LncRNAs LY86-AS1 and VIM-AS1 Distinguish Plasma Cell Leukemia Patients from Multiple Myeloma Patients

Mitophagy-related gene signature for predicting the prognosis of multiple myeloma

Aims

The aims of this study were to explore the molecular mechanism of mitophagy in multiple myeloma (MM) and to develop an effective prognostic signature for the disease based on mitophagy-related genes (MRGs).

Methods

Three gene sets from the Reactome database were used to explore MRGs, following which those that were differentially expressed between MM and normal samples were investigated using the data from the Genomic Data Commons-Multiple Myeloma Research Foundation-CoMMpass Study. Mitophagy-related molecular subtypes of MM were identified and their immune infiltration, associated patient survival rates, immune checkpoint genes, and mitophagy scores were compared. Prognostic genes for MM were identified, and a prognostic model was constructed. Additionally, a nomogram was constructed using the prognostic model and prognosis-related clinical features. Finally, the drug sensitivity and correlation analyses of the subtypes were performed between the two risk groups.

Results

We identified two MM molecular subtypes that exhibited significant differences in mitophagy scores, associated patient survival rates, immune infiltration, and immune checkpoint genes. An MRG-based prognostic signature was constructed using six genes (TRIP13, KIF7, GPR63, CRIP2, DNTT, and HSPB8), which had high predictive prognostic value. A nomogram was constructed by screening five indicators (risk score, subtype, age, sex, and stage) that could predict the 1-, 3-, and 5-year survival probabilities of patients with MM. The two risk groups displayed significant differences in their IC50 values of 33 drugs, such as bleomycin. Patients in the high-risk group tended to fall within Mitophagy_cluster_A.

Conclusion

Our MRG-based signature is a promising prognostic biomarker for MM.

Immunogenic cell death-related long noncoding RNA influences immunotherapy against lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths, accounting for over a million deaths worldwide annually. Immunogenic cell death (ICD) elicits an adaptive immune response. However, the role of ICD-related long noncoding RNAs (lncRNAs) in LUAD is unknown. In this study, we investigated the characteristics of the tumor microenvironment in LUAD, the prognostic significance of ICD-related lncRNAs, and the half-maximal inhibitory concentration (IC50) of possible chemotherapeutic drugs. We sorted prognostic lncRNAs using univariate Cox regression and constructed a risk signature based on them. We then confirmed the model's accuracy and generated a nomogram. Additionally, we performed immune microenvironment analysis, somatic mutation calculation, Tumor Immune Dysfunction and Exclusion (TIDE) analysis, and anticancer pharmaceutical IC50 prediction. Least absolute shrinkage and selection operator Cox regression identified 27 prognostic lncRNAs related to ICD, and a unique risk signature using 10 ICD-related lncRNAs was constructed. The risk score was confirmed to be a reliable predictor of survival, with the highest c-index score. The signature had a remarkable predictive performance with clinical applicability and could accurately predict the overall survival in LUAD. Furthermore, the lncRNA signature was closely associated with immunocyte invasion. We also analyzed the correlation between the risk score, tumor-infiltrating immune cells, and prognosis and identified high immune and ESTIMATE scores in low-risk patients. Moreover, we observed elevated checkpoint gene expression and low TIDE scores in high-risk patients, indicating a good immunotherapy response. Finally, high-risk patients were shown to be susceptible to anticancer medications. Therefore, our unique risk signature comprising 10 ICD-related lncRNAs was demonstrated to indicate the characteristics of the tumor-immune microenvironment in LUAD, predict patients' overall survival, and guide individualized treatment.

Upregulation of lncRNA LANCL1-AS1 inhibits the progression of non-small-cell lung cancer via the miR-3680-3p/GMFG axis

Patients with non-small-cell lung cancer (NSCLC) have a low survival rate. Long non-coding RNA (LncRNA) LANCL1 antisense RNA 1 (LANCL1-AS1) was indicated to be downregulated in NSCLC; however, its detailed function in NSCLC is unanswered. Real-time quantitative polymerase chain reaction revealed the downregulation of LANCL1-AS1 in NSCLC cell lines and subcellular fractionation assay showed that LANCL1-AS1 was mainly located in the cytoplasm of NSCLC cells. Cell counting kit-8, Transwell, and tube formation assays displayed that overexpression of LANCL1-AS1 suppressed NSCLC cell proliferation, migration, invasiveness, and angiogenesis in vitro. Animal experiments validated the tumor-suppressive role of LANCL1-AS1 in tumor-bearing mice. Mechanistically, LANCL1-AS1 upregulated glia maturation factor gamma (GMFG) expression by competitively binding to miR-3680-3p. GMFG knockdown reversed LANCL1-AS1 overexpression-mediated inhibitory impact on NSCLC malignant behaviors. Collectively, LANCL1-AS1 upregulation inhibits the progression of NSCLC by modulating the miR-3680-3p/GMFG axis.

