS could reduce the migration of macrophages and inhibit the polarization of macrophages into M2-phenotype. In addition, we established a TLS score to demonstrate patients' TLS activity. The low TLS-score group overlapped with cluster 1 and displayed a better prognosis. Besides, the low TLS-score group was related to better immunotherapy responses. The HE staining of histopathological sections confirmed that the low TLS-score group exhibited higher infiltration of immune cells. Conclusion: This study reveals broad molecular, tumorigenic, and immunogenic signatures for further functional and therapeutic studies of tertiary lymphoid structure. The TLS score we established effectively predicted immunotherapy response and patients' survival. Its future application and combination await more research.

Define cancer-associated fibroblasts (CAFs) in the tumor microenvironment: new opportunities in cancer immunotherapy and advances in clinical trials.

Despite centuries since the discovery and study of cancer, cancer is still a lethal and intractable health issue worldwide. Cancer-associated fibroblasts (CAFs) have gained much attention as a pivotal component of the tumor microenvironment. The versatility and sophisticated mechanisms of CAFs in facilitating cancer progression have been elucidated extensively, including promoting cancer angiogenesis and metastasis, inducing drug resistance, reshaping the extracellular matrix, and developing an immunosuppressive microenvironment. Owing to their robust tumor-promoting function, CAFs are considered a promising target for oncotherapy. However, CAFs are a highly heterogeneous group of cells. Some subpopulations exert an inhibitory role in tumor growth, which implies that CAF-targeting approaches must be more precise and individualized. This review comprehensively summarize the origin, phenotypical, and functional heterogeneity of CAFs. More importantly, we underscore advances in strategies and clinical trials to target CAF in various cancers, and we also summarize progressions of CAF in cancer immunotherapy.

A cancer-associated fibroblast subtypes-based signature enables the evaluation of immunotherapy response and prognosis in bladder cancer.

Bladder cancer (BLCA) is one of the most prevalent and heterogeneous urinary malignant tumors. Previous researches have reported a significant association between cancer-associated fibroblasts (CAFs) and poor prognosis of tumor patients. However, uncertainty surrounds the role of CAFs in the BLCA tumor microenvironment, necessitating further investigation into the CAFs-related gene signatures in BLCA. In this study, we identified three CAF subtypes in BLCA according to single-cell RNA-seq data and constructed CAFs-related risk score (CRRS) by screening 102,714 signatures. The survival analysis, ROC curves, and nomogram suggested that CRRS was a valuable predictor in 2,042 patients from 9 available public datasets and Xiangya real-world cohort. We further revealed the significant correlation between CRRS and clinicopathological characteristics, genome alterations, and epithelial-mesenchymal transition (EMT). A high CRRS indicated a non-inflamed phenotype and a lower remission rate of immunotherapy in BLCA. In conclusion, the CRRS had the potential to predict the prognosis and immunotherapy response of BLCA patients.

ROS-scavenging nanomedicine for "multiple crosstalk" modulation in non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is a chronic inflammatory disease characterized by "multiple crosstalk" paths of insulin resistance, lipid metabolism, oxidative stress, etc. The combination of LY294002 and oridonin was proposed as a promising therapy by targeting insulin-PI3K/AKT signaling and NF-κB inflammatory pathway. However, due to oxidative stress, disease-associated upregulation of murine CYP3A11 activity can contribute to unexpected drug metabolism and disposition, which could seriously hinder the efficacy of LY294002 and oridonin. Nanotechnology-based effective reactive oxygen species (ROS) scavengers have emerged as a promising strategy to overcome these limitations. In this study, a rationally designed ROS-responsive liposome loaded with oridonin and LY294002 (RLLs) was developed for effective liver deposition and metabolic regulation for NAFLD treatment. First, we ascertained that the insulin resistance-induced signaling response was balanced by the combination of LY294002 and oridonin. Then, the particle size, zeta potential, ROS-responsive release behavior, and ROS-scavenging properties were investigated. Moreover, the CCl4-exposed mouse model was used to assess the biodistribution and therapeutic efficacy of RLLs on NAFLD. Finally, the "multiple crosstalk" modulation of RLLs was explored by western blotting, enzyme-linked immunosorbent assay, and polymerase chain reaction analysis. RLLs exhibited antioxidant efficacy for the safe delivery of LY294002 and oridonin with good stability and biocompatibility. The biodistribution analysis proved that the accumulation of RLLs was significantly increased due to CD44-mediated targeting. In addition, RLLs had comprehensive protective effects on the CCl4-exposed mouse model, leading to significant elimination of excessive ROS, maintenance of insulin sensitivity and CYP450 activity, and reduction of inflammatory response. In the CCl4-induced mice, RLL intervention remarkably improved metabolic profiles and reduced the fibrosis lesions. Our results provided a strategy for the amelioration of oxidative stress and CYP450 activity using ROS-responsive thioketal as an antioxidant adjuvant facilitated by nanotechnology.

