Comprehensive analysis of T-cell regulatory factors and tumor immune microenvironment in stomach adenocarcinoma.

BACKGROUND: Gastric cancer (GC) is one of the most prevalent malignant tumors worldwide and is associated with high morbidity and mortality rates. However, the specific biomarkers used to predict the postoperative prognosis of patients with gastric cancer remain unknown. Recent research has shown that the tumor microenvironment (TME) has an increasingly positive effect on anti-tumor activity. This study aims to build signatures to study the effect of certain genes on gastric cancer. METHODS: Expression profiles of 37 T cell-related genes and their TME characteristics were comprehensively analyzed. A risk signature was constructed and validated based on the screened T cell-related genes, and the roles of hub genes in GC were experimentally validated. RESULTS: A novel T cell-related gene signature was constructed based on CD5, ABCA8, SERPINE2, ESM1, SERPINA5, and NMU. The high-risk group indicated lower overall survival (OS), poorer immune efficacy, and higher drug resistance, with SERPINE2 promoting GC cell proliferation, according to experiments. SERPINE2 and CXCL12 were significantly correlated, indicating poor OS via the Youjiang cohort. CONCLUSIONS: This study identified T cell-related genes in patients with stomach adenocarcinoma (STAD) for prognosis estimation and proposed potential immunotherapeutic targets for STAD.

Deciphering glutamine metabolism patterns for malignancy and tumor microenvironment in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer characterized by metabolic reprogramming. Glutamine metabolism is pivotal in metabolic reprogramming, contributing to the significant heterogeneity observed in ccRCC. Consequently, developing prognostic markers associated with glutamine metabolism could enhance personalized treatment strategies for ccRCC patients. This study obtained RNA sequencing and clinical data from 763 ccRCC cases sourced from multiple databases. Consensus clustering of 74 glutamine metabolism related genes (GMRGs)- profiles stratified the patients into three clusters, each of which exhibited distinct prognosis, tumor microenvironment, and biological characteristics. Then, six genes (SMTNL2, MIOX, TMEM27, SLC16A12, HRH2, and SAA1) were identified by machine-learning algorithms to develop a predictive signature related to glutamine metabolism, termed as GMRScore. The GMRScore showed significant differences in clinical prognosis, expression profile of immune checkpoints, abundance of immune cells, and immunotherapy response of ccRCC patients. Besides, the nomogram incorporating the GMRScore and clinical features showed strong predictive performance in prognosis of ccRCC patients. ALDH18A1, one of the GRMGs, exhibited elevated expression level in ccRCC and was related to markedly poorer prognosis in the integrated cohort, validated by proteomic profiling of 232 ccRCC samples from Fudan University Shanghai Cancer Center (FUSCC). Conducting western blotting, CCK-8, transwell, and flow cytometry assays, we found the knockdown of ALDH18A1 in ccRCC significantly promoted apoptosis and inhibited proliferation, invasion, and epithelial-mesenchymal transition (EMT) in two human ccRCC cell lines (786-O and 769-P). In conclusion, we developed a glutamine metabolism-related prognostic signature in ccRCC, which is tightly linked to the tumor immune microenvironment and immunotherapy response, potentially facilitating precision therapy for ccRCC patients. Additionally, this study revealed the key role of ALDH18A1 in promoting ccRCC progression for the first time.

Immunobiological signatures and the emerging role of SPP1 in predicting tumor heterogeneity, malignancy, and clinical outcomes in stomach adenocarcinoma.

