Identification of key gene modules and hub genes of human mantle cell lymphoma by coexpression network analysis

Transcriptomic and Metabolomics Joint Analyses Reveal the Influence of Gene and Metabolite Expression in Blood on the Lactation Performance of Dual-Purpose Cattle (Bos taurus)

Blood is an important component for maintaining animal lives and synthesizing sugars, lipids, and proteins in organs. Revealing the relationship between genes and metabolite expression and milk somatic cell count (SCC), milk fat percentage, milk protein percentage, and lactose percentage in blood is helpful for understanding the molecular regulation mechanism of milk formation. Therefore, we separated the buffy coat and plasma from the blood of Xinjiang Brown cattle (XJBC) and Chinese Simmental cattle (CSC), which exhibit high and low SCC/milk fat percentage/milk protein percentage/lactose percentages, respectively. The expression of genes in blood and the metabolites in plasma was detected via RNA-Seq and LC-MS/MS, respectively. Based on the weighted gene coexpression network analysis (WGCNA) and functional enrichment analysis of differentially expressed genes (DEGs), we further found that the expression of genes in the blood mainly affected the SCC and milk fat percentage. Immune or inflammatory-response-related pathways were involved in the regulation of SCC, milk fat percentage, milk protein percentage, and lactose percentage. The joint analysis of the metabolome and transcriptome further indicated that, in blood, the metabolism pathways of purine, glutathione, glycerophospholipid, glycine, arginine, and proline are also associated with SCC, while lipid metabolism and amino-acid-related metabolism pathways are associated with milk fat percentage and milk protein percentage, respectively. Finally, related SCC, milk fat percentage, and milk protein percentage DEGs and DEMs were mainly identified in the blood.

Transcriptome meta-analysis reveals the hair genetic rules in six animal breeds and genes associated with wool fineness

Wool plays an irreplaceable role in the lives of livestock and the textile industry. The variety of hair quality and shape leads to the diversity of its functions and applications, and the finer wool has a higher economic value. In this study, 10 coarse and 10 fine ordos fine wool sheep skin samples were collected for RNA-seq, and coarse and fine skin/hair follicle RNA-seq datasets of other five animal breeds were obtained from NCBI. Weighted gene co-expression network analysis showed that the common genes were clustered into eight modules. Similar gene expression patterns in sheep and rabbits with the same wool types, different gene expression patterns in animal species with different hair types, and brown modules were significantly correlated with species and breeds. GO and KEGG enrichment analyses showed that, most genes in the brown module associated with hair follicle development. Hence, gene expression patterns in skin tissues may determine hair morphology in animal. The analysis of differentially expressed genes revealed that 32 highly expressed candidate genes associated with the wool fineness of Ordos fine wool sheep. Among them, KAZALD1 (grey module), MYOC (brown module), C1QTNF6 (brown module), FOS (tan module), ITGAM, MX2, MX1, and IFI6 genes have been reported to be involved in the regulation of the hair follicle cycle or hair loss. Additionally, 12 genes, including KAZALD1, MYOC, C1QTNF6, and FOS, are differentially expressed across various animal breeds and species. The above results suggest that different sheep breeds share a similar molecular regulatory basis of wool fineness. Finally, the study provides a theoretical reference for molecular breeding of sheep breeds as well as for the investigation of the origin and evolution of animal hair.

Intramolecular autoinhibition regulates the selectivity of PRPF40A tandem WW domains for proline-rich motifs

PRPF40A plays an important role in the regulation of pre-mRNA splicing by mediating protein-protein interactions in the early steps of spliceosome assembly. By binding to proteins at the 5´ and 3´ splice sites, PRPF40A promotes spliceosome assembly by bridging the recognition of the splices. The PRPF40A WW domains are expected to recognize proline-rich sequences in SF1 and SF3A1 in the early spliceosome complexes E and A, respectively. Here, we combine NMR, SAXS and ITC to determine the structure of the PRPF40A tandem WW domains in solution and characterize the binding specificity and mechanism for proline-rich motifs recognition. Our structure of the PRPF40A WW tandem in complex with a high-affinity SF1 peptide reveals contributions of both WW domains, which also enables tryptophan sandwiching by two proline residues in the ligand. Unexpectedly, a proline-rich motif in the N-terminal region of PRPF40A mediates intramolecular interactions with the WW tandem. Using NMR, ITC, mutational analysis in vitro, and immunoprecipitation experiments in cells, we show that the intramolecular interaction acts as an autoinhibitory filter for proof-reading of high-affinity proline-rich motifs in bona fide PRPF40A binding partners. We propose that similar autoinhibitory mechanisms are present in most WW tandem-containing proteins to enhance binding selectivity and regulation of WW/proline-rich peptide interaction networks.

