Bioinformatic profiling identifies the glutaminase to be a potential novel cuproptosis-related biomarker for glioma

GLS and GOT2 as prognostic biomarkers associated with dendritic cell and immunotherapy response in breast cancer

Breast cancer is the females' most common cancer. Targeting the immune microenvironment is a new and promising treatment method for breast cancer. Nevertheless, only a small section of patients can profit by immunotherapy, and improving the ability to accurately predict the potential for immunotherapy response is still awaiting further exploration. In this study, we found that the key factors of glutamine metabolism, glutaminase 1 (GLS) and mitochondrial aspartate transaminase (GOT2), showed opposite expression patterns in breast cancer samples. Based on the expression level of GLS and GOT2, we divided the breast cancer samples into two clusters: Cluster 2 showed GLS expressed higher and GOT2 expressed lower, whereas Cluster 1 showed GOT2 expressed higher and GLS expressed lower. GSEA showed that the clusters were related to pathways of immunity. Further analysis showed that Cluster 2 was positively associated with immunity infiltration. Through WGCNA, we identified a module strongly correlated with glutamine metabolism and immunity and identified 11 dendritic cell-associated genes involved in dendritic cell development, maturation, activation and other functions. In addition, Cluster 2 also showed higher immune checkpoint gene expression, which suggest the Cluster 2 had even better response to immunotherapy. The validation dataset could also be clustered into two groups. Cluster 2 (GLS expressed higher and GOT2 expressed lower) of the validation dataset was also positively associated with dendritic cells and a better immunotherapy response. Thus, these data indicate that GLS and GOT2 are prognostic biomarkers which closely related to dendritic cells and better reacted to immunotherapy in breast cancer.

High LYRM4-AS1 predicts poor prognosis in patients with glioma and correlates with immune infiltration

Background

Many researches proved that non-coding RNAs are important in glioma development. We screened the differentially expressed genes through The Cancer Genome Atlas (TCGA) database and identified the molecule LYRM4-AS1 associated with prognosis. As a lncRNA, the expression level and role of LYRM4-AS1 in glioma are inconclusive. Therefore, we attempted to assess the clinical significance, expression and related mechanisms of LYRM4-AS1 in glioma by employing cell experiments and an integrative in silico methodology.

Methods

RNA-seq data were obtained from UCSC XENA and TCGA datasets. The Gene Expression Omnibus (GEO) database was used to download glioma-related expression profile data. The LYRM4-AS1 expression level was evaluated. Survival curves were constructed by the Kaplan-Meier method. Cox regression analysis was used to analyze independent variables. Patients were divided into high and low expression group base on the median LYRM4-AS1 expression value in glioma tissues. The DESeq2 R package was used to identify differentially expressed genes (DEGs) between two different expression LYRM4-AS1 groups. Gene set enrichment analysis (GSEA) was conducted. Next, the single-sample Gene Set Enrichment Analysis (ssGSEA) was done to quantify the immune infiltration of immune cells in glioma tissues. Gene expression profiles for glioma tumor tissues were used to quantify the relative enrichment score for each immune cell. Spearman correlation analysis was used to analyze the correlation between LYRM4-AS1 and biomarkers of immune cells as well as immune checkpoints in glioma. Finally, assays for cell apoptosis, cell viability and wound healing were conducted to evaluate the function on U87 MG and U251 cells after knocking down LYRM4-AS1.

Results

We found that LYRM4-AS1 was upregulated and related to the grade and malignancy of glioma. Survival analyses showed that high expression LYRM4-AS1 patients had poor clinical outcomes (P < 0.01). Cox regression analyses demonstrated that LYRM4-AS1 was an independent risk factor for overall survival (OS) in glioma (HR: 274 1.836; CI [1.278-2.639]; P = 0.001). Enrichment and immune infiltration analysis showed interferon signaling and cytokine-cytokine receptor interaction enriched in the LYRM4-AS1 high-expression phenotype, and LYRM4-AS1 showed significantly positively related to immune infiltration as well as immune checkpoints (P < 0.01). The knockdown of LYRM4-AS1 in U87 MG and U251 cells can inhibit migration and proliferation of cells (P < 0.05).

Conclusions

These findings indicated that the increased LYRM4-AS1 may be useful for the diagnosis and prognosis of glioma and might participate in the immune infiltration.

Impact of Cuproptosis-related markers on clinical status, tumor immune microenvironment and immunotherapy in colorectal cancer: A multi-omic analysis

Background

Cuproptosis, a novel identified cell death form induced by copper, is characterized by aggregation of lipoylated mitochondrial enzymes and the destabilization of Fe-S cluster proteins. However, the function and potential clinical value of cuproptosis and cuproptosis-related biomarkers in colorectal cancer (CRC) remain largely unknown.

Methods

A comprehensive multi-omics (transcriptomics, genomics, and single-cell transcriptome) analysis was performed for identifying the influence of 16 cuproptosis-related markers on clinical status, molecular functions and tumor microenvironment (TME) in CRC. A novel cuproptosis-related scoring system (CuproScore) based on cuproptosis-related markers was also constructed to predict the prognosis of CRC individuals, TME and the response to immunotherapy. In addition, our transcriptome cohort of 15 paired CRC tissue, tissue-array, and various assays in 4 kinds of CRC cell lines in vitro were applied for verification.

