ABSTRACT Metabolism reprogramming within the tumor microenvironment (TME) can have a profound impact on immune cells. Identifying the association between metabolic phenotypes and immune cells in lung adenocarcinoma (LUAD) may reveal mechanisms of resistance to immune checkpoint inhibitors (ICIs). Metabolic phenotypes were classified by expression of metabolic genes. Somatic mutations and transcriptomic features were compared across the different metabolic phenotypes. The metabolic phenotype of LUAD is predominantly determined by reductase-oxidative activity and is divided into two categories: redoxhigh LUAD and redoxlow LUAD. Genetically, redoxhigh LUAD is mainly driven by mutations in KEAP1, STK11, NRF2, or SMARCA4. These mutations are more prevalent in redoxhigh LUAD (72.5%) compared to redoxlow LUAD (17.4%), whereas EGFR mutations are more common in redoxlow LUAD (19.0% vs. 0.7%). Single-cell RNA profiling of pre-treatment and post-treatment samples from patients receiving neoadjuvant chemoimmunotherapy revealed that tissue-resident memory CD8+ T cells are responders to ICIs. However, these cells are significantly reduced in redoxhigh LUAD. The redoxhigh phenotype is primarily attributed to tumor cells and is positively associated with mTORC1 signaling. LUAD with the redoxhigh phenotype demonstrates a lower response rate (39.1% vs. 70.8%, p = 0.001), shorter progression-free survival (3.3 vs. 14.6 months, p = 0.004), and overall survival (12.1 vs. 31.2 months, p = 0.022) when treated with ICIs. The redoxhigh phenotype in LUAD is predominantly driven by mutations in KEAP1, STK11, NRF2, and SMARCA4. This phenotype diminishes the number of tissue-resident memory CD8+ T cells and attenuates the efficacy of ICIs.

Redoxhigh phenotype mediated by KEAP1/STK11/SMARCA4/NRF2 mutations diminishes tissue-resident memory CD8+ T cells and attenuates the efficacy of immunotherapy in lung adenocarcinoma

Obesity is increasingly recognized as a significant factor impacting the outcomes of fertility-preserving therapies for endometrial cancer (EC). This review explores the effects of glycolipid metabolism on EC and its relationship with body weight. We will examine how excess body weight influences the effectiveness of fertility-preserving treatments and discuss potential mechanisms for effective weight management. Additionally, the review highlights the importance of comprehensive weight management as an adjunct strategy to enhance the efficacy of fertility-preserving interventions, providing insights into how to integrate metabolic health into clinical treatment protocols. Weight management can modify the tumor microenvironment by depriving the tumor of nutrients, whereas exercise can enhance immunity, potentially leading to tumor cell death. In addition, progesterone therapy may impede the proliferation of EC cells. Comprehensive weight management can serve as an essential adjuvant treatment for patients undergoing fertility-preserving therapies for EC. In this review, we highlight that comprehensive weight management can serve as a crucial adjuvant treatment for patients undergoing fertility-preserving therapies for endometrial cancer. Targeting glycolipid metabolism and addressing adiposity can improve hormonal balance, reduce inflammation, and enhance fertility outcomes. Further research is necessary to establish specific protocols and evaluate the effectiveness of these strategies in clinical practice.

Weight Management for Fertility-Preservation Therapy in Endometrial Cancer: Opportunities and Challenges

Uterine corpus endometrial carcinoma (UCEC) is one of the most common gynecologic malignancies. Currently, for UCEC cancer, molecular classification based on metabolic gene characteristics is rarely established. Here, we describe the molecular subtype features of UCEC by classifying metabolism-related gene profiles. Therefore, integrative analysis was performed on UCEC patients from the TCGA public database. Consensus clustering of RNA expression data on 2,752 previously reported metabolic genes identified two metabolic subtypes, namely, C1 and C2 subtypes. Two metabolic subtypes for prognostic characteristics, immune infiltration, genetic alteration, and responses to immunotherapy existed with distinct differences. Then, differentially expressed genes (DEGs) among the two metabolic subtypes were also clustered into two subclusters, and the aforementioned features were similar to the metabolic subtypes, supporting that the metabolism-relevant molecular classification is reliable. The results showed that the C1 subtype has high metabolic activity, high immunogenicity, high gene mutation, and a good prognosis. The C2 subtype has some features with low metabolic activity, low immunogenicity, high copy number variation (CNV) alteration, and poor prognosis. Finally, a model was identified, with three gene metabolism-related signatures, which can predict the prognosis. These findings of this study demonstrate a new classification in UCEC based on the metabolic pattern, thereby providing valuable information for understanding UCEC’s molecular characteristics.