Never a dull enzyme, RNA polymerase II

RNA polymerase II (Pol II) is composed of 12 subunits that collaborate to synthesize mRNA within the nucleus. Pol II is widely recognized as a passive holoenzyme, with the molecular functions of its subunits largely ignored. Recent studies employing auxin-inducible degron (AID) and multi-omics techniques have revealed that the functional diversity of Pol II is achieved through the differential contributions of its subunits to various transcriptional and post-transcriptional processes. By regulating these processes in a coordinated manner through its subunits, Pol II can optimize its activity for diverse biological functions. Here, we review recent progress in understanding Pol II subunits and their dysregulation in diseases, Pol II heterogeneity, Pol II clusters and the regulatory roles of RNA polymerases.

p53 directly downregulates the expression of CDC20 to exert anti-tumor activity in mantle cell lymphoma

BACKGROUND

Cell cycle dysregulation characterized by cyclin D1 overexpression is common in mantle cell lymphoma (MCL), while mitotic disorder was less studied. Cell division cycle 20 homologue (CDC20), an essential mitotic regulator, was highly expressed in various tumors. Another common abnormality in MCL is p53 inactivation. Little was known about the role of CDC20 in MCL tumorigenesis and the regulatory relationship between p53 and CDC20 in MCL.

METHODS

CDC20 expression was detected in MCL patients and MCL cell lines harboring mutant p53 (Jeko and Mino cells) and wild-type p53 (Z138 and JVM2 cells). Z138 and JVM2 cells were treated with CDC20 inhibitor apcin, p53 agonist nutlin-3a, or in combination, and then cell proliferation, cell apoptosis, cell cycle, cell migration and invasion were determined by CCK-8, flow cytometry and Transwell assays. The regulatory mechanism between p53 and CDC20 was revealed by dual-luciferase reporter gene assay and CUT&Tag technology. The anti-tumor effect, safety and tolerability of nutlin-3a and apcin were investigated in vivo in the Z138-driven xenograft tumor model.

RESULTS

CDC20 was overexpressed in MCL patients and cell lines compared with their respective controls. The typical immunohistochemical marker of MCL patients, cyclin D1, was positively correlated with CDC20 expression. CDC20 high expression indicated unfavorable clinicopathological features and poor prognosis in MCL patients. In Z138 and JVM2 cells, either apcin or nutlin-3a treatment could inhibit cell proliferation, migration and invasion, and induce cell apoptosis and cell cycle arrest. GEO analysis, RT-qPCR and WB results showed that p53 expression was negatively correlated with CDC20 expression in MCL patients, Z138 and JVM2 cells, while this relationship was not observed in p53-mutant cells. Dual-luciferase reporter gene assay and CUT&Tag assay revealed mechanistically that CDC20 was transcriptionally repressed by p53 through directly binding p53 to CDC20 promoter from - 492 to + 101 bp. Moreover, combined treatment of nutlin-3a and apcin showed better anti-tumor effect than single treatment in Z138 and JVM2 cells. Administration of nutlin-3a/apcin alone or in combination confirmed their efficacy and safety in tumor-bearing mice.

CONCLUSIONS

Our study validates the essential role of p53 and CDC20 in MCL tumorigenesis, and provides a new insight for MCL therapeutics through dual-targeting p53 and CDC20.

Clinical Prognostic Implications of Wnt Hub Genes Expression in Medulloblastoma

Medulloblastoma is the most common type of pediatric malignant primary brain tumor, and about one-third of patients die due to disease recurrence and most survivors suffer from long-term side effects. MB is clinically, genetically, and epigenetically heterogeneous and subdivided into at least four molecular subgroups: WNT, SHH, Group 3, and Group 4. We evaluated common differentially expressed genes between a Brazilian RNA-seq GSE181293 dataset and microarray GSE85217 dataset cohort of pediatric MB samples using bioinformatics methodology in order to identify hub genes of the molecular subgroups based on PPI network construction, survival and functional analysis. The main finding was the identification of five hub genes from the WNT subgroup that are tumor suppressors, and whose lower expression is related to a worse prognosis for MB patients. Furthermore, the common genes correlated with the five tumor suppressors participate in important pathways and processes for tumor initiation and progression, as well as development and differentiation, and some of them control cell stemness and pluripotency. These genes have not yet been studied within the context of MB, representing new important elements for investigation in the search for therapeutic targets, prognostic markers or for understanding of MB biology.

