Immune microenvironment-related gene mapping predicts immunochemotherapy response and prognosis in diffuse large B-cell lymphoma

High Expression of GPR183 Predicts Poor Survival in Cytogenetically Normal Acute Myeloid Leukemia

Acute myeloid leukemia (AML) with a normal karyotype (CN-AML) constitutes approximately 50% of all AML cases, presenting significant prognostic variability, and highlighting the urgent need for the identification of novel molecular biomarkers. In this study, we systematically assessed GPR183 expression levels using qRT-PCR in our clinical follow-up study which included 283 CN-AML patients. Using Kaplan-Meier analysis, we found that patients with high GPR183 expression levels exhibited significantly worse overall survival (OS) (P = 0.046) and event-free survival (EFS) (P = 0.030) compared to those with low GPR183 expression. Comprehensive univariate and multivariate Cox regression analyses confirmed that GPR183 expression is a prognostic factor for OS and EFS (P < 0.05). To further validate these findings, we analyzed an independent cohort of 104 CN-AML patients from the GSE71014 dataset, corroborating our primary results, and indicating that high GPR183 expression is associated with poorer survival outcomes. Additionally, RNA-seq data from the GSE71014 dataset were analyzed by Gene Set Enrichment Analysis (GSEA). The results suggested that GPR183 may influence disease progression through the activation of the "TNFa Signaling Via NF-κB" pathway. Collectively, these findings suggested that GPR183 could serve as a valuable prognostic biomarker in CN-AML, offering insights into the underlying mechanisms of disease progression.

S100A8 is a prognostic signature and associated with immune response in diffuse large B-cell lymphoma

Background

S100A8, a calcium-binding protein belonging to the S100 family, is involved in immune responses and multiple tumor pathogens. Diffuse large B-cell lymphoma (DLBCL) is one of the most common types of B-cell lymphoma and remains incurable in 40% of patients. However, the role of S100A8 and its regulation of the immune response in DLBCL remain unclear.

Methods

The differential expression of S100A8 was identified via the GEO and TCGA databases. The prognostic role of S100A8 in DLBCL was calculated using the Kaplan-Meier curve. The function enrichment of differentially expressed genes (DEGs) was explored through GO, KEGG, GSEA, and PPI analysis. In our cohort, the expression of S100A8 was verified. Meanwhile, the biological function of S100A8 was applied after the inhibition of S100A8 in an in vitro experiment. The association between S100A8 and immune cell infiltration and treatment response in DLBCL was analyzed.

Results

S100A8 was significantly overexpressed and related to a poor prognosis in DLBCL patients. Function enrichment analysis revealed that DEGs were mainly enriched in the IL-17 signaling pathway. Our cohort also verified this point. In vitro experiments suggested that inhibition of S100A8 should promote cell apoptosis and suppress tumor growth. Single-cell RNA sequence analysis indicated that S100A8 might be associated with features of the tumor microenvironment (TME), and immune infiltration analyses discovered that S100A8 expression was involved in TME. In terms of drug screening, we predicted that many drugs were associated with preferable sensitivity.

Conclusion

Elevated S100A8 expression is associated with a poor prognosis and immune infiltration in DLBCL. Inhibition of S100A8 could promote cell apoptosis and suppress tumor growth. Meanwhile, S100A8 has the potential to be a promising immunotherapeutic target for patients with DLBCL.

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.

Predicting leukemic transformation in myelodysplastic syndrome using a transcriptomic signature

Background: For prediction on leukemic transformation of MDS patients, emerging model based on transcriptomic datasets, exhibited superior predictive power to traditional prognostic systems. While these models were lack of external validation by independent cohorts, and the cell origin (CD34+ sorted cells) limited their feasibility in clinical practice. Methods: Transformation associated co-expressed gene cluster was derived based on GSE58831 ('WGCNA' package, R software). Accordingly, the least absolute shrinkage and selection operator algorithm was implemented to establish a scoring system (i.e., MDS15 score), using training set (GSE58831 originated from CD34+ cells) and testing set (GSE15061 originated from unsorted cells). Results: A total of 68 gene co-expression modules were derived, and the 'brown' module was recognized to be transformation-specific (R2 = 0.23, p = 0.005, enriched in transcription regulating pathways). After 50,000-times LASSO iteration, MDS15 score was established, including the 15-gene expression signature. The predictive power (AUC and Harrison's C index) of MDS15 model was superior to that of IPSS/WPSS in both training set (AUC/C index 0.749/0.777) and testing set (AUC/C index 0.933/0.86). Conclusion: By gene co-expression analysis, the crucial gene module was discovered, and a novel prognostic system (MDS15) was established, which was validated not only by another independent cohort, but by a different cell origin.

