SPTBN1 abrogates renal clear cell carcinoma progression via glycolysis reprogramming in a GPT2-dependent manner

Targeting HVEM-GPT2 axis: a novel approach to T cell activation and metabolic reprogramming in non-small cell lung cancer therapy

BACKGROUND

The modulation of tumor microenvironments through immune checkpoint pathways is pivotal for the development of effective cancer immunotherapies. This study aims to explore the role of HVEM in non-small cell lung cancer (NSCLC) microenvironment.

METHODS

The lung cancer datasets for this study were directly downloaded from The Cancer Genome Atlas (TCGA). Single-cell data were sourced from the Tumor Immune Single-cell Hub (TISCH). Multiplex immunohistochemistry (mIHC) was used to explore the cellular composition and spatial distribution of HVEM in lung cancer immune microenvironment. The immune microenvironment of HVEM KO mice bearing mouse lung cancer cell was also evaluated. Co-cultured system and phenotype assays facilitated the examination of Jurkat T cells' effect on A549 and H1299 lung cancer cells. Quantitative PCR and Western blotting determined gene and protein expression, respectively, cellular respiration was measured through oxygen consumption rate (OCR) assays. Lung cancer cells co-cultured with Jurkat T cells were xenografted into nude mice to evaluate tumor growth and metastatic potential. Next, RNA-seq, COIP, Dual-luciferase reporter experiment, and CHIP-seq were used to explore the potential underlying mechanism.

RESULTS

In our study, we investigated the role of HVEM in the microenvironment of NSCLC and its implications in immunotherapy. Crucially, HVEM, part of the tumor necrosis factor receptor superfamily, influences T cell activation, potentially impacting immunotherapeutic outcomes. Using the TIDE algorithm, our results showcased a link between HVEM levels and immune dysfunction in NSCLC patients. Delving deeper into the NSCLC microenvironment, we found HVEM predominantly expressed in T cell subpopulations. CD8 + HVEM + and CD4 + HVEM + indicated better prognosis in lung adenocarcinoma tissue microarray using multiplex immunohistochemistry. Activated T cells, particularly from the Jurkat cell line, significantly inhibited NSCLC progression, reducing both proliferation and invasion capabilities of A549 and H1299 lung cancer cell lines. In vivo models reinforced these observations. Manipulating HVEM expression revealed its essential role in T cell survival and activation. In addition, animal experiments revealed the importance of HVEM in maintaining activated peripheral immunity and inflamed local tumor microenvironment. Furthermore, our data suggest that HVEM is pivotal in T cell metabolic reprogramming, transitioning from oxidative phosphorylation to aerobic glycolysis. RNA sequencing illuminated a potential relationship between HVEM and GPT2, an enzyme tied to amino acid metabolism and cellular energetics. Subsequent experiments confirmed that HVEM's influence on T cell activation and metabolism is potentially mediated through its regulation of GPT2. In addition, GATA1 was validated to regulate HVEM expression in activated Jurkat T cells.

CONCLUSIONS

Our study establishes that HVEM significantly influences T cell functionality and NSCLC cell dynamics, pinpointing the HVEM-GPT2 axis as a promising target for NSCLC therapy.

The role of BUD31 in clear cell renal cell carcinoma: prognostic significance, alternative splicing, and tumor immune environment

BUD31, a splicing factor, is linked to various cancers. This study examines BUD31's expression, prognostic value, mutation profile, genomic instability, tumor immune environment, and role in clear cell renal cell carcinoma (ccRCC), focusing on cell cycle regulation via alternative splicing. BUD31 expression was analyzed using TCGA and GTEx databases across 33 cancers. Techniques included IHC staining, survival analysis, Cox regression, and nomogram construction. Mutation landscape, genomic instability, and tumor immune microenvironment were evaluated. Functional assays on ccRCC cell lines involved BUD31 knockdown, RNA sequencing, and alternative splicing analysis. BUD31 was upregulated in multiple tumors, including ccRCC. High BUD31 expression correlated with worse survival outcomes and was identified as an independent predictor of poor prognosis in ccRCC. High BUD31 expression also correlated with increased genomic instability and a less active immune microenvironment. BUD31 knockdown inhibited cell proliferation, migration, and invasion in vitro and reduced tumor growth in vivo. RNA sequencing identified 390 alternative splicing events regulated by BUD31, including 17 cell cycle-related genes. KEGG analysis highlighted pathways involved in cell cycle regulation, indicating BUD31's role in promoting cell cycle progression through alternative splicing. BUD31 is upregulated in various tumors and is associated with poor outcomes, increased genomic instability, and a suppressed immune microenvironment in ccRCC. BUD31 promotes cell cycle progression via alternative splicing, suggesting it as a prognostic biomarker and potential therapeutic target in ccRCC.

