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Abstract Metabolic reprogramming in cancer significantly impacts immune responses within the tumor microenvironment, but its influence on cancer immunotherapy effectiveness remains uncertain. This study aims to elucidate the prognostic significance of metabolic genes in cancer immunotherapy through a comprehensive analytical approach. Utilizing data from the IMvigor210 trial (n = 348) and validated by retrospective datasets, we performed patient clustering using non‐negative matrix factorization based on metabolism‐related genes. A metabiotic score was developed using a “DeepSurv” neural network to assess correlations with overall survival (OS), progression‐free survival, and immunotherapy response. Validation of the metabolic score and key genes was achieved via comparative gene expression analysis using qPCR. Our analysis identified four distinct metabolic classes with significant variations in OS. Notably, the metabolism‐inactive and hypoxia‐low class demonstrated the most pronounced benefit in terms of OS. The metabolic score predicted immunotherapeutic benefits with high accuracy (AUC: 0.93 at 12 months). SETD3 emerged as a crucial gene, showing strong correlations with improved OS outcomes. This study underscores the importance of metabolic profiling in predicting cancer immunotherapy success. Specifically, patients classified as metabolism‐inactive and hypoxia‐low appear to derive substantial benefits. SETD3 is established as a promising prognostic marker, linking metabolic activity with patient outcomes, advocating for the integration of metabolic profiling into immunotherapy strategies to enhance treatment precision and efficacy.

Background: In breast cancer oncogenesis, the precise role of cell apoptosis holds untapped potential for prognostic and therapeutic insights. Thus, it is important to develop a model predicated for breast cancer patients’ prognosis and immunotherapy response based on apoptosis-related signature.Methods: Our approach involved leveraging a training dataset from The Cancer Genome Atlas (TCGA) to construct an apoptosis-related gene prognostic model. The model’s validity was then tested across several cohorts, including METABRIC, Sun Yat-sen Memorial Hospital Sun Yat-sen University (SYSMH), and IMvigor210, to ensure its applicability and robustness across different patient demographics and treatment scenarios. Furthermore, we utilized Quantitative Polymerase Chain Reaction (qPCR) analysis to explore the expression patterns of these model genes in breast cancer cell lines compared to immortalized mammary epithelial cell lines, aiming to confirm their differential expression and underline their significance in the context of breast cancer.Results: Through the development and validation of our prognostic model based on seven apoptosis-related genes, we have demonstrated its substantial predictive power for the survival outcomes of breast cancer patients. The model effectively stratified patients into high and low-risk categories, with high-risk patients showing significantly poorer overall survival in the training cohort and across all validation cohorts. Importantly, qPCR analysis confirmed that the genes constituting our model indeed exhibit differential expression in breast cancer cell lines when contrasted with immortalized mammary epithelial cell lines.Conclusion: Our study establishes a groundbreaking prognostic model using apoptosis-related genes to enhance the precision of breast cancer prognosis and treatment, particularly in predicting immunotherapy response.

Abstract The exact function of M1 macrophages and CXCL9 in forecasting the effectiveness of immune checkpoint inhibitors (ICIs) is still not thoroughly investigated. We investigated the potential of M1 macrophage and C‐X‐C Motif Chemokine Ligand 9 (CXCL9) as predictive markers for ICI efficacy, employing a comprehensive approach integrating multicohort analysis and single‐cell RNA sequencing. A significant correlation between high M1 macrophage and improved overall survival (OS) and objective response rate (ORR) was found. M1 macrophage expression was most pronounced in the immune‐inflamed phenotype, aligning with increased expression of immune checkpoints. Furthermore, CXCL9 was identified as a key marker gene that positively correlated with M1 macrophage and response to ICIs, while also exhibiting associations with immune‐related pathways and immune cell infiltration. Additionally, through exploring RNA epigenetic modifications, we identified Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G) as linked to ICI response, with high expression correlating with improved OS and immune‐related pathways. Moreover, a novel model based on M1 macrophage, CXCL9, and APOBEC3G‐related genes was developed using multi‐level attention graph neural network, which showed promising predictive ability for ORR. This study illuminates the pivotal contributions of M1 macrophages and CXCL9 in shaping an immune‐active microenvironment, correlating with enhanced ICI efficacy. The combination of M1 macrophage, CXCL9, and APOBEC3G provides a novel model for predicting clinical outcomes of ICI therapy, facilitating personalized immunotherapy.