Metabolism-associated molecular classification of uterine corpus endometrial carcinoma

Introduction Capping actin protein, gelsolin-like (CAPG) is a potential therapeutic target in various cancers. However, the potential immunotherapeutic effects and prognostic value of CAPG in uterine corpus endometrial carcinoma (UCEC) remain unclear. Methods The characterization, methylation effects, prognostic value, targeted miRNAs of CAPG, and the correlation of CAPG with immune cell infiltration and ferroptosis in UCEC were investigated using multiple public databases and online tools. Furtherly, we explored the potential physiological function of CAPG using EdU and Transwell migration assays, identified the cell localization and expression of CAPG and GPX4 by immunofluorescence, and detected the intracellular Fe 2+ levels using a FerroOrange fluorescent probe in Ishikawa cells. Additionally, the OncoPredict package was used to analyze the potential chemotherapeutic drugs for UCEC. Results CAPG showed generally high expression in tumor group. The overall survival rate of the high-risk group was significantly lower than that of the low-risk group. Enrichment analysis indicated that CAPG is involved in immune-related pathways and is closely associated with the tumor microenvironment. CAPG expression levels were affected by abnormal DNA methylation and/or targeted miRNAs, infiltration levels and marker genes of various immune cells, thereby impacting immune response, ferroptosis, and patient prognosis. Ferroptosis analysis indicated that ALOX5 and VLDLR were the top CAPG-related ferroptosis markers; glutathione metabolism levels in tumor group were generally high, and decitabine was a ferroptosis inducer. CAPG-siRNA suppressed the cell proliferation and invasion, and markedly elevated the expression levels of immune-related genes IL8, TNF, TLR4 and the intracellular Fe 2+ levels. CAPG co-located with GPX4 in nucleus and co-regulated ferroptosis and metabolism in Ishikawa cells. Moreover, four chemotherapy drugs showed better sensitivity to UCEC patients in the low-risk cohort. Conclusions CAPG may serve as a potential biomarker of UCEC owing to its role in modulating the immune response and ferroptosis, providing novel perspectives for combined immunotherapy of UCEC. 

Identification of CAPG as a potential prognostic biomarker associated with immune cell infiltration and ferroptosis in uterine corpus endometrial carcinoma

Tongue squamous cell carcinoma (TSCC) has a poor prognosis and destructive characteristics. Reliable biomarkers are urgently required to predict disease outcomes and to guide TSCC treatment. This study aimed to develop a multigene signature and prognostic nomogram that can accurately predict the prognosis of patients with TSCC. We screened differentially expressed genes associated with TSCC using The Cancer Genome Atlas dataset. Based on this, we developed a new multi-mRNA gene signature using univariate Cox regression, Least Absolute Shrinkage and Selection Operator regression, and multivariate Cox regression. We used the concordance index to evaluate the accuracy of this new multigene model. Moreover, we performed receiver operating characteristic and Kaplan–Meier survival analyses to assess the predictive ability of the new multigene model. In addition, we created a prognostic nomogram incorporating clinical and pathological characteristics, with the aim of enhancing the adaptability of this model in practical clinical settings. We successfully developed a new prognostic model based on the expression levels of these 3 mRNAs that can be used to predict the prognosis of patients with TSCC. This prediction model includes 3 genes: KRT33B, CDKN2A, and CA9. In the validation set, the concordance index of this model was 0.851, and the area under the curve was 0.778 and 0.821 in the training and validation sets, respectively. Kaplan–Meier survival analysis showed that regardless of whether it was in the training or validation set, the prognosis of high-risk patients was significantly worse than that of low-risk patients (P < .001). Multivariate Cox regression analysis revealed that this model was an independent prognostic factor for patients with TSCC (P < .001). Our study suggests that this 3-gene signature model has a high level of accuracy and predictive ability, is closely related to the overall survival rate of patients with TSCC, and can independently predict the prognosis of TSCC patients with high accuracy and predictive ability.

