This study unveils 15 risk genes as potential pathogenic and therapeutic biomarkers for DLBCL and provides novel insights and targets for understanding the pathogenesis, diagnosis, and treatment of DLBCL.
Abstract
Background
Diffuse large B-cell lymphoma (DLBCL) presents a complex etiology and challenging diagnosis. This study aims to investigate potential pathogenic genes.
Methods
We identified DLBCL risk genes (DRGs) through expression quantitative trait loci-Mendelian randomization (eQTL-MR). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore biological functions. Single-cell RNA sequencing (scRNA-seq) data were analyzed to delineate the subcellular localization. Immune infiltration analyses examined the role of genes in the DLBCL immune microenvironment. Finally, drug sensitivity analyses were performed to predict potentially sensitive drugs.
Results
Following eQTL-MR and prognostic analyses, we identified 15 genes associated with both the pathogenesis and prognosis of DLBCL. These genes were successfully integrated into a risk gene model, achieving an Area Under the Curve (AUC) of 0.787. GO and KEGG enrichment analyses of genes localized significant pathways, including NF-κB signal transduction. ScRNA-seq analysis suggested that DRGs may be linked to the immune microenvironment of DLBCL. Further immune infiltration analysis confirmed the pivotal role of immune infiltration in the malignant progression of DLBCL.
Conclusion
This study unveils 15 risk genes as potential pathogenic and therapeutic biomarkers for DLBCL. These findings provide novel insights and targets for understanding the pathogenesis, diagnosis, and treatment of DLBCL.
Findings suggest that BRCA1 is not an independent prognostic factor but reflects broader tumor biological processes, particularly DNA repair and redox regulation, and positioning BRCA1 as a key component of tumor biology and a potential target for precision oncology strategies.
Jianhua Yang, Zheng-jun Qiu, Wenchao Song et al.· Current Medicinal Chemistry· 0 citations
BACKGROUND
While mitochondrial dysfunction and immune cell dysregulation have been established as important contributors to the pathogenesis of osteosarcoma (OS), the prognostic value of mitochondria-related genes (MRGs) and immune-related genes (IRGs) in OS remains poorly understood.
METHODS
Data were obtained from public databases. Differentially expressed genes (DEGs) and key module genes were identified viadifferential expression analysis and weighted gene co-expression network analysis, respectively. Candidate genes of interest were identified by intersecting key module genes, DEGs, MRGs, and IRGs. Then, candidate genes were screened using regression analysis and proportional hazards assumption testing to identify prognostic genes. A risk model was then constructed in the TARGET-OS dataset and validated in GSE16091. Independent prognostic, immune infiltration, and gene expression analyses were conducted. In addition, single-cell analysis was performed to identify key cell populations, and pseudotime analysis as well as cell communication network construction were carried out. Finally, the expression patterns of selected prognostic genes were additionally validated via reverse transcription quantitative polymerase chain reaction (RT-qPCR), and the functional role of protein kinase Cα (PRKCA) in osteosarcoma cell proliferation, migration, and invasion was evaluated in vitro.
RESULTS
Glutathione S-transferase Pi-1 (GSTP1), catalase (CAT), TNFSF10, and PRKCA were identified as mitochondria- and immunity-related prognostic genes in OS and were used to construct a risk model. The risk model was able to effectively predict the survival of patients with OS. A nomogram based on the identified prognostic genes additionally exhibited excellent performance when predicting OS patient survival. Significant differences in the abundance of 14 immune cell types and 8 immune checkpoint molecules were noted between the high-risk and low-risk groups. Single-cell analyses were then conducted to annotate 10 cell types, and M1 macrophages were identified as a potentially relevant cell population in the OS microenvironment. During M1 macrophage differentiation, initial reductions in CAT and PRKCA expression were noted, followed by subsequent increases and final decreases. In contrast, the expression of GSTP1 and TNFSF10 in these cells first rose and then declined. Furthermore, PRKCA was selected for further in vitro analyses, which revealed that it can enhance the invasion, migration, and proliferation of OS cells.
CONCLUSION
This study identified GSTP1, CAT, TNFSF10, and PRKCA as prognostic genes associated with mitochondrial and immune function in OS, with in vitro evidence providing direct support for the potential functional role of PRKCA in OS progression. These findings highlight a new avenue for predicting clinical prognosis in OS and may provide a basis for future studies of putative therapeutic targets.