Patient-derived xenograft models in cancer therapy: technologies and applications.

Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.

RUNX1/CD44 axis regulates the proliferation, migration, and immunotherapy of gliomas: A single-cell sequencing analysis.

Background: Glioma is one of the most common, primary, and lethal adult brain tumors because of its extreme aggressiveness and poor prognosis. Several recent studies relevant to the immune function of CD44, a transmembrane glycoprotein as a significant hyaluronic acid receptor, have achieved great success, revealing the critical role of CD44 in immune infiltration in gliomas. The overexpression of CD44 has been verified to correlate with cancer aggressiveness and migration, while the clinical and immune features of CD44 expression have not yet been thoroughly characterized in gliomas. Methods: Molecular and clinical data of glioma collected from publicly available genomic databases were analyzed. Results: CD44 was up-expressed in malignant gliomas, notably in the 1p/19q non-codeletion cases, isocitrate dehydrogenase (IDH) wild-type, and mesenchymal subtypes in GBM samples. CD44 expression level strongly correlates with stromal and immune cells, mainly infiltrating the glioma microenvironment by single-cell sequencing analysis. Meanwhile, CD44 can be a promising biomarker in predicting immunotherapy responses and mediating the expression of PD-L1. Finally, RUNX1/CD44 axis could promote the proliferation and migration of gliomas. Conclusions: Therefore, CD44 was responsible for glioma growth and progression. It could potentially lead to a novel target for glioma immunotherapy or a prognostic biomarker.

An artificial intelligence network-guided signature for predicting outcome and immunotherapy response in lung adenocarcinoma patients based on 26 machine learning algorithms.

The immune cells play an increasingly vital role in influencing the proliferation, progression, and metastasis of lung adenocarcinoma (LUAD) cells. However, the potential of immune cells' specific genes-based model remains largely unknown. In the current study, by analysing single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing data, the tumour-infiltrating immune cell (TIIC) associated signature was developed based on a total of 26 machine learning (ML) algorithms. As a result, the TIIC signature score could predict survival outcomes of LUAD patients across five independent datasets. The TIIC signature score showed superior performance to 168 previously established signatures in LUAD. Moreover, the TIIC signature score developed by the immunofluorescence staining of the tissue array of LUAD patients showed a prognostic value. Our research revealed a solid connection between TIIC signature score and tumour immunity as well as metabolism. Additionally, it has been discovered that the TIIC signature score can forecast genomic change, chemotherapeutic drug susceptibility, and-most significantly-immunotherapeutic response. As a newly demonstrated biomarker, the TIIC signature score facilitated the selection of the LUAD population who would benefit from future clinical stratification.

Identify the immune characteristics and immunotherapy value of CD93 in the pan-cancer based on the public data sets.

