Clinicopathological significance of CD79a expression in classic Hodgkin lymphoma

Sakatani, Akio; Igawa, Takuro; Okatani, Takeshi; Fujihara, Megumu; Asaoku, Hideki; Sato, Yasuharu; Yoshino, Tadashi

doi:10.3960/jslrt.20010

Cited by 13 publications

(11 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an example of an event both predicted by AbSplice and called by FRASER, we showcase CD79A, a well-known oncogene frequently affected by somatic mutations in hematologic malignancies (42,(69)(70)(71). We discovered splicing outliers of CD79A in samples MZL_3758 and LPL_0664, resulting in a truncation of 18 amino acids at the beginning of the 5 th exon (Figure 3E).…”

Section: Resultsmentioning

confidence: 74%

Analysis of 3,760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes

Cao,

Huber,

Ahari

et al. 2023

Preprint

View full text Add to dashboard Cite

Rare driver mutations are suspected to substantially contribute to the large heterogeneity of hematologic malignancies, but their identification remains challenging. To address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from over 3,760 patients spanning 24 disease entities. To discover rare and large regulatory aberrations, we analyzed aberrant expression and splicing events using an extension of DROP (Detection of RNA Outliers Pipeline) and AbSplice, an algorithm that identifies genetic variants causing aberrant splicing. We found a median of seven aberrantly expressed genes, two aberrantly spliced genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for well-characterized driver genes, with odds ratios exceeding three among genes called in more than one sample. We next trained disease-specific driver gene prediction models integrating these data with recurrent mutation analyses. On held-out data, integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Moreover, we found a truncated form of the low density lipoprotein receptor LRP1B to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B be a novel biomarker and a yet unreported functional role of LRP1B within these rare entities. Altogether, this dataset and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.

show abstract

Section: Resultsmentioning

confidence: 74%

Analysis of 3,760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes

Cao,

Huber,

Ahari

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…CD79A, a B-cell receptor-associated protein, plays a critical role in B-cell development and function 48 . CD79A is an important target in classical Hodgkin’s lymphoma 49 . CTLA4, a known immune checkpoint molecule, regulates T-cell activation and the immune response.…”

Section: Discussionmentioning

confidence: 99%

Machine learning reveals diverse cell death patterns in lung adenocarcinoma prognosis and therapy

Wang,

et al. 2024

npj Precis. Onc.

View full text Add to dashboard Cite

Cancer cell growth, metastasis, and drug resistance pose significant challenges in the management of lung adenocarcinoma (LUAD). However, there is a deficiency in optimal predictive models capable of accurately forecasting patient prognoses and guiding the selection of targeted treatments. Programmed cell death (PCD) pathways play a pivotal role in the development and progression of various cancers, offering potential as prognostic indicators and drug sensitivity markers for LUAD patients. The development and validation of predictive models were conducted by integrating 13 PCD patterns with comprehensive analysis of bulk RNA, single-cell RNA transcriptomics, and pertinent clinicopathological details derived from TCGA-LUAD and six GEO datasets. Utilizing the machine learning algorithms, we identified ten critical differentially expressed genes associated with PCD in LUAD, namely CHEK2, KRT18, RRM2, GAPDH, MMP1, CHRNA5, TMPRSS4, ITGB4, CD79A, and CTLA4. Subsequently, we conducted a programmed cell death index (PCDI) based on these genes across the aforementioned cohorts and integrated this index with relevant clinical features to develop several prognostic nomograms. Furthermore, we observed a significant correlation between the PCDI and immune features in LUAD, including immune cell infiltration and the expression of immune checkpoint molecules. Additionally, we found that patients with a high PCDI score may exhibit resistance to immunotherapy and standard adjuvant chemotherapy regimens; however, they may benefit from other FDA-supported drugs such as docetaxel and dasatinib. In conclusion, the PCDI holds potential as a prognostic signature and can facilitate personalized treatment for LUAD patients.

show abstract

“…The CD79A gene encodes the Ig-alpha protein of the B-cell antigen component, which is typically associated with the B-cell receptor signaling complex. CD79A was often used as the marker of the B-cell-relevant tumor, such as Hodgkin’s lymphoma ( Sakatani et al, 2020 ). Frequent downregulation or deletion of CD79A could be potential clues for the diagnosis of plasma cell myeloma in the elderly ( Tanaka et al, 2009 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes in late-onset major depressive disorder through a co-expression network module

Yao

Sun

2022

Front. Genet.

View full text Add to dashboard Cite

Late-onset major depressive disorder (LOD) increases the risk of disability and suicide in elderly patients. However, the complex pathological mechanism of LOD still remains unclear. We selected 10 LOD patients and 12 healthy control samples from the GSE76826 dataset for statistical analysis. Under the screening criteria, 811 differentially expressed genes (DEGs) were screened. We obtained a total of two most clinically significant modules through the weighted gene co-expression network analysis (WGCNA). Functional analysis of the genes in the most clinically significant modules was performed to explore the potential mechanism of LOD, followed by protein–protein interaction (PPI) analysis and hub gene identification in the core area of the PPI network. Furthermore, we identified immune infiltrating cells using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm between healthy subjects and LOD patients with the GSE98793 dataset. Next, six hub genes (CD27, IL7R, CXCL1, CCR7, IGLL5, and CD79A) were obtained by intersecting hub genes with DEGs, followed by verifying the diagnostic accuracy with the receiver operating characteristic curve (ROC). In addition, we constructed the least absolute shrinkage and selection operator (LASSO) regression model for hub gene cross-validation. Finally, we found that CD27 and IGLL5 were good diagnostic indicators of LOD, and CD27 may be the key gene of immune function change in LOD. In conclusion, our research shows that the changes in the immune function may be an important mechanism in the development of LOD, which can provide some guidance for the related research of LOD in the future.

show abstract

Clinicopathological significance of CD79a expression in classic Hodgkin lymphoma

Cited by 13 publications

References 33 publications

Analysis of 3,760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes

Analysis of 3,760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes

Machine learning reveals diverse cell death patterns in lung adenocarcinoma prognosis and therapy

Identification of key genes in late-onset major depressive disorder through a co-expression network module

Contact Info

Product

Resources

About