Metabolism-regulated ferroptosis in cancer progression and therapy

Cancer metabolism mainly includes carbohydrate, amino acid and lipid metabolism, each of which can be reprogrammed. These processes interact with each other to adapt to the complicated microenvironment. Ferroptosis is a regulated cell death induced by iron-dependent lipid peroxidation, which is morphologically different from apoptosis, necrosis, necroptosis, pyroptosis, autophagy-dependent cell death and cuprotosis. Cancer metabolism plays opposite roles in ferroptosis. On the one hand, carbohydrate metabolism can produce NADPH to maintain GPX4 and FSP1 function, and amino acid metabolism can provide substrates for synthesizing GPX4; on the other hand, lipid metabolism might synthesize PUFAs to trigger ferroptosis. The mechanisms through which cancer metabolism affects ferroptosis have been investigated extensively for a long time; however, some mechanisms have not yet been elucidated. In this review, we summarize the interaction between cancer metabolism and ferroptosis. Importantly, we were most concerned with how these targets can be utilized in cancer therapy.

Systematic analysis of glutamine metabolism family genes and exploration of the biological role of GPT in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a malignancy of the digestive system with high incidence rate and mortality, and reliable diagnostic and prognostic markers for CRC are still lacking. Glutamine metabolism is crucial to the occurrence and development of CRC. However, no research has systematically analyzed the biological role of glutamine metabolism-related genes (GMRGs) in CRC.

METHODS

We downloaded gene expression data and clinical data of CRC patients from the TCGA database. The UCSC database downloads pan-cancer gene expression data and prognosis data. Characteristic GMRGs were screened out using differential analysis and two types of machine learning (SVM-REF and RandomForest). Single-cell RNA-sequencing data from CRC patients were downloaded from GEO data. ROC curve was used to evaluate the diagnostic value. Kaplan-Meier method and univariate Cox regression analysis were used to evaluate the prognostic value. The oncopredict package is used to calculate IC50 values for common drugs in CRC patients.

RESULTS

A total of 31 differentially expressed GMRGs were identified, 9 of which were identified as characteristic GMRGs. Further evaluation of diagnostic and prognostic value finally identified GPT as the most important GMRGs in CRC. scRNA-seq analysis revealed that GPT is almost exclusively expressed in epithelial cells. GPT expression is closely related to the tumor microenvironment and can effectively distinguish the sensitivity of different CRC patients to clinical drugs. In addition, pan-cancer analysis showed that GPT is an excellent diagnostic and prognostic marker for multiple cancers.

CONCLUSIONS

GPT is a reliable diagnostic, prognostic marker and therapeutic target in CRC.

Glycolysis-related biomarker TCIRG1 participates in regulation of renal cell carcinoma progression and tumor immune microenvironment by affecting aerobic glycolysis and AKT/mTOR signaling pathway

BACKGROUND

Renal cell carcinoma (RCC) is a hypermetabolic disease. Abnormal up-regulation of glycolytic signaling promotes tumor growth, and glycolytic metabolism is closely related to immunotherapy of renal cancer. The aim of the present study was to determine whether and how the glycolysis-related biomarker TCIRG1 affects aerobic glycolysis, the tumor microenvironment (TME) and malignant progression of clear cell renal cell carcinoma (ccRCC).

METHODS

Based on The Cancer Genome Atlas (TCGA, n = 533) and the glycolysis-related gene set from MSigDB, we identified the glycolysis-related gene TCIRG1 by bioinformatics analysis, analyzed its immunological properties in ccRCC and observed how it affected the biological function and glycolytic metabolism using online databases such as TIMER 2.0, UALCAN, LinkedOmics and in vitro experiments.

RESULTS

It was found that the expression of TCIRG1, was significantly increased in ccRCC tissue, and that high TCIRG1 expression was associated with poor overall survival (OS) and short progression-free interval (PFI). In addition, TCIRG1 expression was highly correlated with the infiltration immune cells, especially CD4+T cell Th1, CD8+T cell, NK cell, and M1 macrophage, and positively correlated with PDCD1, CTLA4 and other immunoinhibitors, CCL5, CXCR3 and other chemokines and chemokine receptors. More importantly, TCIRG1 may regulate aerobic glycolysis in ccRCC via the AKT/mTOR signaling pathway, thereby affecting the malignant progression of ccRCC cell lines.

CONCLUSIONS

Our results demonstrate that the glycolysis-related biomarker TCIRG1 is a tumor-promoting factor by affecting aerobic glycolysis and tumor immune microenvironment in ccRCC, and this finding may provide a new idea for the treatment of ccRCC by combination of metabolic intervention and immunotherapy.