Prognostic significance and biological implications of SM-like genes in mantle cell lymphoma.

BACKGROUND: SM-like (LSM) genes a family of RNA-binding proteins, are involved in mRNA regulation and can function as oncogenes by altering mRNA stability. However, their roles in B-cell progression and tumorigenesis remain poorly understood. METHODS: We analyzed gene expression profiles and overall survival data of 123 patients with mantle cell lymphoma (MCL). The LSM index was developed to assess its potential as a prognostic marker of MCL survival. RESULTS: Five of the eight LSM genes were identified as potential prognostic markers for survival in MCL, with particular emphasis on the LSM.index. The expression levels of these LSM genes demonstrated their potential utility as classifiers of MCL. The LSM.index-high group exhibited both poorer survival rates and lower RNA levels than did the overall transcript profile. Notably, LSM1 and LSM8 were overexpressed in the LSM.index-high group, with LSM1 showing 2.5-fold increase (p < 0.001) and LSM8 depicting 1.8-fold increase (p < 0.01) than those in the LSM.index-low group. Furthermore, elevated LSM gene expression was associated with increased cell division and RNA splicing pathway activity. CONCLUSIONS: The LSM.index demonstrates potential as a prognostic marker for survival in patients with MCL. Elevated expression of LSM genes, particularly LSM1 and LSM8, may be linked to poor survival outcomes through their involvement in cell division and RNA splicing pathways. These findings suggest that LSM genes may contribute to the aggressive behavior of MCL and represent potential targets for therapeutic interventions.

Single-cell exome sequencing reveals polyclonal seeding and TRPS1 mutations in colon cancer metastasis.

Liver metastasis remains the primary cause of mortality in patients with colon cancer. Identifying specific driver gene mutations that contribute to metastasis may offer viable therapeutic targets. To explore clonal evolution and genetic heterogeneity within the metastasis, we conducted single-cell exome sequencing on 150 single cells isolated from the primary tumor, liver metastasis, and lymphatic metastasis from a stage IV colon cancer patient. The genetic landscape of the tumor samples revealed that both lymphatic and liver metastases originated from the same region of the primary tumor. Notably, the liver metastasis was derived directly from the primary tumor, bypassing the lymph nodes. Comparative analysis of the sequencing data for individual cell pairs within different tumors demonstrated that the genetic heterogeneity of both liver and lymphatic metastases was also greater than that of the primary tumor. This finding indicates that liver and lymphatic metastases arose from clusters of circulating tumor cell (CTC) of a polyclonal origin, rather than from a single cell from the primary tumor. Single-cell transcriptome analysis suggested that higher EMT score and CNV scores were associated with more polyclonal metastasis. Additionally, a mutation in the TRPS1 (Transcriptional repressor GATA binding 1) gene, TRPS1 R544Q, was enriched in the single cells from the liver metastasis. The mutation significantly increased CRC invasion and migration both in vitro and in vivo through the TRPS1R544Q/ZEB1 axis. Further TRPS1 mutations were detected in additional colon cancer cases, correlating with advanced-stage disease and inferior prognosis. These results reveal polyclonal seeding and TRPS1 mutation as potential mechanisms driving the development of liver metastases in colon cancer.

Multi-omics and single cell characterization of cancer immunosenescence landscape.

Cellular senescence (CS) is closely related to tumor progression. However, the studies about CS genes across human cancers have not explored the relationship between cancer senescence signature and telomere length. Additionally, single-cell analyses have not revealed the evolutionary trends of malignant cells and immune cells at the CS level. We defined a CS-associated signature, called "senescence signature", and found that patients with higher senescence signature had worse prognosis. Higher senescence signature was related to older age, higher genomic instability, longer telomeres, increased lymphocytic infiltration, higher pro-tumor immune infiltrates (Treg cells and MDSCs), and could predict responses to immune checkpoint inhibitor therapy. Single-cell analysis further reveals malignant cells and immune cells share a consistent evolutionary trend at the CS level. MAPK signaling pathway and apoptotic processes may play a key role in CS, and senescence signature may effectively predict sensitivity of MEK1/2 inhibitors, ERK1/2 inhibitors and BCL-2 family inhibitors. We also developed a new CS prediction model of cancer survival and established a portal website to apply this model ( https://bio-pub.shinyapps.io/cs_nomo/ ).

