Image_4_Circadian Regulation Patterns With Distinct Immune Landscapes in Gliomas Aid in the Development of a Risk Model to Predict Prognosis and Therapeutic Response.tif

Circadian disruption in tumorigenesis has been extensively studied, but how circadian rhythm (CR) affects the formation of tumor microenvironment (TME) and the crosstalk between TME and cancer cells is largely unknown, especially in gliomas. Herein, we retrospectively analyzed transcriptome data and clinical parameters of glioma patients from public databases to explore circadian rhythm-controlled tumor heterogeneity and characteristics of TME in gliomas. Firstly, we pioneered the construction of a CR gene set collated from five datasets and review literatures. Unsupervised clustering was used to identify two CR clusters with different CR patterns on the basis of the expression of CR genes. Remarkably, the CR cluster-B was characterized by enriched myeloid cells and activated immune-related pathways. Next, we applied principal component analysis to construct a CRscore to quantify CR patterns of individual tumors, and the function of the CRscore in prognostic prediction was further verified by univariate and multivariate regression analyses in combination with a nomogram. The CRscore could not only be an independent factor to predict prognosis of glioma patients but also guide patients to choose suitable treatment strategies: immunotherapy or chemotherapy. A glioma patient with a high CRscore might respond to immune checkpoint blockade, whereas one with a low CRscore could benefit from chemotherapy. In this study, we revealed that circadian rhythms modulated tumor heterogeneity, TME diversity, and complexity in gliomas. Evaluating the CRscore of an individual tumor would contribute to gaining a greater understanding of the tumor immune status of each patient, enhancing the accuracy of prognostic prediction, and suggesting more effective treatment options.</p

Image_2_Circadian Regulation Patterns With Distinct Immune Landscapes in Gliomas Aid in the Development of a Risk Model to Predict Prognosis and Therapeutic Response.tif

Circadian disruption in tumorigenesis has been extensively studied, but how circadian rhythm (CR) affects the formation of tumor microenvironment (TME) and the crosstalk between TME and cancer cells is largely unknown, especially in gliomas. Herein, we retrospectively analyzed transcriptome data and clinical parameters of glioma patients from public databases to explore circadian rhythm-controlled tumor heterogeneity and characteristics of TME in gliomas. Firstly, we pioneered the construction of a CR gene set collated from five datasets and review literatures. Unsupervised clustering was used to identify two CR clusters with different CR patterns on the basis of the expression of CR genes. Remarkably, the CR cluster-B was characterized by enriched myeloid cells and activated immune-related pathways. Next, we applied principal component analysis to construct a CRscore to quantify CR patterns of individual tumors, and the function of the CRscore in prognostic prediction was further verified by univariate and multivariate regression analyses in combination with a nomogram. The CRscore could not only be an independent factor to predict prognosis of glioma patients but also guide patients to choose suitable treatment strategies: immunotherapy or chemotherapy. A glioma patient with a high CRscore might respond to immune checkpoint blockade, whereas one with a low CRscore could benefit from chemotherapy. In this study, we revealed that circadian rhythms modulated tumor heterogeneity, TME diversity, and complexity in gliomas. Evaluating the CRscore of an individual tumor would contribute to gaining a greater understanding of the tumor immune status of each patient, enhancing the accuracy of prognostic prediction, and suggesting more effective treatment options.</p

Image_3_Circadian Regulation Patterns With Distinct Immune Landscapes in Gliomas Aid in the Development of a Risk Model to Predict Prognosis and Therapeutic Response.tif

