Identification of a Three-Glycolysis-Related lncRNA Signature Correlated With Prognosis and Metastasis in Clear Cell Renal Cell Carcinoma

Glycolysis-associated lncRNAs in cancer energy metabolism and immune microenvironment: a magic key

The dependence of tumor cells on glycolysis provides essential energy and raw materials for their survival and growth. Recent research findings have indicated that long chain non-coding RNAs (LncRNAs) have a key regulatory function in the tumor glycolytic pathway and offer new opportunities for cancer therapy. LncRNAs are analogous to a regulatory key during glycolysis. In this paper, we review the mechanisms of LncRNA in the tumor glycolytic pathway and their potential therapeutic strategies, including current alterations in cancer-related energy metabolism with lncRNA mediating the expression of key enzymes, lactate production and transport, and the mechanism of interaction with transcription factors, miRNAs, and other molecules. Studies targeting LncRNA-regulated tumor glycolytic pathways also offer the possibility of developing new therapeutic strategies. By regulating LncRNA expression, the metabolic pathways of tumor cells can be interfered with to inhibit tumor growth and metastasis, thus affecting the immune and drug resistance mechanisms of tumor cells. In addition, lncRNAs have the capacity to function as molecular markers and target therapies, thereby contributing novel strategies and approaches to the field of personalized cancer therapy and prognosis evaluation. In conclusion, LncRNA, as key molecules regulating the tumor glycolysis pathway, reveals a new mechanism of abnormal metabolism in cancer cells. Future research will more thoroughly investigate the specific mechanisms of LncRNA glycolysis regulation and develop corresponding therapeutic strategies, thereby fostering new optimism for the realization of precision medicine.

Established and emerging biomarkers of immunotherapy in renal cell carcinoma

Immunotherapies, such as immune checkpoint inhibitors, have heralded impressive progress for patient care in renal cell carcinoma (RCC). Despite this success, some patients' disease fails to respond, and other patients experience significant side effects. Thus, development of biomarkers is needed to ensure that patients can be selected to maximize benefit from immunotherapies. Improving clinicians' ability to predict which patients will respond to immunotherapy and which are most at risk of adverse events - namely through clinical biomarkers - is indispensable for patient safety and therapeutic efficacy. Accordingly, an evolving suite of therapeutic biomarkers continues to be investigated. This review discusses biomarkers for immunotherapy in RCC, highlighting current practices and emerging innovations, aiming to contribute to improved outcomes for patients with RCC.

Exosomes in renal cell carcinoma: challenges and opportunities

Renal cell carcinoma (RCC) is the most common type of kidney cancer that accounts for approximately 2-3% of adult malignancies. Among the primary treatment methods for this type of cancer are surgery and targeted treatment. Still, due to less than optimal effectiveness, there are problems such as advanced distant metastasis, delayed diagnosis, and drug resistance that continue to plague patients. In recent years, therapeutic advances have increased life expectancy and effective treatment in renal cell carcinoma patients. One of these methods is the use of stem cells. Although the therapeutic effects of stem cells, especially mesenchymal stem cells, are still impressive, today, extracellular vesicles (EVs) as carrying molecules and various mediators in intercellular communications, having a central role in tumorigenesis, metastasis, immune evasion, and drug response, and on the other hand, due to its low immunogenicity and strong regulatory properties of the immune system, has received much attention from researchers and doctors. Despite the increasing interest in exosomes as the most versatile type of EVs, the heterogeneity of their efficacy presents challenges and, on the other hand, exciting opportunities for diagnostic and clinical interventions.In the upcoming article, we will review the various aspects of exosomes' effects in the prevention, treatment, and progress of renal cell carcinoma and also ways to optimize them to strengthen their positive sides.

Guidelines on lung adenocarcinoma prognosis based on immuno-glycolysis-related genes

OBJECTIVES

This study developed a new model for risk assessment of immuno-glycolysis-related genes for lung adenocarcinoma (LUAD) patients to predict prognosis and immunotherapy efficacy.

METHODS

LUAD samples and data obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases are used as training and test columns, respectively. Twenty-two (22) immuno-glycolysis-related genes were screened, the patients diagnosed with LUAD were divided into two molecular subtypes by consensus clustering of these genes. The initial prognosis model was developed using the multiple regression analysis method and Receiver Operating characteristic (ROC) analysis was used to verify its predictive potential. Gene set enrichment analysis (GSEA) showed the immune activities and pathways in different risk populations, we calculated immune checkpoints, immune escape, immune phenomena (IPS), and tumor mutation burden (TMB) based on TCGA datasets. Finally, the relationship between the model and drug sensitivity was analyzed.

RESULTS

Fifteen (15) key differentially expressed genes (DEGs) with prognostic value were screened and a new prognostic model was constructed. Four hundred and forty-three (443) samples were grouped into two different risk cohorts based on median model risk values. It was observed that survival rates in high-risk groups were significantly low. ROC curves were used to evaluate the model's accuracy in determining the survival time and clinical outcome of LUAD patients. Cox analysis of various clinical factors proved that the risk score has great potential as an independent prognostic factor. The results of immunological analysis can reveal the immune infiltration and the activity of related functions in different pathways in the two risk groups, and immunotherapy was more effective in low-risk patients. Most chemotherapeutic agents are more sensitive to low-risk patients, making them more likely to benefit.