Single-cell immune landscape of measurable residual disease in acute myeloid leukemia.

Measurable residual disease (MRD) is a powerful prognostic factor of relapse in acute myeloid leukemia (AML). We applied the single-cell RNA sequencing to bone marrow (BM) samples from patients with (n=20) and without (n=12) MRD after allogeneic hematopoietic stem cell transplantation. A comprehensive immune landscape with 184,231 cells was created. Compared with CD8+ T cells enriched in the MRD-negative group (MRD-_CD8), those enriched in the MRD-positive group (MRD+_CD8) showed lower expression levels of cytotoxicity-related genes. Three monocyte clusters (i.e., MRD+_M) and three B-cell clusters (i.e., MRD+_B) were enriched in the MRD-positive group. Conversion from an MRD-positive state to an MRD-negative state was accompanied by an increase in MRD-_CD8 clusters and vice versa. MRD-enriched cell clusters employed the macrophage migration inhibitory factor pathway to regulate MRD-_CD8 clusters. These findings revealed the characteristics of the immune cell landscape in MRD positivity, which will allow for a better understanding of the immune mechanisms for MRD conversion.

Prediction and prognostic potential of NR3C1 gene expression level in DLBCL patients.

Objective: Diffuse Large B-Cell Lymphoma (DLBCL) is a common and frequently occurring subtype of Non-Hodgkin Lymphoma (NHL). The effective treatment and prognosis of DLBCL are still urgently needed to be explored. This article aims to shed light on the connection between DLBCL survival and NR3C1 expression levels. Methods: First, we divided the 952 DLBCL patients into an NR3C1 high-expression group and an NR3C1 low-expression group and compared the baseline characteristics of the two groups. Second, we used multivariate analysis to predict the dependent variable for age, pathology, ECOG score, lactate dehydrogenase (LDH) ratio, and NR3C1 expression level. Finally, we analyzed the progression-free survival (PFS) and overall survival rate (OS) of DLBCL patients with high or low NR3C1 expression. Results: DLBCL patients with high NR3C1 expression had a better prognosis than those with low NR3C1 expression (OS, P < 0.0001). In DLBCL patients of CHOP therapy, high NR3C1 expression was associated with a good survival prognosis in OS (OS, P = 0.028). Conclusion: In multivariate analysis, NR3C1 high expression was an independent prognostic factor that predicted a longer OS of DLBCL (OS, P = 0.0003). NR3C1 is considered an independent predictor of DLBCL patients and can be used as a biomarker for the prognosis of DLBCL.

FAM46C-mediated tumor heterogeneity predicts extramedullary metastasis and poorer survival in multiple myeloma.

Cancers originate from a single cell according to Nowell's theory of clonal evolution. The enrichment of the most aggressive clones has been developed and the heterogeneity arises for genomic instability and environmental selection. Multiple myeloma (MM) is a multiple relapse plasma cell cancer generated from bone marrow. Although there were accumulating researches in multiple myeloma pathogenesis, the heterogeneity remains poorly understood. The participants enrolled in this study were 4 EMP+ (EMP, Extramedullary plasmacytoma) and 2 EMP- primarily untreated MM patients. Single cell RNA sequencing and analysis were conducted for the single cell suspension, which was sorted by flow cytometry from peripheral blood mononuclear cells or bone marrow cells. In our research, the results of single cell RNA sequencing show that FAM46C determines MM tumor heterogeneity predicting extramedullary metastasis by influencing RNA stability. Further, we integrated and analyzed 2280 multiple myeloma samples from 7 independent datasets, which uncover that FAM46C mediated tumor heterogeneity predicts poorer survival in multiple myeloma.

High trophinin-associated protein expression predicts good survival in acute myeloid leukemia with normal cytogenetics.