BACKGROUND: Immunotherapy, as a form of immunobiological therapy, represents a promising approach for enhancing patients' immune responses. This work aims to present innovative ideas and insights for prognostic assessment and clinical treatment of stomach adenocarcinoma (STAD) by leveraging immunobiological signatures. METHODS: We employed weighted gene co-expression network analysis (WGCNA) and unsupervised clustering analysis to identify hub genes. These hub genes were utilized to construct a prognostic risk model, and their impact on the tumor microenvironment (TME) and DNA variations was assessed using large-scale STAD patient cohorts. Additionally, we conducted transfection experiments with plasmids to investigate the influence of SPP1 on the malignancy of HGC27 and NCI-N87 cells. RESULTS: Unsupervised clustering of 12 immune-related genes (IRGs) revealed three distinct alteration patterns with unique molecular phenotypes, clinicopathological characteristics, prognosis, and TME features. Using LASSO and multivariate Cox regression analyses, we identified three hub genes (MMP12, SPP1, PLAU) from the IRGs to establish a risk signature. This IRG-related risk model significantly stratified the prognosis risk among STAD patients in the training (n = 522), testing (n = 521), and validation (n = 300) cohorts. Notably, there were discernible differences in therapy responses and TME characteristics, such as tumor purity and lymphocyte infiltration, between the risk model groups. Subsequently, a nomogram that incorporates the IRG signature and clinicopathological factors demonstrated superior sensitivity and specificity in predicting outcomes for STAD patients. Furthermore, down-regulation of SPP1, as observed after siRNA transfection, significantly inhibited the proliferation and migration abilities of HGC27 and NCI-N87 cells. CONCLUSIONS: In summary, this study highlights the critical role of immune-related signatures in STAD and offers novel insights into prognosis indicators and immunotherapeutic targets for this condition. SPP1 emerges as an independent prognostic factor for STAD and appears to regulate STAD progression by influencing the immune microenvironment.

Integrating machine learning and bioinformatics analysis to m6A regulator-mediated methylation modification models for predicting glioblastoma patients' prognosis and immunotherapy response.

BACKGROUND: Epigenetic regulations of immune responses are essential for cancer development and growth. As a critical step, comprehensive and rigorous explorations of m6A methylation are important to determine its prognostic significance, tumor microenvironment (TME) infiltration characteristics and underlying relationship with glioblastoma (GBM). METHODS: To evaluate m6A modification patterns in GBM, we conducted unsupervised clustering to determine the expression levels of GBM-related m6A regulatory factors and performed differential analysis to obtain m6A-related genes. Consistent clustering was used to generate m6A regulators cluster A and B. Machine learning algorithms were implemented for identifying TME features and predicting the response of GBM patients receiving immunotherapy. RESULTS: It is found that the m6A regulatory factor significantly regulates the mutation of GBM and TME. Based on Europe, America, and China data, we established m6Ascore through the m6A model. The model accurately predicted the results of 1206 GBM patients from the discovery cohort. Additionally, a high m6A score was associated with poor prognoses. Significant TME features were found among the different m6A score groups, which demonstrated positive correlations with biological functions (i.e., EMT2) and immune checkpoints. CONCLUSIONS: m6A modification was important to characterize the tumorigenesis and TME infiltration in GBM. The m6Ascore provided GBM patients with valuable and accurate prognosis and prediction of clinical response to various treatment modalities, which could be useful to guide patient treatments.

Transcriptomic and immunologic implications of the epithelial-mesenchymal transition model reveal a novel role of SFTA2 in prognosis of non-small-cell lung carcinoma.

Non-small-cell lung cancer (NSCLC) is the second most common cancer worldwide, and most deaths are associated with epithelial-mesenchymal transition (EMT). Therefore, this study aimed to explore the role of EMT-related transcriptomic profiles in NSCLC and the effect of EMT-based signatures on clinical diagnosis, prognosis, and treatment responses for patients with NSCLC. After integrating the transcriptomics and clinicopathological data, we first constructed EMT clusters (C1 and C2) using machine learning algorithms, found the significant relationship between EMT clusters and survival outcomes, and then explored the impact of EMT clusters on the tumor heterogeneity, drug efficiency, and immune microenvironment of NSCLC. Prominently, differential-enriched tumor-infiltrated lymphocytes were found between EMT clusters, especially the macrophages and monocyte. Next, we identified the most significantly down-regulated gene SFTA2 in the EMT clusters C2 with poor prognosis. Using RT-qPCR and RNA-seq data from the public database, we found prominently elevated SFTA2 expression in NSCLC tissues compared with normal lung tissues, and the tumor suppressor role of SFTA2 in 82 Chinese patients with NSCLC. After Cox regression and survival analysis, we demonstrated that higher SFTA2 expression in tumor samples significantly predicts favorable prognosis of NSCLC based on multiple independent cohorts. In addition, the prognostic value of SFTA2 expression differs for patients with lung adenocarcinoma and squamous cell carcinoma. In conclusion, this study demonstrated that the EMT process is involved in the malignant progression and the constructed EMT clusters exerted significant predictive drug resistance and prognostic value for NSCLC patients. In addition, we first identified the high tumoral expression of SFTA2 correlated with better prognosis and could serve as a predictive biomarker for outcomes and treatment response of NSCLC patients.