Results

Cuproptosis-related markers were closely associated with both clinical prognosis and molecular functions. And the cuproptosis-related molecular phenotypes and scoring system (CuproScore) could distinguish and predict the prognosis of CRC patients, TME, and the response to immunotherapy in both public and our transcriptome cohorts. Besides, the expression, function and clinical significance of these markers were also checked and analyzed in CRC cell lines and CRC tissues in our own cohorts.

Conclusions

In conclusion, we indicated that cuproptosis and CPRMs played a significant role in CRC progression and in modeling the TME. Inducing cuproptosis may be a useful tool for tumor therapy in the future.

Cuproptosis: mechanisms and links with cancers

Cuproptosis was a copper-dependent and unique kind of cell death that was separate from existing other forms of cell death. The last decade has witnessed a considerable increase in investigations of programmed cell death, and whether copper induced cell death was an independent form of cell death has long been argued until mechanism of cuproptosis has been revealed. After that, increasing number of researchers attempted to identify the relationship between cuproptosis and the process of cancer. Thus, in this review, we systematically detailed the systemic and cellular metabolic processes of copper and the copper-related tumor signaling pathways. Moreover, we not only focus on the discovery process of cuproptosis and its mechanism, but also outline the association between cuproptosis and cancers. Finally, we further highlight the possible therapeutic direction of employing copper ion ionophores with cuproptosis-inducing functions in combination with small molecule drugs for targeted therapy to treat specific cancers.

Development and validation of a cuproptosis-associated prognostic model for diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous disease. Therefore, more reliable biomarkers are required to better predict the prognosis of DLBCL. Cuproptosis is a novel identified form of programmed cell death (PCD) that is different from oxidative stress-related cell death (e.g., apoptosis, ferroptosis, and necroptosis) by Tsvetkov and colleagues in a recent study released in Science. Cuproptosis is copper-dependent PCD that is closely tied to mitochondrial metabolism. However, the prognostic value of cuproptosis-related genes (CRGs) in DLBCL remains to be further elucidated. In the present study, we systematically evaluated the molecular changes of CRGs in DLBCL and found them to be associated with prognosis. Subsequently, based on the expression profiles of CRGs, we characterized the heterogeneity of DLBCL by identifying two distinct subtypes using consensus clustering. Two isoforms exhibited different survival, biological functions, chemotherapeutic drug sensitivity, and immune microenvironment. After identifying differentially expressed genes (DEGs) between CRG clusters, we built a prognostic model with the Least absolute shrinkage and selection operator (LASSO) Cox regression analysis and validated its prognostic value by Cox regression analysis, Kaplan-Meier curves, and receiver operating characteristic (ROC) curves. In addition, the risk score can predict clinical characteristics, levels of immune cell infiltration, and prognosis. Furthermore, a nomogram incorporating clinical features and risk score was generated to optimize risk stratification and quantify risk assessment. Compared to the International Prognostic Index (IPI), the nomogram has demonstrated more accuracy in survival prediction. Furthermore, we validated the prognostic gene expression levels through external experiments. In conclusion, cuproptosis-related gene signature can serve as a potential prognostic predictor in DLBCL patients and may provide new insights into cancer therapeutic targets.

A novel prognostic signature of cuproptosis-related genes and the prognostic value of FDX1 in gliomas

Background: Gliomas are the most common malignant tumors of the central nervous system, with extremely bad prognoses. Cuproptosis is a novel form of regulated cell death. The impact of cuproptosis-related genes on glioma development has not been reported. Methods: The TCGA, GTEx, and CGGA databases were used to retrieve transcriptomic expression data. We employed Cox's regressions to determine the associations between clinical factors and cuproptosis-related gene expression. Overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) were evaluated using the Kaplan-Meier method. We also used the least absolute shrinkage and selection operator (LASSO) regression technique. Results: The expression levels of all 10 CRGs varied considerably between glioma tumors and healthy tissues. In glioma patients, the levels of CDKN2A, FDX1, DLD, DLAT, LIAS, LIPT1, and PDHA1 were significantly associated with the OS, disease-specific survival, and progression-free interval. We used LASSO Cox's regression to create a prognostic model; the risk score was (0.882340) *FDX1 expression + (0.141089) *DLD expression + (-0.333875) *LIAS expression + (0.356469) *LIPT1 expression + (-0.123851) *PDHA1 expression. A high-risk score/signature was associated with poor OS (hazard ratio = 3.50, 95% confidence interval 2, -4.55, log-rank p < 0.001). Cox's regression revealed that the FDX1 level independently predicted prognosis; FDX1 may control immune cell infiltration of the tumor microenvironment. Conclusion: The CRG signature may be prognostic in glioma patients, and the FDX1 level may independently predict glioma prognosis. These data may afford new insights into treatment.