A prognostic 15-gene model based on differentially expressed genes among metabolic subtypes in diffuse large B-cell lymphoma

The outcomes of patients with diffuse large B-cell lymphoma (DLBCL) vary widely, and about 40% of them could not be cured by the standard first-line treatment, R-CHOP, which could be due to the high heterogeneity of DLBCL. Here, we aim to construct a prognostic model based on the genetic signature of metabolic heterogeneity of DLBCL to explore therapeutic strategies for DLBCL patients. Clinical and transcriptomic data of one training and four validation cohorts of DLBCL were obtained from the GEO database. Metabolic subtypes were identified by PAM clustering of 1,916 metabolic genes in the 7 major metabolic pathways in the training cohort. DEGs among the metabolic clusters were then analyzed. In total, 108 prognosis-related DEGs were identified. Through univariable Cox and LASSO regression analyses, 15 DEGs were used to construct a risk score model. The overall survival (OS) and progression-free survival (PFS) of patients with high risk were significantly worse than those with low risk (OS: HR 2.86, 95%CI 2.04-4.01, p < 0.001; PFS: HR 2.42, 95% CI 1.77-3.31, p < 0.001). This model was also associated with OS in the four independent validation datasets (GSE10846: HR 1.65, p = 0.002; GSE53786: HR 2.05, p = 0.02; GSE87371: HR 1.85, p = 0.027; GSE23051: HR 6.16, p = 0.007) and PFS in the two validation datasets (GSE87371: HR 1.67, p = 0.033; GSE23051: HR 2.74, p = 0.049). Multivariable Cox analysis showed that in all datasets, the risk model could predict OS independent of clinical prognosis factors (p < 0.05). Compared with the high-risk group, patients in the low-risk group predictively respond to R-CHOP (p = 0.0042), PI3K inhibitor (p < 0.05), and proteasome inhibitor (p < 0.05). Therefore, in this study, we developed a signature model of 15 DEGs among 3 metabolic subtypes, which could predict survival and drug sensitivity in DLBCL patients.

CD3D Is an Independent Prognostic Factor and Correlates With Immune Infiltration in Gastric Cancer

The protein encoded by CD3D is part of the T-cell receptor/CD3 complex (TCR/CD3 complex) and is involved in T-cell development and signal transduction. Previous studies have shown that CD3D is associated with prognosis and treatment response in breast, colorectal, and liver cancer. However, the expression and clinical significance of CD3D in gastric cancer are not clear. In this study, we collected 488 gastric cancer tissues and 430 paired adjacent tissues to perform tissue microarrays (TMAs). Then, immunohistochemical staining of CD3D, CD3, CD4, CD8 and PD-L1 was conducted to investigate the expression of CD3D in gastric cancer and the correlation between the expression of CD3D and tumor infiltrating lymphocytes (TILs) and PD-L1. The results showed that CD3D was highly expressed in gastric cancer tissues compared with paracancerous tissues (P<0.000). Univariate and multivariate analyses showed that CD3D was an independent good prognostic factor for gastric cancer (P=0.004, HR=0.677, 95%CI: 0.510-0.898 for univariate analyses; P=0.046, HR=0.687, 95%CI: 0.474-0.994 for multivariate analyses). In addition, CD3D was negatively correlated with the tumor location, Borrmann type and distant metastasis (P=0.012 for tumor location; P=0.007 for Borrmann type; P=0.027 for distant metastasis). In addition, the expression of CD3D was highly positively correlated with the expression of CD3, CD4, CD8, and PD-L1, and the combination of CD3D with CD3, CD4, CD8 and PD-L1 predicted the best prognosis (P=0.043). In summary, CD3D may play an important regulatory role in the tumor immune microenvironment of gastric cancer and may serve as a potential indicator of prognosis and immunotherapy response.