Prognostic analysis of cuproptosis-related gene in triple-negative breast cancer.

Background: Cuproptosis is a copper-dependent cell death mechanism that is associated with tumor progression, prognosis, and immune response. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of triple-negative breast cancer (TNBC) remains unclear. Patients and methods: In total, 346 TNBC samples were collected from The Cancer Genome Atlas database and three Gene Expression Omnibus datasets, and were classified using R software packages. The relationships between the different subgroups and clinical pathological characteristics, immune infiltration characteristics, and mutation status of the TME were examined. Finally, a nomogram and calibration curve were constructed to predict patient survival probability to improve the clinical applicability of the CRG_score. Results: We identified two CRG clusters with immune cell infiltration characteristics highly consistent with those of the immune-inflamed and immune-desert clusters. Furthermore, we demonstrated that the gene signature can be used to evaluate tumor immune cell infiltration, clinical features, and prognostic status. Low CRG_scores were characterized by high tumor mutation burden and immune activation, good survival probability, and more immunoreactivity to CTLA4, while high CRG_scores were characterized by the activation of stromal pathways and immunosuppression. Conclusion: This study revealed the potential effects of CRGs on the TME, clinicopathological features, and prognosis of TNBC. The CRGs were closely associated with the tumor immunity of TNBC and are a potential tool for predicting patient prognosis. Our data provide new directions for the development of novel drugs in the future.

Identification of adenylate cyclase 2 methylation in bladder cancer with implications for prognosis and immunosuppressive microenvironment.

Background: The incidence and mortality of bladder cancer (BCa) are increasing, while the existing diagnostic methods have limitations. Therefore, for early detection and response prediction, it is crucial to improve the prognosis and treatment strategies. However, with existing diagnostic methods, detecting BCa in the early stage is challenging. Hence, novel biomarkers are urgently needed to improve early diagnosis and treatment efficiency. Methods: The gene expression profile and gene methylation profile dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), differentially methylated genes (DMGs), and methylation-regulated differentially expressed genes (MeDEGs) were gradually identified. A cancer genome map was obtained using online gene expression profile interaction analysis, and survival implications were produced using Kaplan-Meier survival analysis. GSEA was employed to predict the marker pathways where DEGs were significantly involved. The study used bisulfite PCR amplification combined with bisulfite amplicon sequencing (BSAS) to screen for methylation analysis of multiple candidate regions of the adenylate cyclase 2 (ADCY2) based on the sequence design of specific gene regions and CpG islands. Results: In this study, DEGs and DMGs with significantly up- or down-regulated expression were selected. The intersection method was used to screen the MeDEGs. The interaction network group in STRING was then visualized using Cytoscape, and the PPI network was constructed to identify the key genes. The key genes were then analyzed using functional enrichment. To compare the relationship between key genes and the prognosis of BCa patients, we further investigated ADCY2 and found that ADCY2 can be a potential clinical biomarker in BCa prognosis and immunotherapy response prediction. In human BCa 5637 and MGH1 cells, we developed and verified the effectiveness of ADCY2 primers using BSAS technology. The findings revealed that the expression of ADCY2 is highly regulated by the methylation of the promoter regions. Conclusion: This study revealed that increased expression of ADCY2 was significantly correlated with increased tumor heterogeneity, predicting worse survival and immunotherapy response in BCa patients.