Construction and validation of a prognostic model for tongue cancer based on three genes signature

Background: An imbalance in the redox homeostasis has been reported in multiple cancers and is associated with a poor prognosis of disease. However, the prognostic value of redox-related genes in non-small-cell lung cancer (NSCLC) remains unclear. Methods: RNA sequencing data, DNA methylation data, mutation, and clinical data of NSCLC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Redox-related differentially expressed genes (DEGs) were used to construct the prognostic signature using least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan–Meier survival curve and receiver operator characteristic (ROC) curve analyses were applied to validate the accuracy of the gene signature. Nomogram and calibration plots of the nomogram were constructed to predict prognosis. Pathway analysis was performed using gene set enrichment analysis. The correlations of risk score with tumor stage, immune infiltration, DNA methylation, tumor mutation burden (TMB), and chemotherapy sensitivity were evaluated. The prognostic signature was validated using GSE31210, GSE26939, and GSE68465 datasets. Real-time polymerase chain reaction (PCR) was used to validate dysregulated genes in NSCLC. Results: A prognostic signature was constructed using the LASSO regression analysis and was represented as a risk score. The high-risk group was significantly correlated with worse overall survival (OS) (p < 0.001). The area under the ROC curve (AUC) at the 5-year stage was 0.657. The risk score was precisely correlated with the tumor stage and was an independent prognostic factor for NSCLC. The constructed nomogram accurately predicted the OS of patients after 1-, 3-, and 5-year periods. DNA replication, cell cycle, and ECM receptor interaction were the main pathways enriched in the high-risk group. In addition, the high-risk score was correlated with higher TMB, lower methylation levels, increased infiltrating macrophages, activated memory CD4+ T cells, and a higher sensitivity to chemotherapy. The signature was validated in GSE31210, GSE26939, and GSE68465 datasets. Real-time PCR validated dysregulated mRNA expression levels in NSCLC. Conclusions: A prognostic redox-related gene signature was successfully established in NSCLC, with potential applications in the clinical setting.

Construction of a redox-related gene signature for overall survival prediction and immune infiltration in non-small-cell lung cancer

Background Uterine corpus endometrial carcinoma (UCEC) is the third most common gynecologic malignancy. Fatty acid metabolism (FAM) is an essential metabolic process in the immune microenvironment that occurs reprogramming in the presence of tumor signaling and nutrient competition. This study aimed to identify the fatty acid metabolism-related genes (FAMGs) to develop a risk signature for predicting UCEC. Methods The differentially expressed FAMGs between UCEC samples and controls from TCGA database were discovered. A prognostic signature was then constructed by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Based on the median risk score, UCEC samples were categorized into high- and low-FAMGs groups. Kaplan-Meier (K-M) curve was applied to determine patients’ overall survival (OS). The independent prognostic value was assessed by uni- and multivariate analyses. The associations between the risk score and immune status, immune score, and drug resistance were evaluated. Quantitative Real-time PCR (qRT-PCR) was utilized to confirm FAMGs expression levels in UCEC cells. Results We built a 10-FAMGs prognostic signature and examined the gene mutation and copy number variations (CNV). Patients with a high-FAMGs had a worse prognosis compared to low-FAMGs patients in TCGA train and test sets. We demonstrated that FAMGs-based risk signature was a significant independent prognostic predictor of UCEC. A nomogram was also created incorporating this risk model and clinicopathological features, with high prognostic performance for UCEC. The immune status of each group was varied, and immune score was higher in a low-FAMGs group. HLA-related genes such as DRB1, DMA, DMB, and DQB2 had higher expression levels in the low-FAMGs group. Meanwhile, high-FAMGs patients were likely to response more strongly to the targeted drugs Bortezomib, Foretinib and Gefitinib. The qRT-PCR evidence further verified the significant expression of FAMGs in this signature. Conclusions A FAMGs-based risk signature might be considered as an independent prognostic indicator to predict UCEC prognosis, evaluate immune status and provide a new direction for therapeutic strategies.

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Integrated bioinformatics analysis and experimental validation reveals fatty acid metabolism-related prognostic signature and immune responses for uterine corpus endometrial carcinoma

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Construction of a redox-related gene signature for overall survival prediction and immune infiltration in non-small-cell lung cancer

Integrated bioinformatics analysis and experimental validation reveals fatty acid metabolism-related prognostic signature and immune responses for uterine corpus endometrial carcinoma