BACKGROUND: Nearly half of adult acute myeloid leukemia (AML) patients were classified into cytogenetic normal acute myeloid leukemia (CN-AML). The expression level of Trophinin associated protein (TROAP) was proven to be associated with the prognosis of several cancers, but it is still unclear in the prognosis of patients with CN-AML. METHODS: We integrated CN-AML patient samples from 4 datasets to analyze the relationship between TROAP expression and the survival of CN-AML. In addition, we investigated 92 AML patients of The Cancer Genome Atlas (TCGA) database to analyze the relationship between TROAP expression and the survival of AML patients received chemotherapy. We investigated the relationship between the expression of TROAP and drug sensitivity in AML cell lines. RESULTS: CN-AML patients with high TROAP expression were related to good event-free survival (EFS) and overall survival (OS). In AML patients received chemotherapy, high TROAP expression was associated with good survival prognosis. Additionally, the expression of TROAP gene in leukemia stem cells (LSC) + group was lower. Among multiple drugs, the lower the expression of TROAP, the lower the IC50. CONCLUSION: TROAP could serve as an independent predictor of CN-AML patients and could act as a potential biomarker for the prognosis of CN-AML. TROAP expression levels were closely correlated with the drug sensitivity of multiple drugs.

The expression level of Neuronal Calcium Sensor 1 can predict the prognosis of cytogenetically normal AML.

Acute myeloid leukemia (AML) is malignant clonal expansion of myeloid blasts with high heterogeneity and numerous molecular biomarkers have been found to judge the prognosis in some specific classifications of AML. Furthermore, as for patients with cytogenetically normal acute myeloid leukemia (CN-AML), we need to find more new biomarkers to predict the patients' outcomes. Recently, the expression level of Neuronal Calcium Sensor 1 (NCS1) has been associated with the prognosis of breast cancer and hepatocellular carcinoma, but nothing related has been reported about hematological malignancies. Therefore, we make this study to explore the relationship between the NCS1 expression level and CN-AML. We analyzed the relation between survival and NCS1 RNA expression through 75 CN-AML patients from Cancer Genome Atlas (TCGA) database and 433 CN-AML patients (3 independent datasets) from Gene Expression Omnibus (GEO) database. Additionally, we compared the NCS1 RNA expression between 138 leukemia stem cells positive (LSCs+) samples and 89 leukemia stem cells negative (LSCs-) samples from 78 AML patients from GSE76004 dataset. In our study, CN-AML patients with high expression level of NCS1 have longer EFS or OS. In addition, the NCS1 expression level in leukemia stem cells was low (p = 0.00039). According to these findings, we concluded that the high expression of NCS1 can predict favorable prognosis in CN-AML patients. Furthermore, our work put forward that NCS1 expresses lower in LSCs+, which might be an important mechanism to explain the aggressiveness of AML.

The prognostic value of RASGEF1A RNA expression and DNA methylation in cytogenetically normal acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is a significantly heterogeneous malignancy of the blood. Cytogenetic abnormalities are crucial for the prognosis of AML. However, since more than half of patients with AML are cytogenetically normal AML (CN-AML), predictive prognostic indicators need to be further refined. In recent years, gene abnormalities are considered to be strong prognostic factors of CN-AML, already having clinical significance for treatment. In addition, the relationship of methylation in some genes and AML prognosis predicting has been discovered. RASGEF1A is a guanine nucleotide exchange factors of Ras and widely expressed in brain tissue, bone marrow and 17 other tissues. RASGEF1A has been reported to be associated with a variety of malignant tumors, examples include Hirschsprung disease, renal cell carcinoma, breast cancer, diffuse large B cell lymphoma, intrahepatic cholangiocarcinoma and so on [1, 2]. However, the relationship between the RASGEF1A gene and CN-AML has not been reported. METHODS: By integrating the Cancer Genome Atlas (TCGA) database 75 patients with CN-AML and 240 Gene Expression Omnibus (GEO) database CN-AML samples, we examined the association between RASGEF1A's RNA expression level and DNA methylation of and AML patients' prognosis. Then, we investigated the RASGEF1A RNA expression and DNA methylation's prognostic value in 77 patients with AML after allogeneic hematopoietic stem cell transplantation (Allo-HSCT) as well as 101 AML patients after chemotherapy respectively. We investigated the association between sensitivity to Crenolanib and expression level of RASGED1A in patients by integrating 191 CN-AML patients from BeatAML dadataset. We integrated the expression and methylation of RASGEF1A to predict the CN-AML patients' prognosis and investigated the relationship between prognostic of AML patients with different risk classification and expression levels or methylation levels of RASGEF1A. RESULTS: We found that RASGEF1A gene high expression group predicted poorer event-free survival (EFS) (P< 0.0001) as well as overall survival (OS) (P< 0.0001) in CN-AML samples, and the identical results were found in AML patients receiving chemotherapy (P< 0.0001) and Allo-HSCT (P< 0.0001). RASGEF1A RNA expression level is an CN-AML patients' independent prognostic factor (EFS: HR = 5.5534, 95% CI: 1.2982-23.756, P= 0.0208; OS: HR = 5.3615, 95% CI: 1.1014-26.099, P= 0.0376). The IC50 (half maximal inhibitory concentration) of Crenolanib of CN-AML samples with RASGEF1A high expression level is lower. In addition, patients with high RASGEF1A methylation level had significant favorable prognosis (EPS: P< 0.0001, OS: P< 0.0001). Furthermore, the integrative analysis of expression and methylation of RASGEF1A could classify CN-AML patients into subgroups with different prognosis (EFS: P= 0.034, OS: P= 0.0024). Expression levels or methylation levels of RASGEF1A help to improve risk classification of 2010 European Leukemia Net. CONCLUSION: Higher RASGEF1A RNA expression and lower DNA methylation predicts CN-AML patients' poorer prognosis. The RASGEF1A high expression level from patients with CN-AML have better sensitivity to Crenolanib. The integrative analysis of RASGEF1A RNA expression and DNA methylation can provide a more accurate classification for prognosis. Lower RASGEF1A expression is a favorable prognostic factor for AML patients receiving chemotherapy or Allo-HSCT. 2010 European Leukemia Net's risk classification can be improved by RASGEF1A expression levels or methylation levels.