First Report of FARSA in the Regulation of Cell Cycle and Survival in Mantle Cell Lymphoma Cells via PI3K-AKT and FOXO1-RAG1 Axes

Cancer-associated factors have been largely identified in the understanding of tumorigenesis and progression. However, aminoacyl-transfer RNA (tRNA) synthetases (aaRSs) have so far been neglected in cancer research due to their canonical activities in protein translation and synthesis. FARSA, the alpha subunit of the phenylalanyl-tRNA synthetase is elevated across many cancer types, but its function in mantle cell lymphoma (MCL) remains undetermined. Herein, we found the lowest levels of FARSA in patients with MCL compared with other subtypes of lymphomas, and the same lower levels of FARSA were observed in chemoresistant MCL cell lines. Unexpectedly, despite the essential catalytic roles of FARSA, knockdown of FARSA in MCL cells did not lead to cell death but resulted in accelerated cell proliferation and cell cycle, whereas overexpression of FARSA induced remarkable cell-cycle arrest and overwhelming apoptosis. Further RNA sequencing (RNA-seq) analysis and validation experiments confirmed a strong connection between FARSA and cell cycle in MCL cells. Importantly, FARSA leads to the alteration of cell cycle and survival via both PI3K-AKT and FOXO1-RAG1 axes, highlighting a FARSA-mediated regulatory network in MCL cells. Our findings, for the first time, reveal the noncanonical roles of FARSA in MCL cells, and provide novel insights into understanding the pathogenesis and progression of B-cell malignancies.

Identification of immune-related genes and development of a prognostic model in mantle cell lymphoma

Background

The immune landscape, prognostic model, and molecular variations of mantle cell lymphoma (MCL) remain unclear. Hence, an integrated bioinformatics analysis of MCL datasets is required for the development of immunotherapy and the optimization of targeted therapies.

Methods

Data were obtained from the Gene Expression Omnibus (GEO) database (GSE32018, GSE45717 and GSE93291). The differentially expressed immune-related genes were selected, and the hub genes were screened by three machine learning algorithms, followed by enrichment and correlation analyses. Next, MCL molecular clusters based on the hub genes were identified by K-Means clustering, the probably approximately correct (PAC) algorithm, and principal component analysis (PCA). The landscape of immune cell infiltration and immune checkpoint molecules in distinct clusters was explored by single-sample gene-set enrichment analysis (ssGSEA) as well as the CIBERSORT and xCell algorithms. The prognostic genes and prognostic risk score model for MCL clusters were identified by least absolute shrinkage and selection operator (LASSO)-Cox analysis and cross-validation for lambda. Correlation analysis was performed to explore the correlation between the screened prognostic genes and immune cells or immune checkpoint molecules.

Results

Four immune-related hub genes (CD247, CD3E, CD4, and GATA3) were screened in MCL, mainly enriched in the T-cell receptor signaling pathway. Based on the hub genes, two MCL molecular clusters were recognized. The cluster 2 group had a significantly worse overall survival (OS), with down-regulated hub genes, and a variety of activated immune effector cells declined. The majority of immune checkpoint molecules had also decreased. An efficient prognostic model was established, including six prognostic genes (LGALS2, LAMP3, ICOS, FCAMR, IGFBP4, and C1QA) differentially expressed between two MCL clusters. Patients with a higher risk score in the prognostic model had a poor prognosis. Furthermore, most types of immune cells and a range of immune checkpoint molecules were positively correlated with the prognostic genes.

Conclusions

Our study identified distinct molecular clusters based on the immune-related hub genes, and showed that the prognostic model affected the prognosis of MCL patients. These hub genes, modulated immune cells, and immune checkpoint molecules might be involved in oncogenesis and could be potential prognostic biomarkers in MCL.

CDC20 in and out of mitosis: a prognostic factor and therapeutic target in hematological malignancies

Cell division cycle 20 homologue (CDC20) is a well-known regulator of cell cycle, as it controls the correct segregation of chromosomes during mitosis. Many studies have focused on the biological role of CDC20 in cancer development, as alterations of its functionality have been linked to genomic instability and evidence demonstrated that high CDC20 expression levels are associated with poor overall survival in solid cancers. More recently, novel CDC20 functions have been demonstrated or suggested, including the regulation of apoptosis and stemness properties and a correlation with immune cell infiltration. Here, we here summarize and discuss the role of CDC20 inside and outside mitosis, starting from its network of interacting proteins. In the last years, CDC20 has also attracted more interest in the blood cancer field, being overexpressed and showing an association with prognosis both in myeloid and lymphoid malignancies. Preclinical findings showed that selective CDC20 and APC/CCDC20/APC/CCDH1 inhibitors, namely Apcin and proTAME, are effective against lymphoma and multiple myeloma cells, resulting in mitotic arrest and apoptosis and synergizing with clinically-relevant drugs. The evidence and hypothesis presented in this review provide the input for further biological and chemical studies aiming to dissect novel potential CDC20 roles and targeting strategies in hematological malignancies.

