A 9-LncRNA Signature for Predicting Prognosis and Immune Response in Diffuse Large B-Cell Lymphoma

Tumor Biology Hides Novel Therapeutic Approaches to Diffuse Large B-Cell Lymphoma: A Narrative Review

Diffuse large B-cell lymphoma (DLBCL) is a malignancy of immense biological and clinical heterogeneity. Based on the transcriptomic or genomic approach, several different classification schemes have evolved over the years to subdivide DLBCL into clinically (prognostically) relevant subsets, but each leaves unclassified samples. Herein, we outline the DLBCL tumor biology behind the actual and potential drug targets and address the challenges and drawbacks coupled with their (potential) use. Therapeutic modalities are discussed, including small-molecule inhibitors, naked antibodies, antibody-drug conjugates, chimeric antigen receptors, bispecific antibodies and T-cell engagers, and immune checkpoint inhibitors. Candidate drugs explored in ongoing clinical trials are coupled with diverse toxicity issues and refractoriness to drugs. According to the literature on DLBCL, the promise for new therapeutic targets lies in epigenetic alterations, B-cell receptor and NF-κB pathways. Herein, we present putative targets hiding in lipid pathways, ferroptosis, and the gut microbiome that could be used in addition to immuno-chemotherapy to improve the general health status of DLBCL patients, thus increasing the chance of being cured. It may be time to devote more effort to exploring DLBCL metabolism to discover novel druggable targets. We also performed a bibliometric and knowledge-map analysis of the literature on DLBCL published from 2014-2023.

NEAT1 and CHROMR lncRNAs: a promising diagnostic tool for diffuse large B-cell lymphoma especially in elderly patients

Background: Long non-coding (lnc) RNAs have crucial regulatory roles in molecular pathways, and their dysregulation is associated with the pathogenesis of malignancies such as Diffuse large B-cell lymphoma (DLBCL). Therefore, we aimed to study the NEAT1 and CHROMR expression in DLBCL and explore their association with clinicopathological characteristics.Methods & materials: DLBCL and non-tumor lymph node specimens were obtained to assess the expression levels.Results: NEAT1 and CHROMR expressions were significantly increased in DLBCL, and were linked with the age of DLBCL patients (aged >60). NEAT1 and CHROMR overexpression may serve as moderate-to-good diagnostic biomarkers, with NEAT1 and CHROMR exhibiting area under the curve values of 0.781 and 0.831, respectively.

Cuproptosis-related lncRNA signature as a prognostic tool and therapeutic target in diffuse large B cell lymphoma

Cuproptosis is a newly defined form of programmed cell death that relies on mitochondria respiration. Long noncoding RNAs (lncRNAs) play crucial roles in tumorigenesis and metastasis. However, whether cuproptosis-related lncRNAs are involved in the pathogenesis of diffuse large B cell lymphoma (DLBCL) remains unclear. This study aimed to identify the prognostic signatures of cuproptosis-related lncRNAs in DLBCL and investigate their potential molecular functions. RNA-Seq data and clinical information for DLBCL were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Cuproptosis-related lncRNAs were screened out through Pearson correlation analysis. Utilizing univariate Cox, least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analysis, we identified seven cuproptosis-related lncRNAs and developed a risk prediction model to evaluate its prognostic value across multiple groups. GO and KEGG functional analyses, single-sample GSEA (ssGSEA), and the ESTIMATE algorithm were used to analyze the mechanisms and immune status between the different risk groups. Additionally, drug sensitivity analysis identified drugs with potential efficacy in DLBCL. Finally, the protein-protein interaction (PPI) network were constructed based on the weighted gene co-expression network analysis (WGCNA). We identified a set of seven cuproptosis-related lncRNAs including LINC00294, RNF139-AS1, LINC00654, WWC2-AS2, LINC00661, LINC01165 and LINC01398, based on which we constructed a risk model for DLBCL. The high-risk group was associated with shorter survival time than the low-risk group, and the signature-based risk score demonstrated superior prognostic ability for DLBCL patients compared to traditional clinical features. By analyzing the immune landscapes between two groups, we found that immunosuppressive cell types were significantly increased in high-risk DLBCL group. Moreover, functional enrichment analysis highlighted the association of differentially expressed genes with metabolic, inflammatory and immune-related pathways in DLBCL patients. We also found that the high-risk group showed more sensitivity to vinorelbine and pyrimethamine. A cuproptosis-related lncRNA signature was established to predict the prognosis and provide insights into potential therapeutic strategies for DLBCL patients.