Global characterization of megakaryocytes in bone marrow, peripheral blood, and cord blood by single-cell RNA sequencing.

Megakaryocytes (MK) are mainly derived from bone marrow and are mainly involved in platelet production. Studies have shown that MK derived from bone marrow may have immune function, and that MK from peripheral blood are associated with prostate cancer. Single-cell transcriptome sequencing can help us better understand the heterogeneity and potential function of MK cell populations in bone marrow (BM), peripheral Blood (PB), and cord blood (CB) of healthy and diseased people.We integrated more than 1.2 million single-cell transcriptome data from 132 samples of PB, BM, and CB from healthy individuals and patients from different dataset. We examined the MK (including MK and product of MK) by single-cell RNA sequencing data analysis methods and identification of MK-related protein expression by the Human Protein atlas. We investigate the relationship between the MK subtype and Non-Small Cell Lung Cancer (NSCLC) in 77 non-cancer and 402 NSCLC. We found that MK were widely distributed and the amount of MK in peripheral blood was more than that in bone marrow and there were specificity MK subtypes in peripheral blood. We found classical MK1 with typical MK characteristics and non-classical MK2 closely related to immunity which was the most common subtype in bone marrow and cord blood. Classical MK1 was closely related to Non-Small Cell Lung Cancer (NSCLC) and can be used as a diagnostic marker. MK2 may have potential adaptive immune function and play a role in tumor NSCLC and autoimmune diseases Systemic Lupus Erythematosus. MK have 14 subtypes and are widely distributed in PB, CB, and BM. MK subtypes are closely related to immunity and have potential to be a diagnostic indicator of NSCLC.

5-Hydroxymethylcytosine profiles of cfDNA are highly predictive of R-CHOP treatment response in diffuse large B cell lymphoma patients.

BACKGROUND: Although R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) remains the standard chemotherapy regimen for diffuse large B cell lymphoma (DLBCL) patients, not all patients are responsive to the scheme, and there is no effective method to predict treatment response. METHODS: We utilized 5hmC-Seal to generate genome-wide 5hmC profiles in plasma cell-free DNA (cfDNA) from 86 DLBCL patients before they received R-CHOP chemotherapy. To investigate the correlation between 5hmC modifications and curative effectiveness, we separated patients into training (n = 56) and validation (n = 30) cohorts and developed a 5hmC-based logistic regression model from the training cohort to predict the treatment response in the validation cohort. RESULTS: In this study, we identified thirteen 5hmC markers associated with treatment response. The prediction performance of the logistic regression model, achieving 0.82 sensitivity and 0.75 specificity (AUC = 0.78), was superior to existing clinical indicators, such as LDH and stage. CONCLUSIONS: Our findings suggest that the 5hmC modifications in cfDNA at the time before R-CHOP treatment are associated with treatment response and that 5hmC-Seal may potentially serve as a clinical-applicable, minimally invasive approach to predict R-CHOP treatment response for DLBCL patients.