Identification and Validation of an Energy Metabolism-Related lncRNA-mRNA Signature for Lower-Grade Glioma

Metabolism: an important player in glioma survival and development

Gliomas are malignant tumors originating from both neuroglial cells and neural stem cells. The involvement of neural stem cells contributes to the tumor's heterogeneity, affecting its metabolic features, development, and response to therapy. This review provides a brief introduction to the importance of metabolism in gliomas before systematically categorizing them into specific groups based on their histological and molecular genetic markers. Metabolism plays a critical role in glioma biology, as tumor cells rely heavily on altered metabolic pathways to support their rapid growth, survival, and progression. Dysregulated metabolic processes, involving carbohydrates, lipids, and amino acids not only fuel tumor development but also contribute to therapy resistance and metastatic potential. By understanding these metabolic changes, key intervention points, such as mutations in genes like RTK, EGFR, RAS, and IDH can be identified, paving the way for novel therapeutic strategies. This review emphasizes the connection between metabolic pathways and clinical challenges, offering actionable insights for future research and therapeutic development in gliomas.

Glycolysis‑related lncRNA may be associated with prognosis and immune activity in grade II‑III glioma

Glucose metabolism, as a novel theory to explain tumor cell behavior, has been intensively studied in various tumors. The present study explored the long non-coding RNAs (lncRNAs) related to glycolysis in grade II-III glioma, aiming to provide a promising target for further research. Pearson correlation analysis was used to identify glycolysis-related lncRNAs. Univariate/multivariate Cox regression analysis and the Least Absolute Shrinkage and Selection Operator algorithm were applied to identify glycolysis-related lncRNAs to construct a prognosis prediction model. Subsequently, multi-dimensional evaluations were used to verify whether the risk model could predict the prognosis and survival rate of patients with grade II-III glioma. Finally, it was verified by functional experiments. The present study finally identified seven glycolysis-related lncRNAs (CRNDE, AC022034.1, RHOQ-AS1, AL159169.2, AL133215.2, AC007098.1 and LINC02587) to construct a prognosis prediction model. The present study further investigated the underlying immune microenvironment, somatic landscape and functional enrichment pathways. Additionally, individualized immunotherapeutic strategies and candidate compounds were identified to guide clinical treatment. The experimental results demonstrated that CRNDE could increase the proliferation of SHG-44 cells. In conclusion, a large sample of human grade II-III glioma in The Cancer Genome Atlas database was used to construct a risk model using glycolysis-related lncRNAs to predict the prognosis of patients with grade II-III glioma.

Prognostic significance and immunoinfiltration analysis of genes associated with epithelial-mesenchymal transition and energy metabolism in bladder urothelial carcinoma

BACKGROUND

Epithelial-mesenchymal transition (EMT) and aberrant energy metabolism are pivotal biological processes in tumor progression, significantly impacting tumor prognosis. However, the relationship between EMT, energy metabolism, and the immune microenvironment in bladder urothelial carcinoma (BLCA) remains inadequately understood.

METHODS

Bladder cancer samples from The Cancer Genome Atlas were categorized into two groups via clustering analysis to elucidate disparities in expression, prognostic significance, and immune infiltration of genes associated with EMT and energy metabolism between these groups. Key genes associated with EMT and energy metabolism in BLCA were identified through Cox multifactorial regression analysis, immune infiltration analysis, etc. Subsequently, their prognostic significance in BLCA was validated.

RESULTS

Cluster analysis revealed significant differences in the expression of genes associated with EMT and energy metabolism between the two groups. Group 2 exhibited significantly improved overall survival and progression-free survival compared to Group 1. Chondroitin sulfate proteoglycan 4 (CSPG4) emerged as the most critical gene associated with EMT, energy metabolism, prognosis, and immune infiltration in BLCA. Immunohistochemical assays demonstrated differential expression of CSPG4 in bladder tumors and normal bladder tissues, with high CSPG4 expression correlating with a poorer BLCA prognosis. Furthermore, CSPG4 exhibited an association with the immune checkpoint molecule programmed death-1 (PD1) in BLCA.