J. Ai, Zhidong Peng, Peichuan Xu et al.· Frontiers in Bioscience· 0 citations
Background Angioimmunoblastic T-cell lymphoma (AITL) is an aggressive subtype of peripheral T-cell lymphoma with poor clinical outcomes and limited therapeutic options. The contribution of metabolic reprogramming and immune microenvironmental alterations to AITL progression remains insufficiently defined. Methods We conducted integrative transcriptomic and proteomic analyses of AITL samples compared with reactive lymphoid hyperplasia to identify molecules associated with treatment response and prognosis. Immune infiltration and pathway enrichment analyses were performed, and findings were validated in independent GEO cohorts (GSE19069 and GSE58445). Key protein expression was confirmed by immunohistochemistry and multiplex immunofluorescence. Results Differential expression analyses revealed that lysosomal alpha-glucosidase (GAA), a key enzyme involved in glycogen metabolism, was significantly upregulated at both the RNA and protein levels in patients who failed to respond to standard chemotherapy. Elevated GAA expression was associated with inferior overall survival in multivariate analysis. Immunohistochemistry staining and multiplex immunofluorescence further confirmed the spatial expression pattern of GAA in AITL tissues. Immune deconvolution and pathway enrichment analyses suggested that GAA-high tumors exhibited increased CD8+ T-cell infiltration accompanied by transcriptional features of T-cell exhaustion, as well as transcriptionally inferred metabolic alterations characterized by enhanced glycolysis-related signatures and reduced oxidative phosphorylation-related signatures. These findings were further validated in an independent AITL cohort from the GEO database. Conclusions Our study identifies GAA as a candidate biomarker associated with adverse clinical outcomes, immune microenvironmental features, and metabolic pathway alterations in AITL, highlighting glycogen metabolism as a previously underexplored biological axis and a potential therapeutic vulnerability warranting further functional investigation.
Yanfei Liu, Yun-Fei Shi, Yang Zhao et al.· Frontiers in Immunology· 0 citations
A seven-gene immune-related prognostic signature that, combined with clinicopathological parameters, provides a robust tool for individualized survival prediction and may guide precision management in CRC patients is developed and validated.
LINC01615, a long noncoding RNA, plays a pivotal role in the progression of kidney renal clear cell carcinoma (KIRC). This study aimed to assess the prognostic value of LINC01615-associated genes in KIRC by developing a risk model. Differential expression analysis in the The Cancer Genome Atlas-KIRC dataset identified differentially expressed genes between high and low LINC01615 expression groups, as well as between KIRC and control groups. Signature genes were subsequently selected through protein-protein interaction (PPI) network analysis, while prognostic genes were identified via Cox regression. The risk model was then constructed and validated using the E-MTAB-1980 dataset. Furthermore, an independent prognostic analysis identified key risk factors, and a nomogram was created for clinical application. Additional analyses, including enrichment analysis, immune-related analysis, drug sensitivity evaluation, and regulatory network construction, were performed to explore the underlying mechanisms in high and low-risk groups. The GSE40435 dataset was employed for the validation of prognostic gene expression. Reverse transcription-quantitative PCR (RT-qPCR) was conducted to confirm the expression levels of prognostic genes and LINC01615 in clinical samples. LINC01615 expression was found to differ significantly between KIRC and control groups, with notable survival differences observed between high and low expression groups. A total of 757 candidate genes were identified. Among these, COL4A4, COL5A1, and COL15A1 were screened as prognostic genes, and a risk model with better accuracy was constructed. Age and risk score were recognized as independent risk factors, and the nomogram demonstrated enhanced predictive accuracy. Twelve drugs showed a significant negative correlation with risk scores. Additionally, the high-risk group exhibited an increased likelihood of immune escape. A regulatory relationship between hsa-miR-3163 and COL4A4/LINC01615 was identified. In both The Cancer Genome Atlas-KIRC and GSE40435 datasets, COL5A1 and COL15A1 were overexpressed in the KIRC group. RT-qPCR results for COL5A1 and COL4A4 were consistent with the above findings, while COL15A1 showed no significant differences in clinical samples, possibly due to the small sample size. COL4A4, COL5A1, and COL15A1 were identified as prognostic biomarkers through bioinformatics analysis. The developed risk model offers valuable insights for clinical prognostic prediction and immunotherapy in KIRC.
Shi-Bin Guo, Shuangqin Xu, Peng Song et al.· Medicine· 0 citations
BACKGROUND
Burkitt lymphoma (BL) is a highly aggressive malignancy with limited effective treatments due to toxicity/resistance. Identifying and prioritizing candidate biomarkers and potential therapeutic targets from complex transcriptomic data, ahead of functional validation, remains a major unmet need.
METHODS
We integrated differential gene expression analysis across BL vs. control cohorts (Gene Expression Omnibus [GEO] datasets GSE43677/GSE12453) with Random Forest machine learning to prioritize candidates. Validated top genes via immunohistochemistry in an independent cohort (n = 10 BL, n = 10 reactive lymphoid hyperplasia [RLH] controls), followed by immune cell infiltration analysis.
RESULTS
This approach identified four candidate genes; only Chromatin Assembly Factor 1 Subunit A (CHAF1A) showed profound protein-level overexpression in BL tumors vs. RLH. In a single-dataset CIBERSORT analysis, CHAF1A expression showed exploratory correlational associations with several immune-cell fractions, most strongly a positive association with M0 macrophages; these in silico associations are hypothesis-generating and do not by themselves establish a mechanistic role in the tumor immune microenvironment.
CONCLUSION
CHAF1A is identified as a candidate biomarker associated with BL that, in exploratory single-dataset analysis, also correlates with immune-cell infiltration; these findings nominate it as a potential therapeutic target that warrants functional validation in future studies.