DNA methyltransferases-associated long non-coding RNA PRKCQ-AS1 regulate DNA methylation in myelodysplastic syndrome

INTRODUCTION

Myelodysplastic syndrome (MDS) is a group of clonal hematopoietic stem cell disorders. DNA hypermethylation is considered to be the key mechanism of pathogenesis for MDS. Studies have demonstrated that DNA methylation can be regulated by the co-effect between long non-coding RNAs (lncRNAs) and DNA methyltransferases (DNMTs). The aim of this study was to identify DNMTs-associated differentially expressed (DE) lncRNAs, which may be a novel diagnostic and therapeutic target for MDS.

METHODS

Two gene expression profile datasets (GSE4619 and GSE19429) were downloaded from the Gene Expression Omnibus (GEO) database. Systematic bioinformatics analysis was conducted. Then we verified the expression of PRKCQ-AS1 in MDS patients and features in SKM-1 cells.

RESULTS

Bioinformatics analysis revealed that the DNMT-associated DE-lncRNA PRKCQ-AS1 was functionally related to DNA methylation. The target genes of PRKCQ-AS1 associated with DNA methylation are mainly methionine synthetase (MTR) and ten-eleven-translocation 1 (TET1). Moreover, the high expression of PRKCQ-AS1 was verified in real MDS cases. Further cellular analysis in SKM-1 cells revealed that overexpressed PRKCQ-AS1 promoted methylation levels of long interspersed nuclear element 1 (LINE-1) and cell proliferation, and apparently elevated both mRNA and protein levels of MTR and TET1, while knockdown of PRKCQ-AS1 showed opposite trend in SKM-1 cells.

CONCLUSION

DNMT-associated DE-lncRNA PRKCQ-AS1 may affects DNA methylation levels by regulating MTR and TET1.

Role of long non-coding RNA ELFN1-AS1 in carcinogenesis

As one of the leading causes of death worldwide, cancer significantly burdens patients and the healthcare system. The role of long non-protein coding RNAs (lncRNAs) in carcinogenesis has been extensively studied. The lncRNA ELFN1-AS1 was discovered recently, and subsequent studies have revealed its aberrantly high expression in various cancer tissues. In vitro and in vivo experiments have consistently demonstrated the close association between increased ELFN1-AS1 expression and malignant tumor characteristics, particularly in gastrointestinal malignancies. Functional assays have further revealed the mechanistic role of ELFN1-AS1 as a competitive endogenous RNA for microRNAs, inducing tumor growth, invasive features, and drug resistance. Additionally, the investigation into the clinical implication of ELFN1-AS1 has demonstrated its potential as a diagnostic, therapeutic, and, notably, prognostic marker. This review provides a comprehensive summary of evidence regarding the involvement of ELFN1-AS1 in cancer initiation and development, highlighting its clinical significance.

Unlocking the predictive potential of long non-coding RNAs: a machine learning approach for precise cancer patient prognosis

The intricate web of cancer biology is governed by the active participation of long non-coding RNAs (lncRNAs), playing crucial roles in cancer cells' proliferation, migration, and drug resistance. Pioneering research driven by machine learning algorithms has unveiled the profound ability of specific combinations of lncRNAs to predict the prognosis of cancer patients. These findings highlight the transformative potential of lncRNAs as powerful therapeutic targets and prognostic markers. In this comprehensive review, we meticulously examined the landscape of lncRNAs in predicting the prognosis of the top five cancers and other malignancies, aiming to provide a compelling reference for future research endeavours. Leveraging the power of machine learning techniques, we explored the predictive capabilities of diverse lncRNA combinations, revealing their unprecedented potential to accurately determine patient outcomes.

Identification and Validation of Ferroptosis-Related LncRNAs Signature as a Novel Prognostic Model for Chronic Lymphocytic Leukemia

Background

Chronic lymphocytic leukemia (CLL) is a subtype of B-cell malignancy with high heterogeneity. Ferroptosis is a novel cell death induced by iron and lipid peroxidation and exhibits prognostic value in many cancers. Emerging studies on long non-coding RNAs (lncRNAs) and ferroptosis reveal the unique value in tumorigenesis. However, the prognostic value of ferroptosis-related lncRNAs (FRLs) remains unclear in CLL.

Aim

We aimed to construct a FRLs risk model to predict prognosis and improve prognostic stratification for clinical practice.

Methods

RNA-sequencing data and clinical characteristics of CLL patients were downloaded from the GEO database. Based on ferroptosis-related genes from FerrDb database, differentially expressed FRLs with prognostic significance were identified and used to generate the risk model. The capability of the risk model was assessed and evaluated. GO and KEGG analyses were performed to confirm biological roles and potential pathways.