The development of immune checkpoint inhibitors (ICIs) has significantly advanced cancer treatment. However, their efficacy is not consistent across all patients, underscoring the need for personalized approaches. In this study, we examined the relationship between activated CD4+ memory T cell expression and ICI responsiveness. A notable correlation was observed between increased activated CD4+ memory T cell expression and better patient survival in various cohorts. Additionally, the chemokine CXCL13 was identified as a potential prognostic biomarker, with higher expression levels associated with improved outcomes. Further analysis highlighted CXCL13's role in influencing the Tumor Microenvironment, emphasizing its relevance in tumor immunity. Using these findings, we developed a deep learning model by the Multi-Layer Aggregation Graph Neural Network method. This model exhibited promise in predicting ICI treatment efficacy, suggesting its potential application in clinical practice.

Abstract Background Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms. Methods In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics. Results The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02–0.06, P

ABSTRACT Objective: The incidence of diabetic nephropathy (DN) is gradually increasing worldwide. Podocyte injury, such as podocyte apoptosis and loss of the slit diaphragm (SD)-specific markers are early pathogenic features of DN. Materials and methods: The cultured mouse podocytes were separated into a high glucose-treated (HG, 30mM) group to mimic DN in vitro, a low glucose-treated (LG, 5mM) group as a control and HG+ angiotensin-(1-7)(Ang-(1-7)) and HG+Ang-(1-7) + D-Ala7-Ang-(1-7) (A779, Ang-(1-7)/Mas receptor antagonist) experimental groups. The Cell Counting Kit-8 (CCK-8) method and flow cytometry was used to detect podocyte activity and podocyte apoptosis respectively. The expression of angiotensin type 1 receptor (AT1R), Mas receptor (MasR) and podocyte-specific markers were examined by q-PCR and Western blot, respectively. Results: The results showed that the decrease in podocyte activity; the increase in podocyte apoptosis; the decreased mRNA and protein expression of nephrin, podocin, WT-1 and MasR; and the upregulated expression of AT1R induced by HG could be reversed by Ang-(1-7). However, these effects were blocked by A779. The possible mechanisms of the Ang-(1-7)-mediated effect depended on MasR. In addition, the protective effect of Ang-(1-7) on podocyte activity was dose-dependent and most obvious at 10 µM. A779 had the greatest antagonistic action against Ang-(1-7) at a concentration of 10 μM. Conclusion: This study reveals that binding of Ang-(1-7) to its specific receptor MasR may counteract the effects of Ang II mediated by AT1R to significantly attenuate podocyte injury induced by high glucose. Ang-(1-7)/MasR targeting in podocytes may be a therapeutic approach to attenuate renal injury in DN.

Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients’ transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.