CONCLUSIONS

EMT and energy metabolism exert pivotal influences on the immune microenvironment in BLCA. CSPG4 holds promise as a prognostic biomarker for patients with BLCA, offering valuable insights into potential immunotherapeutic strategies for this patient population.

A Prognostic Model of Bladder Cancer Based on Metabolism-Related Long Non-Coding RNAs

Background

Some studies have revealed a close relationship between metabolism-related genes and the prognosis of bladder cancer. However, the relationship between metabolism-related long non-coding RNAs (lncRNA) regulating the expression of genetic material and bladder cancer is still blank. From this, we developed and validated a prognostic model based on metabolism-associated lncRNA to analyze the prognosis of bladder cancer.

Methods

Gene expression, lncRNA sequencing data, and related clinical information were extracted from The Cancer Genome Atlas (TCGA). And we downloaded metabolism-related gene sets from the human metabolism database. Differential expression analysis is used to screen differentially expressed metabolism-related genes and lncRNAs between tumors and paracancer tissues. We then obtained metabolism-related lncRNAs associated with prognosis by correlational analyses, univariate Cox analysis, and logistic least absolute shrinkage and selection operator (LASSO) regression. A risk scoring model is constructed based on the regression coefficient corresponding to lncRNA calculated by multivariate Cox analysis. According to the median risk score, patients were divided into a high-risk group and a low-risk group. Then, we developed and evaluated a nomogram including risk scores and Clinical baseline data to predict the prognosis. Furthermore, we performed gene-set enrichment analysis (GSEA) to explore the role of these metabolism-related lncRNAs in the prognosis of bladder cancer.

Results

By analyzing the extracted data, our research screened out 12 metabolism-related lncRNAs. There are significant differences in survival between high and low-risk groups divided by the median risk scoring model, and the low-risk group has a more favorable prognosis than the high-risk group. Univariate and multivariate Cox regression analysis showed that the risk score was closely related to the prognosis of bladder cancer. Then we established a nomogram based on multivariate analysis. After evaluation, the modified model has good predictive efficiency and clinical application value. Furthermore, the GSEA showed that these lncRNAs affected bladder cancer prognosis through multiple links.

Conclusions

A predictive model was established and validated based on 12 metabolism-related lncRNAs and clinical information, and we found these lncRNA affected bladder cancer prognosis through multiple links.

A Hypoxia-Related Long Non-Coding RNAs Signature Associated With Prognosis in Lower-Grade Glioma

Accumulating evidence suggests that hypoxia microenvironment and long non-coding lncRNAs (lncRNAs) exert critical roles in tumor development. Herein, we aim to develop a hypoxia-related lncRNA (HRL) model to predict the survival outcomes of patient with lower-grade glioma (LGG). The RNA-sequencing data of 505 LGG samples were acquired from The Cancer Genome Atlas (TCGA). Using consensus clustering based on the expression of hypoxia-related mRNAs, these samples were divided into three subsets that exhibit distinct hypoxia content, clinicopathologic features, and survival status. The differentially expressed lncRNAs across the subgroups were documented as candidate HRLs. With LASSO regression analysis, eight informative lncRNAs were selected for constructing the prognostic HRL model. This signature had a good performance in predicting LGG patients’ overall survival in the TCGA cohort, and similar results could be achieved in two validation cohorts from the Chinese Glioma Genome Atlas. The HRL model also showed correlations with important clinicopathologic characteristics such as patients’ age, tumor grade, IDH mutation, 1p/19q codeletion, MGMT methylation, and tumor progression risk. Functional enrichment analysis indicated that the HLR signature was mainly involved in regulation of inflammatory response, complement, hypoxia, Kras signaling, and apical junction. More importantly, the signature was related to immune cell infiltration, estimated immune score, tumor mutation burden, neoantigen load, and expressions of immune checkpoints and immunosuppressive cytokines. Finally, a nomogram was developed by integrating the HRL signature and clinicopathologic features, with a concordance index of 0.852 to estimate the survival probability of LGG patients. In conclusion, our study established an effective HRL model for prognosis assessment of LGG patients, which may provide insights for future research and facilitate the designing of individualized treatment.