CONCLUSION

A novel prognostic model for LUAD patients was established based on IGRG, which could more accurately predict the prognosis and an effective immunotherapy approach for patients.

Prediction of the Prognosis of Clear Cell Renal Cell Carcinoma by Cuproptosis-Related lncRNA Signals Based on Machine Learning and Construction of ceRNA Network

Background

Clear cell renal cell carcinoma's (ccRCC) occurrence and development are strongly linked to the metabolic reprogramming of tumors, and thus far, neither its prognosis nor treatment has achieved satisfying clinical outcomes.

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively, provided us with information on the RNA expression of ccRCC patients and their clinical data. Cuproptosis-related genes (CRGS) were discovered in recent massive research. With the help of log-rank testing and univariate Cox analysis, the prognostic significance of CRGS was examined. Different cuproptosis subtypes were identified using consensus clustering analysis, and GSVA was used to further investigate the likely signaling pathways between various subtypes. Univariate Cox, least absolute shrinkage and selection operator (Lasso), random forest (RF), and multivariate stepwise Cox regression analysis were used to build prognostic models. After that, the models were verified by means of the C index, Kaplan-Meier (K-M) survival curves, and time-dependent receiver operating characteristic (ROC) curves. The association between prognostic models and the tumor immune microenvironment as well as the relationship between prognostic models and immunotherapy were next examined using ssGSEA and TIDE analysis. Four online prediction websites-Mircode, MiRDB, MiRTarBase, and TargetScan-were used to build a lncRNA-miRNA-mRNA ceRNA network.

Results

By consensus clustering, two subgroups of cuproptosis were identified that represented distinct prognostic and immunological microenvironments.

Conclusion

A prognostic risk model with 13 CR-lncRNAs was developed. The immune microenvironment and responsiveness to immunotherapy are substantially connected with the model, which may reliably predict the prognosis of patients with ccRCC.

The Role of Long Noncoding RNA (lncRNAs) Biomarkers in Renal Cell Carcinoma

Renal cell carcinoma is one of the common cancers whose incidence and mortality are continuously growing worldwide. Initially, this type of tumour is usually asymptomatic. Due to the lack of reliable diagnostic markers, one-third of ccRCC patients already have distant metastases at the time of diagnosis. This underlines the importance of establishing biomarkers that would enable the prediction of the disease's course and the risk of metastasis. LncRNA, which modulates genes at the epigenetic, transcriptional, and post-transcriptional levels, appears promising. The actions of lncRNA involve sponging and sequestering target miRNAs, thus affecting numerous biological processes. Studies have confirmed the involvement of RNAs in various diseases, including RCC. In this review, we focused on MALAT1 (a marker of serious pathological changes and a factor in the promotion of tumorigenesis), RCAT1 (tumour promoter in RCC), DUXAP9 (a plausible marker of localized ccRCC), TCL6 (exerting tumour-suppressive effects in renal cancer), LINC00342 (acting as an oncogene), AGAP2 Antisense1 (plausible predictor of RCC progression), DLEU2 (factor promoting tumours growth via the regulation of epithelial-mesenchymal transition), NNT-AS1 (sponge of miR-22 contributing to tumour progression), LINC00460 (favouring ccRCC development and progression) and Lnc-LSG1 (a factor that may stimulate ccRCC metastasis).

Construction of a prognostic glycolysis-related lncRNA signature for patients with colorectal cancer

Aerobic glycolysis is a common metabolic phenotype in tumors that helps cancer cells adjust to severe living conditions and can aid metastasis in several types of carcinomas, including colorectal cancer (CRC). Long non-coding RNAs (lncRNAs) can influence tumor biology and have been previously used to assess patients' outcomes and to identify potential therapeutic targets. However, despite the importance of glycolysis-related lncRNAs (GRLs) in the development of CRC, studies on their use as prognostic markers are still limited. Herein, we applied a series of bioinformatic analyses to screen potential prognostic lncRNAs for colorectal cancer. Out of all lncRNAs screened, nine GRLs were selected to constitute a prognostic signature. Based on the signature, two molecular subtypes were classified with distinct prognostic outcomes and excellent diagnostic accuracy (The 1-, 3- and 5-year AUC are 0.756, 0.716, and 0.721, respectively). The prognostic value of this signature was further validated using another cohort. The enriched molecular pathways, immune infiltration, and mutation landscape were also significantly different between the two groups. The different drug sensitivity results between the two groups suggest a potential strategy for precise treatment. Furthermore, we confirmed that AFAP1-AS1 could regulate aerobic glycolysis and metastasis of CRC cells. Overall, we developed a glycolysis-related lncRNA (GRL) signature and suggested that this signature could offer a predictive value and identify potential therapeutic targets for cancer therapy.