BackgroundBreast cancer has become the malignancy with the highest mortality rate in female patients worldwide. The limited efficacy of immunotherapy as a breast cancer treatment has fueled the development of research on the tumor immune microenvironment.MethodsIn this study, data on breast cancer patients were collected from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohorts. Differential gene expression analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed to select overall survival (OS)-related, tumor tissue highly expressed, and immune- and inflammation-related genes. A tumor immune-inflammation signature (TIIS) consisting of 18 genes was finally screened out in the LASSO Cox regression model. Model performance was assessed by time-dependent receiver operating characteristic (ROC) curves. In addition, the CIBERSORT algorithm and abundant expression of immune checkpoints were utilized to clarify the correlation between the risk signature and immune landscape in breast cancer. Furthermore, the association of IL27 with the immune signature was analyzed in pan-cancer and the effect of IL27 on the migration of breast cancer cells was investigated since the regression coefficient of IL27 was the highest.ResultsA TIIS based on 18 genes was constructed via LASSO Cox regression analysis. In the TCGA-BRCA training cohort, 10-year AUC reached 0.89, and prediction performance of this signature was also validated in the METABRIC set. The high-risk group was significantly correlated with less infiltration of tumor-killing immune cells and the lower expression level of the immune checkpoint. Furthermore, we recommended some small-molecule drugs as novel targeted drugs for new breast cancer types. Finally, the relationship between IL27, a significant prognostic immune and inflammation cytokine, and immune status was analyzed in pan-cancer. Expression of IL27 was significantly correlated with immune regulatory gene expression and immune cell infiltration in pan-cancer. Furthermore, IL27 treatment improved breast cancer cell migration.ConclusionThe TIIS represents a promising prognostic tool for estimating OS in patients with breast cancer and is correlated with immune status.

Homeostatic plasticity encompasses the mechanisms by which neurons stabilize their synaptic strength and excitability in response to prolonged and destabilizing changes in their network activity. Prolonged activity blockade leads to homeostatic scaling of action potential (AP) firing rate in hippocampal neurons in part by decreased activity of N-Methyl-D-Aspartate receptors and subsequent transcriptional down-regulation of potassium channel genes including KCNQ3 which encodes Kv7.3. Neuronal Kv7 channels are mostly heterotetramers of Kv7.2 and Kv7.3 subunits and are highly enriched at the axon initial segment (AIS) where their current potently inhibits repetitive and burst firing of APs. However, whether a decrease in Kv7.3 expression occurs at the AIS during homeostatic scaling of intrinsic excitability and what signaling pathway reduces KCNQ3 transcript upon prolonged activity blockade remain unknown. Here, we report that prolonged activity blockade in cultured hippocampal neurons reduces the activity of extracellular signal-regulated kinase 1/2 (ERK1/2) followed by a decrease in the activation of brain-derived neurotrophic factor (BDNF) receptor, Tropomyosin receptor kinase B (TrkB). Furthermore, both prolonged activity blockade and prolonged pharmacological inhibition of ERK1/2 decrease KCNQ3 and BDNF transcripts as well as the density of Kv7.3 and ankyrin-G at the AIS. Collectively, our findings suggest that a reduction in the ERK1/2 activity and subsequent transcriptional down-regulation may serve as a potential signaling pathway that links prolonged activity blockade to homeostatic control of BDNF-TrkB signaling and Kv7.3 density at the AIS during homeostatic scaling of AP firing rate.

The Golgi apparatus (GA) is a cellular organelle that participates in the packaging, modification, and transport of proteins and lipids from the endoplasmic reticulum to be further fabricated before being presented to other cellular components. Recent studies have demonstrated that GA facilitates numerous cellular processes in cancer development. Therefore, this study aimed to establish a novel lung adenocarcinoma (LUAD) risk evaluation model based on GA gene signatures. In this study, we used TCGA-LUAD (n = 500) as the training cohort and GSE50081 (n = 127), GSE68465 (442), and GSE72094 (398) as the validation cohorts. Two immunotherapy datasets (GSE135222 and GSE126044) were also obtained from a previous study. Based on machine algorithms and bioinformatics methods, a GA gene-related risk score (GARS) was established. We found that the GARS independently predicted the prognosis of LUAD patients and remained effective across stages IA to IIIA. Then, we identified that the GARS was highly correlated with mutations in P53 and TTN. Further, this study identified that GARS is related to multiple immune microenvironmental characteristics. Furthermore, we investigated GSE135222 and GSE126044 and found that a lower GARS may be indicative of an improved therapeutic effect of PD-1/PD-L1 therapy. We also found that high GARS may lead to a better response to multiple anticancer drugs. Finally, we established a nomogram to better guide clinical application. To our knowledge, this is the first study to demonstrate a novel GA signature-based risk score formula to predict clinical prognosis and guide the treatment of LUAD patients.