Circadian disruption in tumorigenesis has been extensively studied, but how circadian rhythm (CR) affects the formation of tumor microenvironment (TME) and the crosstalk between TME and cancer cells is largely unknown, especially in gliomas. Herein, we retrospectively analyzed transcriptome data and clinical parameters of glioma patients from public databases to explore circadian rhythm-controlled tumor heterogeneity and characteristics of TME in gliomas. Firstly, we pioneered the construction of a CR gene set collated from five datasets and review literatures. Unsupervised clustering was used to identify two CR clusters with different CR patterns on the basis of the expression of CR genes. Remarkably, the CR cluster-B was characterized by enriched myeloid cells and activated immune-related pathways. Next, we applied principal component analysis to construct a CRscore to quantify CR patterns of individual tumors, and the function of the CRscore in prognostic prediction was further verified by univariate and multivariate regression analyses in combination with a nomogram. The CRscore could not only be an independent factor to predict prognosis of glioma patients but also guide patients to choose suitable treatment strategies: immunotherapy or chemotherapy. A glioma patient with a high CRscore might respond to immune checkpoint blockade, whereas one with a low CRscore could benefit from chemotherapy. In this study, we revealed that circadian rhythms modulated tumor heterogeneity, TME diversity, and complexity in gliomas. Evaluating the CRscore of an individual tumor would contribute to gaining a greater understanding of the tumor immune status of each patient, enhancing the accuracy of prognostic prediction, and suggesting more effective treatment options.</p

Image_1_Circadian Regulation Patterns With Distinct Immune Landscapes in Gliomas Aid in the Development of a Risk Model to Predict Prognosis and Therapeutic Response.tif

Circadian disruption in tumorigenesis has been extensively studied, but how circadian rhythm (CR) affects the formation of tumor microenvironment (TME) and the crosstalk between TME and cancer cells is largely unknown, especially in gliomas. Herein, we retrospectively analyzed transcriptome data and clinical parameters of glioma patients from public databases to explore circadian rhythm-controlled tumor heterogeneity and characteristics of TME in gliomas. Firstly, we pioneered the construction of a CR gene set collated from five datasets and review literatures. Unsupervised clustering was used to identify two CR clusters with different CR patterns on the basis of the expression of CR genes. Remarkably, the CR cluster-B was characterized by enriched myeloid cells and activated immune-related pathways. Next, we applied principal component analysis to construct a CRscore to quantify CR patterns of individual tumors, and the function of the CRscore in prognostic prediction was further verified by univariate and multivariate regression analyses in combination with a nomogram. The CRscore could not only be an independent factor to predict prognosis of glioma patients but also guide patients to choose suitable treatment strategies: immunotherapy or chemotherapy. A glioma patient with a high CRscore might respond to immune checkpoint blockade, whereas one with a low CRscore could benefit from chemotherapy. In this study, we revealed that circadian rhythms modulated tumor heterogeneity, TME diversity, and complexity in gliomas. Evaluating the CRscore of an individual tumor would contribute to gaining a greater understanding of the tumor immune status of each patient, enhancing the accuracy of prognostic prediction, and suggesting more effective treatment options.</p

Table_1_Circadian Regulation Patterns With Distinct Immune Landscapes in Gliomas Aid in the Development of a Risk Model to Predict Prognosis and Therapeutic Response.xlsx

Circadian disruption in tumorigenesis has been extensively studied, but how circadian rhythm (CR) affects the formation of tumor microenvironment (TME) and the crosstalk between TME and cancer cells is largely unknown, especially in gliomas. Herein, we retrospectively analyzed transcriptome data and clinical parameters of glioma patients from public databases to explore circadian rhythm-controlled tumor heterogeneity and characteristics of TME in gliomas. Firstly, we pioneered the construction of a CR gene set collated from five datasets and review literatures. Unsupervised clustering was used to identify two CR clusters with different CR patterns on the basis of the expression of CR genes. Remarkably, the CR cluster-B was characterized by enriched myeloid cells and activated immune-related pathways. Next, we applied principal component analysis to construct a CRscore to quantify CR patterns of individual tumors, and the function of the CRscore in prognostic prediction was further verified by univariate and multivariate regression analyses in combination with a nomogram. The CRscore could not only be an independent factor to predict prognosis of glioma patients but also guide patients to choose suitable treatment strategies: immunotherapy or chemotherapy. A glioma patient with a high CRscore might respond to immune checkpoint blockade, whereas one with a low CRscore could benefit from chemotherapy. In this study, we revealed that circadian rhythms modulated tumor heterogeneity, TME diversity, and complexity in gliomas. Evaluating the CRscore of an individual tumor would contribute to gaining a greater understanding of the tumor immune status of each patient, enhancing the accuracy of prognostic prediction, and suggesting more effective treatment options.</p