Clinicopathological and laboratory parameters of plasma cell leukemia among Indian population

BACKGROUND

Plasma cell leukemia (PCL) is a rare and aggressive plasma cell neoplasm distinguished by extensive clonal expansion of plasma cells in the bone marrow (BM) and peripheral blood (PB). PCL is divided into two subtypes: primary (pPCL) originates de novo without preceding multiple myeloma, while secondary (sPCL) comprises a leukemic modification that occurs as a late manifestation from previous multiple myeloma (MM). pPCL and sPCL are clinically and biologically two different entities. The molecular mechanisms of the development of PCL, either primary or secondary, remain poorly understood. We aim to present 5 years of data on clinical profiles and treatment outcomes of pPCL and sPCL patients treated at our cancer hospital in India and to find a predictive parameter of the development of PCL in cases of MM.

METHODS

In this study, we retrospectively reviewed and evaluated the clinicopathological features, laboratory parameters, immunophenotypic profile, and patient outcomes of 17 PCL cases diagnosed among 180 plasma cell dyscrasia patients during the study period to establish a correlation between pPCL & sPCL for diagnosis and management of PCL.

RESULTS

A total of 17 PCL patients were diagnosed among 180 plasma cell dyscrasia patients during the study period. Among PCL patients, 9 cases had pPCL (52.94% of all PCL patients), and 8 cases had sPCL (47.06% of all PCL patients). Peculiar differences were seen between the two PCL types. Both types of PCL had a younger age at the time of diagnosis, having elevated BM plasma cell infiltration percentage, frequent anemia, thrombocytopenia, elevated beta-2-microglobulin (B2M) levels, raised LDH levels, and positive M-protein in both serum and urine. In addition, SFLC assay and Immunofixation assay showed higher κ and lower λ in pPCL compared with sPCL (P<0.05). Higher Renal insufficiency was also observed in pPCL compared to sPCL (P=0.335). The survival and response to treatment of PCL patients remain considerably poor, sPCL exhibit shorter overall survival (OS) than pPCL with (median 1.75 months vs. 7 months respectively, P=0.1682). Plasma cell leukemia (PCL) needs to be diagnosed early and requires prompt initiation of treatment before patients get complications.

CONCLUSION

Our study characterizes the clinical and laboratory features of pPCL and sPCL and may aid physicians in prognosticating the course of disease of their patients. However, future multicentre studies are the need of the hour to develop accurate diagnostic criteria and establish the efficacy of therapeutic regimens.

Genomics of Plasma Cell Leukemia

Simple Summary

Plasma cell leukemia (PCL) is a very aggressive plasma cell disorder with a dismal prognosis, despite the therapeutic progress made in the last few years. The implementation of genomic high-throughput technologies in the clinical setting has revealed new insights into the genomic landscape of PCL, some of which may have an impact on the development of novel therapeutic approaches. The purpose of this review is to provide a comprehensive overview and update of the genomic studies carried out in PCL.

Abstract

Plasma cell leukemia (PCL) is a rare and highly aggressive plasma cell dyscrasia characterized by the presence of clonal circulating plasma cells in peripheral blood. PCL accounts for approximately 2–4% of all multiple myeloma (MM) cases. PCL can be classified in primary PCL (pPCL) when it appears de novo and in secondary PCL (sPCL) when it arises from a pre-existing relapsed/refractory MM. Despite the improvement in treatment modalities, the prognosis remains very poor. There is growing evidence that pPCL is a different clinicopathological entity as compared to MM, although the mechanisms underlying its pathogenesis are not fully elucidated. The development of new high-throughput technologies, such as microarrays and new generation sequencing (NGS), has contributed to a better understanding of the peculiar biological and clinical features of this disease. Relevant information is now available on cytogenetic alterations, genetic variants, transcriptome, methylation patterns, and non-coding RNA profiles. Additionally, attempts have been made to integrate genomic alterations with gene expression data. However, given the low frequency of PCL, most of the genetic information comes from retrospective studies with a small number of patients, sometimes leading to inconsistent results.