Background: Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer (NSCLC), is associated with poor prognosis. However, current stage-based clinical methods are insufficient for survival prediction and decision-making. This study aimed to establish a novel model for evaluating the risk of LUAD based on hypoxia, immunity, and epithelial-mesenchymal transition (EMT) gene signatures.Methods: In this study, we used data from TCGA-LUAD for the training cohort and GSE68465 and GSE72094 for the validation cohorts. Immunotherapy datasets GSE135222, GSE126044, and IMvigor210 were obtained from a previous study. Using bioinformatic and machine algorithms, we established a risk model based on hypoxia, immune, and EMT gene signatures, which was then used to divide patients into the high and low risk groups. We analyzed differences in enriched pathways between the two groups, following which we investigated whether the risk score was correlated with stemness scores, genes related to m6A, m5C, m1A and m7G modification, the immune microenvironment, immunotherapy response, and multiple anti-cancer drug sensitivity.Results: Overall survival differed significantly between the high-risk and low-risk groups (HR = 4.26). The AUCs for predicting 1-, 3-, and 5-year survival were 0.763, 0.766, and 0.728, respectively. In the GSE68465 dataset, the HR was 2.03, while the AUCs for predicting 1-, 3-, and 5-year survival were 0.69, 0.651, and 0.618, respectively. The corresponding values in the GSE72094 dataset were an HR of 2.36 and AUCs of 0.653, 0.662, and 0.749, respectively. The risk score model could independently predict OS in patients with LUAD, and highly correlated with stemness scores and numerous m6A, m5C, m1A and m7G modification-related genes. Furthermore, the risk model was significantly correlated with multiple immune microenvironment characteristics. In the GSE135222 dataset, the HR was 4.26 and the AUC was 0.702. Evaluation of the GSE126044 and IMvigor210 cohorts indicated that PD-1/PD-LI inhibitor treatment may be indicated in patients with low risk scores, while anti-cancer therapy with various drugs may be indicated in patients with high risk scores.Conclusion: Our novel risk model developed based on hypoxia, immune, and EMT gene signatures can aid in predicting clinical prognosis and guiding treatment in patients with LUAD.

Homeostatic plasticity encompasses the mechanisms by which neurons stabilize their synaptic strength and excitability in response to prolonged and destabilizing changes in their network activity. Prolonged activity blockade leads to homeostatic scaling of action potential (AP) firing rate in hippocampal neurons in part by decreased activity of N-Methyl-D-Aspartate receptors and subsequent transcriptional down-regulation of potassium channel genes including KCNQ3 which encodes Kv7.3. Neuronal Kv7 channels are mostly heterotetramers of Kv7.2 and Kv7.3 subunits and are highly enriched at the axon initial segment (AIS) where their current potently inhibits repetitive and burst firing of APs. However, whether a decrease in Kv7.3 expression occurs at the AIS during homeostatic scaling of intrinsic excitability and what signaling pathway reduces KCNQ3 transcript upon prolonged activity blockade remain unknown. Here, we report that prolonged activity blockade in cultured hippocampal neurons reduces the activity of extracellular signalregulated kinase 1/2 (ERK1/2) followed by a decrease in the activation of brain-derived neurotrophic factor (BDNF) receptor, Tropomyosin receptor kinase B (TrkB). Furthermore, both prolonged activity blockade and prolonged pharmacological inhibition of ERK1/2 decrease KCNQ3 and BDNF transcripts as well as the density of Kv7.3 and ankyrin-G at the AIS. Collectively, our findings suggest that a reduction in the ERK1/2 activity and subsequent transcriptional down-regulation may serve as a potential signaling pathway that links prolonged activity blockade to homeostatic control of BDNF-TrkB signaling and Kv7.3 density at the AIS during homeostatic scaling of AP firing rate. [ABSTRACT FROM AUTHOR]