Image_5_Circadian Regulation Patterns With Distinct Immune Landscapes in Gliomas Aid in the Development of a Risk Model to Predict Prognosis and Therapeutic Response.tif

Circadian disruption in tumorigenesis has been extensively studied, but how circadian rhythm (CR) affects the formation of tumor microenvironment (TME) and the crosstalk between TME and cancer cells is largely unknown, especially in gliomas. Herein, we retrospectively analyzed transcriptome data and clinical parameters of glioma patients from public databases to explore circadian rhythm-controlled tumor heterogeneity and characteristics of TME in gliomas. Firstly, we pioneered the construction of a CR gene set collated from five datasets and review literatures. Unsupervised clustering was used to identify two CR clusters with different CR patterns on the basis of the expression of CR genes. Remarkably, the CR cluster-B was characterized by enriched myeloid cells and activated immune-related pathways. Next, we applied principal component analysis to construct a CRscore to quantify CR patterns of individual tumors, and the function of the CRscore in prognostic prediction was further verified by univariate and multivariate regression analyses in combination with a nomogram. The CRscore could not only be an independent factor to predict prognosis of glioma patients but also guide patients to choose suitable treatment strategies: immunotherapy or chemotherapy. A glioma patient with a high CRscore might respond to immune checkpoint blockade, whereas one with a low CRscore could benefit from chemotherapy. In this study, we revealed that circadian rhythms modulated tumor heterogeneity, TME diversity, and complexity in gliomas. Evaluating the CRscore of an individual tumor would contribute to gaining a greater understanding of the tumor immune status of each patient, enhancing the accuracy of prognostic prediction, and suggesting more effective treatment options.</p

Table_1_Sublethal heat treatment enhances lactic acid uptake in macrophages via MCT1, leading to reduced paraspeckle formation and a subsequent decrease in macrophage pyroptosis.xlsx

IntroductionHeat ablation is one of the key modalities in treating liver cancer, yet the residual cancer tissues suffering sublethal heat treatment possess a potential for increased malignancy. This study conducts a comprehensive analysis of cellular dynamics, metabolic shifts, and macrophage polarization within the tumor microenvironment following sublethal heat treatment.MethodsWe observed significant acidification in tumor cell supernatants, attributed to increased lactic acid production. The study focused on how this pH shift, crucial in tumor progression and resistance, influences macrophage polarization, especially towards the M2 phenotype known for tumor-promoting functions. We also examined the upregulation of MCT1 expression post sublethal heat treatment and its primary role in lactic acid transport.ResultsNotably, the study found minimal disparity in MCT1 expression between hepatocellular carcinoma patients and healthy liver tissues, highlighting the complexity of cancer biology. The research further revealed an intricate relationship between lactic acid, MCT1, and the inhibition of macrophage pyroptosis, offering significant insights for therapeutic strategies targeting the tumor immune environment. Post sublethal heat treatment, a reduction in paraspeckle under lactic acid exposure was observed, indicating diverse cellular impacts. Additionally, PKM2 was identified as a key molecule in this context, with decreased levels after sublethal heat treatment in the presence of lactic acid.DiscussionCollectively, these findings illuminate the intertwined mechanisms of sublethal heat treatments, metabolic alterations, and immune modulation in the tumor milieu, providing a deeper understanding of the complex interplay in cancer biology and treatment.</p