A glycolysis-related gene signatures in diffuse large B-Cell lymphoma predicts prognosis and tumor immune microenvironment

MicroRNAs in Diffuse Large B-Cell Lymphoma (DLBCL): Biomarkers with Prognostic Potential

Background/Objectives: The heterogeneity of diffuse large B-cell lymphoma (DLBCL) based on differences in both genetic and epigenetic factors contributes to the dynamics of tumor growth and efficacy of cytoreductive therapy, as well as considerably affecting disease prognosis. This study aimed to detect microRNAs (miRNAs) capable of improving prognostic accuracy in DLBCL patients. Methods: We performed miRNA sequencing in bone marrow (BM) samples collected from DLBCL patients. Next, the expression levels of miRNAs in lymph node (LN) samples (n = 43) and BM samples (n = 70) were analyzed by real-time RT-PCR in the group of DLBCL patients. Results: It was found that the expression levels of miRNA-10b, -100, -125a, -125b, -126, -143, -23a and let-7a were statistically significantly reduced in the group of DLBCL patients who had a poor prognosis compared to DLBCL patients with a favorable prognosis (p < 0.05). Kaplan-Meier survival analysis demonstrated that the upregulated expression of miRNA-23a, miRNA-125a, and miRNA-100 was associated with better overall survival in DLBCL patients. A statistically significant elevation in the expression levels of miRNA-151a, miRNA-148b and miRNA-192 in the BM samples was observed for DLBCL patients both with and without BM involvement compared to BM samples from non-cancerous blood disease (NCBD) patients (p < 0.05). Statistically significant upregulation of PD-L1, TIMP1, TOP2A, and TP53 was observed in BM samples from DLBCL patients with and without BM involvement in comparison with BM samples from NCBD patients (p < 0.05). Conclusions: miRNA-23a, miRNA-125a, and miRNA-100 were shown to be potential prognostically significant biomarkers in DLBCL patients. Changes in expression levels of miRNA-151a, miRNA-148b, miRNA-192, PD-L1, TIMP1, TOP2A, and TP53 reflect alterations in the BM without morphological or immunophenotypic signs of a DLBCL-related BM pathology.

Immune gene features and prognosis in colorectal cancer: insights from ssGSEA typing

BACKGROUND

Colorectal cancer (CRC) is a molecularly heterogeneous disease, and its treatment and prognosis vary greatly among subgroups. Therefore, it is necessary to identify prognostic factors associated with the biological heterogeneity of CRC in order to improve patients' survival expectations.

METHODS

We obtained and merged RNA-Seq data along with clinical details for colorectal cancer (CRC) from The Cancer Genome Atlas (TCGA) repository, and then performed immunocluster typing on all CRC specimens. We conducted differential expression gene (DEG) analysis, gene set enrichment analysis (GSEA), and tumor microenvironment (TME) analysis on CRC samples that were divided into high and low Immunity categories. Moreover, we pinpointed prognostic genes from immune-related gene (IRGs) sets, developed a prognostic risk model, and executed survival analysis, receiver operating characteristic (ROC) curve analysis, and independent prognostic analysis. Additionally, we assessed the risk for patients categorized into high- and low-risk groups based on the model. Lastly, we created a Nomogram to customize the prediction of survival outcomes in CRC patients.

RESULTS

CRC samples were divided into high and low Immunity groups based on the median value of the immunity score. Between the two groups, a total of 1550 DEGs were identified and 395 differentially expressed immune-related genes (DE-IRGs) were identified by intersection with 2483 IRGs. The DE-IRGs of the high Immunity group were dominated by Cytokine receptor interactions, chemokine signaling pathways and immune cell-mediated cytotoxicity, and molecule function of immune effector process. TME analysis showed that most of the 27 immune cells and functions were highly enriched in high Immunity group, whose Immune Score, Stromal Score and ESTIMATE Score were significantly higher. Subsequently, a prognostic risk model of CRC was constructed based on 12 prognostic genes, and the accuracy and reliability of the model prediction were verified. Finally, Nomogram enabled accurate individual prediction of the survival prognosis of CRC patients.

CONCLUSIONS

Our study develops an immune-related prognostic model and Nomogram that reliably predicts survival outcomes in CRC patients and enhances understanding of the tumor immunity and molecular mechanisms of CRC.