The Predictive Competing Endogenous RNA Regulatory Networks and Potential Prognostic and Immunological Roles of Cyclin A2 in Pan-Cancer Analysis

Although accumulating evidence has verified the relationship between CCNA2 and cancers, no pan-cancer analysis about the function and the upstream molecular mechanism of CCNA2 is available. For the first time, we analyzed potential oncogenic roles of CCNA2 in 33 cancer types via The Cancer Genome Atlas (TCGA) database. Overexpression of CCNA2 is widespread in almost all cancer types, and it is related to poor prognosis and advanced pathological stages in most cases. Moreover, we conducted upstream miRNAs and lncRNAs of CCNA2 to establish upstream regulatory networks in kidney renal clear cell carcinoma (LINC00997/miR-27b-3p/CCNA2), liver hepatocellular carcinoma (SNHG16, GUSBP11, FGD5-AS1, LINC00630, CD27-AS1, LINC00997/miR-22-3p/CCNA2, miR-29b-3p/CCNA2, miR-29c-3p/CCNA2, and miR-204-5p/CCNA2), and lung adenocarcinoma (miRNA-218-5p/CCNA2 and miR-204-5p/CCNA2) by expression analysis, survival analysis, and correlation analysis. The CCNA2 expression is positively correlated with Th2 cell infiltration and negatively correlated with CD4+ central memory and effector memory T-cell infiltration in different cancer types. Furthermore, CCNA2 is positively associated with expressions of immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT) in most cancer types. Our first CCNA2 pan-cancer study contributes to understanding the prognostic and immunological roles and potential upstream molecular mechanisms of CCNA2 in different cancers.

CHN1 is a Novel Prognostic Marker for Diffuse Large B-Cell Lymphoma

PURPOSE

Diffuse large B-cell lymphoma (DLBCL) is the most common B-cell malignancy. Thirty to forty percent of DLBCL patients still experience relapse or develop refractory disease even with standard immunochemotherapy, leading to a poor prognosis. Currently, although several gene-based classification methods can be used to predict the prognosis of DLBCL, some patients are still unable to be classified. This study was performed to identify a novel prognostic biomarker for DLBCL.

PATIENTS AND METHODS

A total of 1850 B-cell non-Hodgkin lymphoma (B-NHL) patients in 8 independent datasets with microarray gene expression profiles were retrieved from the Gene Expression Omnibus (GEO) database and Lymphoma/Leukemia Molecular Profiling Project (LLMPP). The candidate genes were selected through three filters in a strict pipeline. Survival analysis was performed in two independent datasets of patients with both gene expression data and clinical information. Gene set enrichment analysis (GSEA) and the CIBERSORT algorithm were used to explore the biological functions of the genes.

RESULTS

We identified 6 candidate genes associated with the clinical outcome of DLBCL patients: CHN1, CD3D, CLU, ICOS, KLRB1 and LAT. Unlike the other five genes, CHN1 has not been previously reported to be implicated in lymphoma. We also observed that CHN1 had prognostic significance in important clinical subgroups; in particular, high CHN1 expression was significantly related to good outcomes in DLBCL patients with the germinal center B-cell-like (GCB) subtype, stage III-IV, or an International Prognostic Index (IPI) score > 2. Multivariate Cox regression analysis of the two datasets showed that CHN1 was an independent prognostic factor for DLBCL. Additionally, GSEA and CIBERSORT indicated that CHN1 was correlated with cell adhesion and T cell immune infiltration.

CONCLUSION

Our data indicate for the first time that high CHN1 expression is associated with favorable outcomes in DLBCL patients, suggesting its potential utility as a prognostic marker in DLBCL.

Identification of Key Pathways and Genes Related to the Development of Hair Follicle Cycle in Cashmere Goats

The development of hair follicle in cashmere goats shows significant periodic change, as with mice and humans. However, for cashmere goat with double-coat, the periodic change may be due to other regulatory molecules and signal pathways. To understand the mechanism of periodic development of hair follicle, we performed a weighted gene coexpression network analysis (WGCNA) to mine key genes and establish an interaction network by utilizing the NCBI public dataset. Ten coexpression modules, including 7689 protein-coding genes, were constructed by WGCNA, six of which are considered to be significantly related to the development of the hair follicle cycle. A functional enrichment analysis for each model showed that they are closely related to ECM- receptor interaction, focal adhesion, PI3K-Akt signaling pathway, estrogen signaling pathway, and so on. Combined with the analysis of differential expressed genes, 12 hub genes from coexpression modules were selected as candidate markers, i.e., COL1A1, C1QTNF6, COL1A2, AQP3, KRTAP3-1, KRTAP11-1, FA2H, NDUFS5, DERL2, MRPL14, ANTKMT and XAB2, which might be applied to improve cashmere production.