CD93 is a transmembrane receptor that is mainly expressed on endothelial cells. A recent study found that upregulated CD93 in tumor vessels is essential for tumor angiogenesis in several cancers. However, the underlying mechanisms are largely unexplored. Our present research systematically analyzed the characteristics of CD93 in tumor immunotherapy among 33 cancers. CD93 levels and co-expression of CD93 on cancer and stromal cells were detected using public databases and multiple immunofluorescence staining. The Kaplan-Meier (KM) analysis identified the predictive role of CD93 in these cancer types. The survival differences between CD93 mutants and WT, CNV groups, and methylation were also investigated. The immune landscape of CD93 in the tumor microenvironment was analyzed using the SangerBox, TIMER 2.0, and single-cell sequencing. The immunotherapy value of CD93 was predicted through public databases. CD93 mRNA and protein levels differed significantly between cancer samples and adjacent control tissues in multiply cancer types. CD93 mRNA expression associated with patient prognosis in many cancers. The correlation of CD93 levels with mutational status of other gene in these cancers was also analyzed. CD93 levels significantly positively related to three scores (immune, stromal, and extimate), immune infiltrates, immune checkpoints, and neoantigen expression.. Additionally, single-cell sequencing revealed that CD93 is predominantly co-expressed on tumor and stromal cells, such as endothelial cells, cancer-associated fibroblasts (CAFs), neutrophils, T cells, macrophages, M1 and M2 macrophages. Several immune-related signaling pathways were enriched based on CD93 expression, including immune cells activation and migration, focal adhesion, leukocyte transendothelial migration, oxidative phosphorylation, and complement. Multiple immunofluorescence staining displayed the relationship between CD93 expression and CD8, CD68, and CD163 in these cancers. Finally, the treatment response of CD93 in many immunotherapy cohorts and sensitive small molecules was predicted from the public datasets. CD93 expression is closely associated with clinical prognosis and immune infiltrates in a variety of tumors. Targeting CD93-related signaling pathways in the tumor microenvironment may be a novel therapeutic strategy for tumor immunotherapy.

Identification of CD73 as a Novel Biomarker Encompassing the Tumor Microenvironment, Prognosis, and Therapeutic Responses in Various Cancers.

CD73 is essential in promoting tumor growth by prohibiting anti-tumor immunity in many cancer types. While the mechanism remains largely unknown, our paper comprehensively confirmed the onco-immunological characteristics of CD73 in the tumor microenvironment (TME) of pan-cancer. This paper explored the expression pattern, mutational profile, prognostic value, tumor immune infiltration, and response to immunotherapy of CD73 in a continuous cohort of cancers through various computational tools. The co-expression of CD73 on cancer cells, immune cells, and stromal cells in the TME was also detected. Especially, we examined the correlation between CD73 and CD8+ (a marker of T cell), CD68+ (a marker of macrophage), and CD163+ (a marker of M2 macrophage) cells using multiplex immunofluorescence staining of tissue microarrays. CD73 expression is significantly associated with a patient's prognosis and could be a promising predictor of these cancers. High CD73 levels are strongly linked to immune infiltrations, neoantigens, and immune checkpoint expression in the TME. In particular, enrichment signaling pathway analysis demonstrated that CD73 was obviously related to activation pathways of immune cells, including T cells, macrophages, and cancer-associated fibroblasts (CAFs). Meanwhile, single-cell sequencing algorithms found that CD73 is predominantly co-expressed on cancer cells, CAFs, M2 macrophages, and T cells in several cancers. In addition, we explored the cellular communication among 14 cell types in glioblastoma (GBM) based on CD73 expression. Based on the expression of CD73 as well as macrophage and T cell markers, we predicted the methylation and enrichment pathways of these markers in pan-cancer. Furthermore, a lot of therapeutic molecules sensitive to these markers were predicted. Finally, potential anticancer inhibitors, immunotherapies, and gene therapy responses targeting CD73 were identified from a series of immunotherapy cohorts. CD73 is closely linked to clinical prognosis and immune infiltration in many cancers. Targeting CD73-dependent signaling pathways may be a promising therapeutic strategy for future tumor immunotherapy.

Molecular insight into pentraxin-3: Update advances in innate immunity, inflammation, tissue remodeling, diseases, and drug role.