LMP1 enhances aerobic glycolysis in natural killer/T cell lymphoma

Natural killer/T cell lymphoma (NKTCL) exhibits highly aggressive clinical behavior, and the outcomes for relapsed/refractory patients are still poor. Recently, the mechanism underlying the effect of Epstein-Barr virus (EBV) infection, which has not been fully defined in NKTCL, has attracted great attention. We explored how LMP1 promoted aerobic glycolysis via metabolic sequencing combined with mRNA sequencing and immunoprecipitation coupled to mass spectrometry. Experimental assays were used to determine the effects of LMP1 and its downstream pathway on the function and glucose metabolism of NKTCL cells. The correlations between LMP1 expression in patients and their clinical features, treatment response, and prognosis were analyzed. Results show that LMP1 enhances NKTCL cell proliferation in vitro and in vivo, inhibits apoptosis, and decreases gemcitabine sensitivity. In addition, LMP1 also enhances aerobic glycolysis in NKTCL cells, as indicated by increases in glucose uptake, lactate production, and extracellular acidification rate. Clinically, LMP1 expression is correlated with risk stratification, treatment response, and prognosis, and higher LMP1 expression indicates greater SUVmax for NKTCL patients. Mechanistically, LMP1 competitively binds to TRAF3 to promote cell proliferation and aerobic glycolysis by regulating the noncanonical NF-κB pathway. The application of an NF-κB pathway inhibitor or reactivation of the NF-κB pathway affects aerobic glycolysis and the biological function of NKTCL cells. In summary, this study is the first to describe and define in detail how LMP1 affects glucose metabolism in NKTCL and might provide a novel perspective for further treatment.

Should we use nomograms for risk predictions in diffuse large B cell lymphoma patients? A systematic review

Models based on risk stratification are increasingly reported for Diffuse large B cell lymphoma (DLBCL). Due to a rising interest in nomograms for cancer patients, we aimed to review and critically appraise prognostic models based on nomograms in DLBCL patients. A literature search in PubMed/Embase identified 59 articles that proposed prognostic models for DLBCL by combining parameters of interest (e.g., clinical, laboratory, immunohistochemical, and genetic) between January 2000 and 2024. Of them, 40 studies proposed different gene expression signatures and incorporated them into nomogram-based prognostic models. Although most studies assessed discrimination and calibration when developing the model, many lacked external validation. Current nomogram-based models for DLBCL are mainly developed from publicly available databases, lack external validation, and have no applicability in clinical practice. However, they may be helpful in individual patient counseling, although careful considerations should be made regarding model development due to possible limitations when choosing nomograms for prognostication.

Diffuse large B-cell lymphoma: the significance of CD8+ tumor-infiltrating lymphocytes exhaustion mediated by TIM3/Galectin-9 pathway

BACKGROUND

Overexpression of T-cell immunoglobulin and mucin domain-containing protein 3 (TIM3) is related to the exhaustion of CD8+ tumor-infiltrating lymphocytes (TILs) in diffuse large B-cell lymphoma (DLBCL). However, the mechanism of TIM3-mediated CD8+TILs exhaustion in DLBCL remains poorly understood. Therefore, we aimed to clarify the potential pathway involved in TIM3-mediated CD8+TILs exhaustion and its significance in DLBCL.

METHODS

The expression of TIM3 and its correlation with CD8+TILs exhaustion, the key ligand of TIM3, and the potential pathway of TIM3-mediated CD8+TILs exhaustion in DLBCL were analyzed using single-cell RNA sequencing and validated by RNA sequencing. The biological significance of TIM3-related pathway in DLBCL was investigated based on RNA sequencing, immunohistochemistry, and reverse transcription-quantitative polymerase chain reaction data. Finally, the possible regulatory mechanism of TIM3-related pathway in DLBCL was explored using single-cell RNA sequencing and RNA sequencing.

RESULTS

Our results demonstrated that CD8+TILs, especially the terminally exhausted state, were the major clusters that expressed TIM3 in DLBCL. Galectin-9, mainly expressed in M2 macrophages, is the key ligand of TIM3 and can induce the exhaustion of CD8+TILs through TIM3/Galectin-9 pathway. Meanwhile, high TIM3/Galectin-9 enrichment is related to immunosuppressive tumor microenvironment, severe clinical manifestations, inferior prognosis, and poor response to CHOP-based chemotherapy, and can predict the clinical efficacy of immune checkpoint blockade therapy in DLBCL. Furthermore, the TIM3/Galectin-9 enrichment in DLBCL may be regulated by the IFN-γ signaling pathway.