Results

A novel ferroptosis-related lncRNAs prognostic score (FPS) model containing six FRLs (PRKCQ, TRG.AS1, LNC00467, LNC01096, PCAT6 and SBF2.AS1) was identified. Patients in the training and validation cohort were evenly divided into high- and low-risk groups. Our results indicated that patients in the high-risk group had worse survival than those in the low-risk group. Functional enrichment analyses showed that the differently expressed genes (DEGs) between the two groups were enriched in the chemokine signaling pathway, hematopoietic cell lineage, T cell differentiation, TCR pathway and NF-κB pathway. Moreover, significant differences in immune cell infiltration were also observed. Surprisingly, FPS was proved to be an independent prognostic indicator for OS.

Conclusion

We established and evaluated a novel prognostic risk model with 6 FRLs that could predict prognosis accurately and describe the distinct immune infiltration in CLL.

Role of estrogen receptor signaling pathway-related genes in diffuse large B-cell lymphoma and identification of key targets via integrated bioinformatics analysis and experimental validation

Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous malignancy. Epidemiologically, the incidence of DLBCL is higher in men, and the female sex is a favorable prognostic factor, which can be explained by estrogen. This study aimed to explore the potential targets of the estrogen receptor (ER) signaling pathway and provide a meaningful way to treat DLBCL patients. Datasets were obtained from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs). Representative gene sets estrogen receptor pathways, and growth regulatory pathways were identified based on Gene Set Enrichment Analysis (GSEA) analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for function and pathway analysis. STRING and Cytoscape were used to construct the interaction network, and the MCODE plug-in performed the module analysis. GEPIA, TCGA, and LOGpc databases were used for expression and predictive analysis. The Human Protein Atlas (HPA) database was used to analyze the protein expression levels, cBioPortal was used to explore genetic alterations, and ROC analysis and prognostic assessment were used to predict the diagnostic value of genes. Finally, BJAB cells were treated with ER inhibitor fulvestrant and specific shRNA, and the expression of hub genes was verified by RT-qPCR. We identified 81 overlapping DEGs and CDC6, CDC20, KIF20A, STIL, and TOP2A as novel biomarkers affecting the prognosis of DLBCL. In addition, the STAT and KRAS pathways are considered potential growth regulatory pathways. These results hold promise for new avenues for the treatment of DLBCL patients.

Comprehensive analysis of lncRNA-mediated ceRNA regulatory networks and key genes associated with papillary thyroid cancer coexistent with Hashimoto's thyroiditis

OBJECTIVE

The incidence of papillary thyroid cancer (PTC) concomitant with Hashimoto's thyroiditis (HT) is gradually increasing over the past decades. This study aims to identify differentially expressed lncRNAs between tumor tissues of PTC with or without HT and further to confer a better understanding of lncRNA-based competing endogenous RNA (ceRNA) network in PTC with HT.

METHODS

GSE138198 containing tissue mRNA data and GSE192560 containing lncRNA data were utilized to perform differentially expression analysis. The ceRNA network was constructed based on miRNA-mRNA interactions merging with lncRNA-microRNA interactions. Functional enrichment analysis and protein-protein interaction (PPI) analysis were performed. The mRNA levels of core genes in the PPI analysis in tumor tissues collected from 112 PTC patients including 35 cases coexistent with HT were determined by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

A total of 57 genes and 40 lncRNAs, with value of |log2 fold change (FC)|≥ 1 and the adjusted P-value < 0.05, were deemed as differentially expressed genes and lncRNAs between PTC with and without HT. The pathways most significantly enriched by differentially expressed genes between PTC with and without HT were viral protein interaction with cytokine and cytokine receptor and cytokine-cytokine receptor interaction. CXCL10, CXCL9, CCL5, FCGR3A, and CCR2 owned degree values not less than 10 were deemed as core genes differentially expressed between PTC with and without HT. A total of 76 pairs of lncRNA-miRNA-mRNA ceRNA were obtained. Results of qRT-PCR partially demonstrated the bioinformatics results that the mRNA levels of CXCL10, CXCL9, CCL5, and CCR2 were remarkably elevated in tumor tissues collected from PTC patients coexistent with HT than those without HT (P < 0.001).

CONCLUSION

Our study offers a better understanding of the lncRNA-related ceRNA network involved in PTC with HT, providing novel key genes associated with PTC coexistent with HT.