Pentraxin-3 (PTX3) is the prototype of the long pentraxin subfamily, an acute-phase protein consisting of a C-terminal pentraxin domain and a unique N-terminal domain. PTX3 was initially isolated from human umbilical vein endothelial cells and human FS-4 fibroblasts. It was subsequently found to be also produced by synoviocytes, chondrocytes, osteoblasts, smooth muscle cells, myeloid dendritic cells, epithelial cells, and tumor cells. Various modulatory factors, such as miRNAs, cytokines, drugs, and hypoxic conditions, could regulate the expression level of PTX3. PTX3 is essential in regulating innate immunity, inflammation, angiogenesis, and tissue remodeling. Besides, PTX3 may play dual (pro-tumor and anti-tumor) roles in oncogenesis. PTX3 is involved in the occurrence and development of many non-cancerous diseases, including COVID-19, and might be a potential biomarker indicating the prognosis, activity,and severity of diseases. In this review, we summarize and discuss the potential roles of PTX3 in the oncogenesis and pathogenesis of non-cancerous diseases and potential targeted therapies based on PTX3.

Machine learning-based tumor-infiltrating immune cell-associated lncRNAs for predicting prognosis and immunotherapy response in patients with glioblastoma.

Long noncoding ribonucleic acids (RNAs; lncRNAs) have been associated with cancer immunity regulation. However, the roles of immune cell-specific lncRNAs in glioblastoma (GBM) remain largely unknown. In this study, a novel computational framework was constructed to screen the tumor-infiltrating immune cell-associated lncRNAs (TIIClnc) for developing TIIClnc signature by integratively analyzing the transcriptome data of purified immune cells, GBM cell lines and bulk GBM tissues using six machine learning algorithms. As a result, TIIClnc signature could distinguish survival outcomes of GBM patients across four independent datasets, including the Xiangya in-house dataset, and more importantly, showed superior performance than 95 previously established signatures in gliomas. TIIClnc signature was revealed to be an indicator of the infiltration level of immune cells and predicted the response outcomes of immunotherapy. The positive correlation between TIIClnc signature and CD8, PD-1 and PD-L1 was verified in the Xiangya in-house dataset. As a newly demonstrated predictive biomarker, the TIIClnc signature enabled a more precise selection of the GBM population who would benefit from immunotherapy and should be validated and applied in the near future.

Large-scale bulk RNA-seq analysis defines immune evasion mechanism related to mast cell in gliomas.

Accumulating evidence has demonstrated that the immune cells have an emerging role in controlling anti-tumor immune responses and tumor progression. The comprehensive role of mast cell in glioma has not been illustrated yet. In this study, 1,991 diffuse glioma samples were collected from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). xCell algorithm was employed to define the mast cell-related genes. Based on mast cell-related genes, gliomas were divided into two clusters with distinct clinical and immunological characteristics. The survival probability of cluster 1 was significantly lower than that of cluster 2 in the TCGA dataset, three CGGA datasets, and the Xiangya cohort. Meanwhile, the hypoxic and metabolic pathways were active in cluster 1, which were beneficial to the proliferation of tumor cells. A potent prognostic model based on mast cell was constructed. Via machine learning, DRG2 was screened out as a characteristic gene, which was demonstrated to predict treatment response and predict survival outcome in the Xiangya cohort. In conclusion, mast cells could be used as a potential effective prognostic factor for gliomas.

Prognostic stratification based on m5C regulators acts as a novel biomarker for immunotherapy in hepatocellular carcinoma.