CONCLUSIONS

Our study highlights that TIM3/Galectin-9 pathway plays a crucial role in CD8+TILs exhaustion and the immune escape of DLBCL, which facilitates further functional studies and could provide a theoretical basis for the development of novel immunotherapy in DLBCL.

GPX4 is a potential diagnostic and therapeutic biomarker associated with diffuse large B lymphoma cell proliferation and B cell immune infiltration

At present, GPX4's role in the occurrence and development of diffuse large B lymphoma (DLBCL) is rarely reported. This study's purpose is to explore GPX4's significance in the diagnosis, treatment, and pathological mechanisms of DLBCL. The TIMER 2.0, GEPIA, and GEO databases were used to analyze GPX4's expression levels in DLBCL tissue, peripheral blood, and single cells, and evaluate its potential performance as a therapeutic and diagnostic marker. Cell experiments validate GPX4's role in DLBCL cells. And revealed the potential mechanism of GPX4's action from three aspects: immunity, pathogenic gene expression, and protein interaction. The results indicate that GPX4 can be used as a biomarker for treatment and diagnosis (FC > 1.5, P < 0.05, AUC>0.8, KM-P value < 0.05). In single cell data, GPX4 also showed high expression in immune cells. Besides, cell experiments have confirmed that GPX4's high expression can inhibit DLBCL cells' proliferation. Meanwhile, we found a negative correlation between GPX4 and the 16 core DLBCL's pathogenic genes, and a significant negative correlation with immune B cell infiltration. In summary, GPX4 can serve as a potential therapeutic and diagnostic marker for DLBCL. GPX4's high expression can lead to a good prognosis in DLBCL patients, which may be related to its inhibition of cancer cell proliferation, high expression of key pathogenic genes, and infiltration of immune B cells.

Comprehensive analysis of the prognostic implication and immune infiltration of CISD2 in diffuse large B-cell lymphoma

Background

Diffuse large B-cell lymphoma (DLBCL) is the most common B-cell lymphoma in adults. CDGSH iron sulfur domain 2 (CISD2) is an iron-sulfur protein and plays a critical role of cell proliferation. The aberrant expression of CISD2 is associated with the progression of multiple cancers. However, its role in DLBCL remains unclear.

Methods

The differential expression of CISD2 was identified via public databases, and quantitative real-time PCR (qRT-PCR) and western blot were used to identifed the expression of CISD2. We estimated the impact of CISD2 on clinical prognosis using the Kaplan-Meier plotter. Meanwhile, the drug sensitivity of CISD2 was assessed using CellMiner database. The 100 CISD2-related genes from STRING obtained and analyzed using the LASSO Cox regression. A CISD2 related signature for risk model (CISD2Risk) was established. The PPI network of CISD2Risk was performed, and functional enrichment was conducted through the DAVID database. The impacts of CISD2Risk on clinical features were analyzed. ESTIMATE, CIBERSORT, and MCP-counter algorithm were used to identify CISD2Risk associated with immune infiltration. Subsequently, Univariate and multivariate Cox regression analysis were applied, and a prognostic nomogram, accompanied by a calibration curve, was constructed to predict 1-, 3-, and 5-years survival probabilities.

Results

CISD2 was upregulated in DLBCL patients comparing with normal controls via public datasets, similarly, CISD2 was highly expressed in DLBCL cell lines. Overexpression of CISD2 was associated with poor prognosis in DLBCL patients based on the GSE31312, the GSE32918, and GSE93984 datasets (P<0.05). Nine drugs was considered as a potential therapeutic agents for CISD2. By using the LASSO cox regression, twenty seven genes were identified to construct CISD2Risk, and biological functions of these genes might be involved in apoptosis and P53 signaling pathway. The high CISD2Risk value had a worse prognosis and therapeutic effect (P<0.05). The higher stromal score, immune score, and ESTIMATE score were associated with lowe CISD2Risk value, CISD2Risk was negatively correlated with several immune infiltrating cells (macrophages M0 and M1, CD8 T cells, CD4 naïve T cells, NK cell, etc) that might be correlated with better prognosis. Additionally, The high CISD2Risk was identified as an independent prognostic factor for DLBCL patients using both univariate and multivariate Cox regression. The nomogram produced accurate predictions and the calibration curves were in good agreement.