Background: Immunotherapy is a promising anti-cancer strategy in hepatocellular carcinoma (HCC). However, a limited number of patients can benefit from it. There are currently no reliable biomarkers available to find the potential beneficiaries. Methylcytosine (m5C) is crucial in HCC, but its role in forecasting clinical responses to immunotherapy has not been fully clarified. Methods: In this study, we analyzed 371 HCC patients from The Cancer Genome Atlas (TCGA) database and investigated the expression of 18 m5C regulators. We selected 6 differentially expressed genes (DEGs) to construct a prognostic risk model as well as 2 m5C-related diagnostic models. Results: The 1-, 3-, and 5-year area under the curve (AUC) of m5C scores for the overall survival (OS) was 0.781/0.762/0.711, indicating the m5C score system had an ideal distinction of prognostic prediction for HCC. The survival analysis showed that patients with high-risk scores present a worse prognosis than the patients with low-risk scores (p< 0.0001). We got consistent results in 6 public cohorts and validated them in Xiangya real-world cohort by quantitative real-time PCR and immunohistochemical (IHC) assays. The high-m5C score group was predicted to be in an immune evasion state and showed low sensitivity to immunotherapy, but high sensitivity to chemotherapy and potential targeted drugs and agents, such as sepantronium bromide (YM-155), axitinib, vinblastine and docetaxel. Meanwhile, we also constructed two diagnostic models to distinguish HCC tumors from normal liver tissues or liver cirrhosis. Conclusion: In conclusion, our study helps to early screen HCC patients and select patients who can benefit from immunotherapy. Step forwardly, for the less likely beneficiaries, this study provides them with new potential targeted drugs and agents for choice to improve their prognosis.

Hyaluronic acids mediate the infiltration, migration, and M2 polarization of macrophages: evaluating metabolic molecular phenotypes in gliomas.

Gliomas cause high mortality around the world. The metabolic pattern of the tumor was previously suggested to be associated with the patient's survival outcome and immune activity. Yet, this relationship in glioma remains unknown. This study systematically evaluated the immune landscape in different phenotypes classified by metabolic-related pathways of 3068 glioma samples and 33 glioblastoma single-cell sequencing samples. Machine learning prediction analysis of microarray with R (pamr) was used for validating clustering results. A total of 5842 pan-cancer samples were used for external validation of the metabolic clusters. Cell Counting Kit-8 (CCK8) assay, cell clone assay, EdU assay, wound healing assay, Transwell assay, and co-culture assay were performed to verify the distinction in molecular characteristics among metabolic clusters. Metabolomics and RNA sequencing were performed on HS683 and U251 cells to annotate potential hyaluronic acid (HA)-mediated pathways. Three distinct metabolic phenotypes were identified. Metabolic cluster 1 correlated with a high number of immune infiltrating cells and poor survival of glioma patients. Metabolic clusters were proved with different levels of the macrophage markers CD68 and CD163 by multiplex immunofluorescence staining. Glioma cells from other metabolic clusters also expressed various levels of HA. HA was further found to mediate glioma proliferation, progression, and invasion. Moreover, HA potentially promoted macrophage recruitment and M2 polarization through the IL-1/CHI3L1 and TGF-b/CHI3L1 axes. HA also regulated the expression of PD-L1. This work revealed the significant connection between metabolic patterns, especially HA, and tumor immune infiltration in gliomas.

Pericyte mediates the infiltration, migration, and polarization of macrophages by CD163/MCAM axis in glioblastoma.

Microenvironment cells (MCs) play a critical role in tumor proliferation, progression, and metastasis. However, it has not been adequately addressed whether MCs could be used as a reliable prognostic marker in glioblastoma (GBM). In the current study, the cell pair (CP) score was constructed in 1137 GBM samples based on the cell pair algorithm and Gaussian finite mixture model (GMM) and was verified in 73 GBM samples from the Xiangya cohort. CP score predicted GBM patients' survival and response to anti-PD-1 treatment with high sensitivity. Macrophage markers CD68 and CD163 were co-expressed with pericyte markers MCAM and MG2. Pericyte could mediate the infiltration, migration, and M2 type polarization of macrophages by MCAM. The CP score was a valuable tool for predicting survival outcomes and guiding immunotherapy for GBM patients. Cell pair pericyte/macrophage and gene pair CD163/MCAM were biologically significant in the tumor microenvironment of GBM.

Immune checkpoint modulators in cancer immunotherapy: recent advances and emerging concepts.

The discovery of immune checkpoint inhibitors (ICIs) has now been universally acknowledged as a significant breakthrough in tumor therapy after the targeted treatment of checkpoint molecules: anti-programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) and anti-cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) on several cancer types achieved satisfying results. However, there are still quite a lot of patients suffering from severe side effects and ineffective treatment outcomes. Although the current ICI therapy is far from satisfying, a series of novel immune checkpoint molecules with remarkable preclinical and clinical benefits are being widely investigated, like the V-domain Ig suppressor of T cell activation (VISTA), which can also be called PD-1 homolog (PD-1H), and ectonucleotidases: CD39, CD73, and CD38, which belong to the ribosyl cyclase family, etc. In this review, we systematically summarized and discussed these molecules' biological structures, molecular features, and the corresponding targeted drugs, aiming to help the in-depth understanding of immune checkpoint molecules and promote the clinical practice of ICI therapy.

A Pan-Cancer Analysis Reveals CLEC5A as a Biomarker for Cancer Immunity and Prognosis.

Background: CLEC5A is a member of the C-type lectin superfamily. It can activate macrophages and lead to a series of immune-inflammation reactions. Previous studies reveal the role of CLEC5A in infection and inflammation diseases. Method: We acquire and analyze data from The Cancer Genome Atlas (TCGA) database, Genotype-Tissue Expression (GTEx) database, and other comprehensive databases via GSCALite, cBioPortal, and TIMER 2.0 platforms or software. Single-cell sequencing analysis was performed for quantifying the tumor microenvironment of several types of cancers. Results: CLEC5A is differentially expressed in a few cancer types, of which overexpression accompanies low overall survival of patients. DNA methylation mainly negatively correlates with CLEC5A expression. Moreover, CLEC5A is positively related to immune infiltration, including macrophages, cancer-associated fibroblasts (CAFs), and regulatory T cells (Tregs). Immune checkpoint genes are significantly associated with CLEC5A expression in diverse cancers. In addition, CLEC5A expression correlates with mismatch repair (MMR) in several cancers. Tumor mutation burden (TMB), microsatellite instability (MSI), and neoantigens show a positive association with CLEC5A expression in several cancers. Furthermore, CLEC5A in cancer correlates with signal transduction, the immune system, EMT, and apoptosis process. The drug sensitivity analysis screens out potential therapeutic agents associated with CLEC5A expression, including FR-180204, Tivozanib, OSI-930, Linifanib, AC220, VNLG/124, Bexarotene, omacetaxine mepesuccinate, narciclasine, leptomycin B, PHA-793887, LRRK2-IN-1, and CR-1-31B. Conclusion: CLEC5A overexpresses in multiple cancers in contrast to normal tissues, and high CLEC5A expression predicts poor prognosis of patients and immune infiltration. CLEC5A is a potential prognostic biomarker of diverse cancers and a target for anti-tumor therapy.

Pan-Cancer Analysis of the Immunological Role of PDIA5: A Potential Target for Immunotherapy.

The aberrant protein disulfide isomerase A5 (PDIA5) expression was relevant to the poor prognosis of patients with human cancers. However, its relationship with the epigenetic and genetic alterations and its effect on tumor immunity is still lacking. In the present study, we comprehensively analyzed the immune infiltration role of PDIA5 in human cancers based on large-scale bioinformatics analyses and in vitro experiments. Obvious DNA methylation and moderate alteration frequency of PDIA5 were observed in human cancers. The expression level of PDIA5 was significantly correlated with infiltrated immune cells, immune pathways, and other immune signatures. We found that cancer cells and macrophages exhibited high PDIA5 expression in human cancers using the single-cell RNA sequencing analysis. We also demonstrated the interaction between PDIA5 and immune cells in glioblastoma multiforme (GBM). Multiplex immunofluorescence staining showed the upregulated expression level of PDIA5 and the increased number of M2 macrophage markers-CD163 positive cells in pan-cancer samples. Notably, PDIA5 silencing resulted in upregulated expression of PD-L1 and SPP1 in U251 cells. Silencing of PDIA5 in hepG2 cells, U251 cells, and PC3 cells contributed to a decline in their ability of proliferation, clone formation, and invasion and inhibited the migration of cocultured M2 macrophages. Additionally, PDIA5 also displayed predictive value in the immunotherapy response of both murine and human cancer cohorts. Overall, our findings indicated that PDIA5 might be a potential target for immunotherapies in cancers.