Conclusion

Our study demonstrates that high expression of CISD2 in DLBCL patients is associated with poor prognosis. We have successfully constructed and validated a good prognostic prediction and efficacy monitoring for CISD2Risk that included 27 genes. Meanwhile, CISD2Risk may be a promising evaluator for immune infiltration and serve as a reference for clinical decision-making in DLBCL patients.

A novel angiogenesis-related scoring model predicts prognosis risk and treatment responsiveness in diffuse large B-cell lymphoma

Diffuse large B cell lymphoma (DLBCL) is a highly heterogeneous disease with varying therapeutic responses and prognoses. Angiogenesis is a crucial factor in lymphoma growth and progression, but no scoring model based on angiogenesis-related genes (ARGs) has been developed for prognostic evaluation of DLBCL patients. In this study, we used univariate Cox regression to identify prognostic ARGs and found two distinct clusters of DLBCL patients in the GSE10846 dataset based on the expression of these prognostic ARGs. These two clusters had different prognoses and immune cell infiltration. Using LASSO regression analysis, we constructed a novel seven-ARG-based scoring model in GSE10846 dataset, and it was further validated in the GSE87371 dataset. The DLBCL patients were divided into high- and low-score groups based on the median risk score as a cut-off. The high-score group had a worse prognosis and showed higher expression of immune checkpoints, M2 macrophages, myeloid-derived suppressor cells, and regulatory T cells, indicating a stronger immunosuppressive environment. DLBCL patients in high-score group were resistant to doxorubicin and cisplatin, which are components of frequently used chemotherapy regimens, but more sensitive to gemcitabine and temozolomide. Using RT-qPCR, we found that two candidate risk genes, RAPGEF2 and PTGER2, were over-expressed in DLBCL tissues compared with control tissues. Taken together, the ARG-based scoring model provides a promising direction for the prognosis and immune status of DLBCL patients, and benefits the development of personalized treatment for DLBCL patients.

A comprehensive pan-cancer analysis identifies a novel glycolysis score and its hub genes as prognostic and immunological biomarkers

Background

Glycolysis plays significant roles in tumor progression and immune response. However, the exact role of glycolysis in prognosis and immune regulation has not been explored in all cancer types. This study first calculated a novel glycolysis score and screened out 12 glycolytic hub genes, and comprehensively analyzed molecular expression, clinical implications, and immune features of glycolysis among pan-cancer.

Methods

The glycolysis score was derived by the single sample gene set enrichment analysis (ssGSEA) algorithm. The correlations of glycolysis with clinical parameters were analyzed using "limma" package. Downstream pathways of glycolysis were identified by Gene Set Enrichment Analysis (GSEA). The immune cell infiltration was explored and validated by three databases. The association between glycolysis and some immunotherapy biomarkers was explored by Pearson correlation analysis. Single-nucleotide variation (SNV), copy number variation (CNV), DNA methylation, and drug sensitivity analyses of 12 glycolytic hub genes were investigated. IMvigor210 and GSE91061 immunotherapeutic cohorts were retrieved to assess the ability of glycolysis score to predict immunotherapy efficacy. The expression of glycolysis key genes was detected in normal and endometrial cancer cell lines.

Results

We found that glycolysis score was generally higher in tumor tissues compared to normal tissues and a high glycolysis score predominated as a risk prognostic factor. A high glycolysis score was associated with decreased immunostimulatory natural killer (NK) cells and CD8+ T cells infiltration, well increased immunosuppressive M2-tumor-associated macrophages (M2-TAM) cells infiltration. Tumor mutational burden (TMB), microsatellite instability (MSI), and immune checkpoints (ICPs) all closely interacted with glycolysis score and the frequency of gene mutation was confirmed to be higher in colon adenocarcinoma (COAD) patients with higher glycolysis score. The SNV, CNV, and DNA methylation of 12 glycolysis key genes occurred at different frequencies and showed different impacts on survival outcomes. The predictive and prognostic value of glycolysis score for immunotherapy outcomes was validated in two immunotherapy cohorts. The expression levels of key genes differ in normal endometrial and three endometrial cancer cell lines.

Conclusions

This work indicated that glycolysis score and 12 glycolytic hub genes were correlated with an immunosuppressive microenvironment. They could be served as promising biomarkers aiding diagnosis, predicting prognosis and immunotherapy response for some tumor patients.