Comparative and Functional Genomics最新文献

Lower-grade gliomas (LGG) are the most common intracranial malignancies that readily evolve to high-grade gliomas and increase drug resistance. Paraptosis is defined as a nonapoptotic form of programmed cell death, which is gradually focused on patients with gliomas to develop treatment options. However, the specific role of paraptosis in LGG and its correlation is still vague. In this study, we first establish the novel paraptosis-based prognostic model for LGG patients. The relevant data of LGG patients were acquired from The Cancer Genome Atlas database, and we found that LGG patients could be divided into three different clusters based on paraptosis via consensus cluster analysis. Through least absolute shrinkage and selection operator regression analysis and multivariate Cox regression analysis, 10-paraptosis-related gene (PRG) signatures (CDK4, TNK2, DSTYK, CDKN3, CCR4, CASP9, HSPA5, RGR, LPAR1, and PDCD6IP) were identified to separate LGG patients into high- and low-risk subgroups successfully. The Kaplan–Meier analysis and time-dependent receiver-operating characteristic showed that the performances of predicting overall survival (OS) were dramatically high. The parallel results were reappeared and verified by using the Chinese Glioma Genome Atlas and Gene Expression Omnibus databases. Independent prognostic analysis and nomogram construction implied that risk scores could be considered the independent factor to predict OS. Enrichment analysis indicated that immune-related biological processes were generally enriched, and different immune statuses were highly infiltrated in high-risk group. We also confirmed the potential relationship of 10-PRG signatures and drug sensitivity of Food and Drug Administration–approved drugs. In summary, our findings provide a novel knowledge of paraptosis status and crucial direction to further explore the role of PRG signatures in LGG.

Aim. The aim of this study is to demonstrate the expression and clinicopathological significance of complement C1q B chain (C1QB) in cervical cancer. Methods. In total, 120 cervical cancer tissues, as well as 20 samples each of high-grade squamous intraepithelial lesions (HSILs), low-grade squamous intraepithelial lesions (LSILs), and benign cervical tissue, were collected to evaluate the expression of C1QB protein via immunohistochemical staining. We conducted an integrated analysis of C1QB mRNA expression in cervical cancer using public microarrays and RNA-seq data sets by calculating standard mean differences (SMDs). Simultaneously, we explored the relations of C1QB with clinicopathological parameters and the expression of P16, Ki-67, and P53. Results. The expression of C1QB protein was higher in cervical cancer samples than that in benign cervical tissue, LSIL, and HSIL samples (p < 0.05). A combined SMD of 0.65 (95% CI: [0.52, 0.79], p < 0.001) revealed upregulation of C1QB mRNA in cervical cancer. C1QB expression may also be related to the depth of infiltration, lymphovascular invasion, and perineural invasion in cervical cancer (p < 0.05). We also found that C1QB protein expression was positively correlated with P16 and Ki-67 expression in cervical cancer (p < 0.05). The gene set enrichment analysis showed that C1QB may participate in apoptosis and autophagy. A relationship was predicted between C1QB expression and drug sensitivity to cisplatin, paclitaxel, and docetaxel. Conclusion. We confirmed the overexpression of C1QB in cervical cancer at both mRNA and protein levels for the first time. C1QB may serve as an oncogene in the tumorigenesis of cervical cancer, but this possibility requires further study.

ALDH1A3 and Linc00284 involve in colorectal cancer (CRC) development; however, the regulatory mechanism is still unclear. In this study, we collected clinicopathological characteristics and tissue samples from 73 CRC patients to analyze the expression of ALDH1A3, Linc00284, TGFβ signaling and miR-361-5p using qPCR, Western blotting, and ELISA. Multiple CRC cell lines were evaluated in this study, and the highest level of ALDH1A3 was observed in SW480 cells. To investigate the regulatory mechanism, RIP and luciferase assays were used to validate the interaction between Linc00284, miR-361-5p, and TGFβ. Proliferation, viability, migration, and invasion assays were performed to profile the effects of the ALDH1A3–Linc00284 axis in CRC cell functions, which was upregulated in CRC tissues. Knockdown ALDH1A3 or Linc00284 significantly reduced TGFβ expression and suppressed the EMT process, while overexpression had opposite effects. miR-361-5p targeted TGFβ directly, which negatively correlated with ALDH1A3–Linc00284 expression and CRC progression. Mechanistically, upregulation of ALDH1A3–Linc00284 promotes colorectal cancer invasion and migration by regulating miR-361-5p/TGFβ signaling pathway. Dysregulation of the ALDH1A3–Linc00284-miR-361-5p-TGFβ axis causes CRC invasion, which might provide a new insight into the treatment of CRC.

Assessing the impact of variants of unknown significance on splicing has become a critical issue and a bottleneck, especially with the widespread implementation of whole-genome or exome sequencing. Although multiple in silico tools are available, the interpretation and application of these tools are difficult and practical guidelines are still lacking. A streamlined decision-making process can facilitate the downstream RNA analysis in a more efficient manner. Therefore, we evaluated the performance of 8 in silico tools (Splice Site Finder, MaxEntScan, Splice-site prediction by neural network, GeneSplicer, Human Splicing Finder, SpliceAI, Splicing Predictions in Consensus Elements, and SpliceRover) using 114 NF1 spliceogenic variants, experimentally validated at the mRNA level. The change in the predicted score incurred by the variant of the nearest wild-type splice site was analyzed, and for type II, III, and IV splice variants, the change in the prediction score of de novo or cryptic splice site was also analyzed. SpliceAI and SpliceRover, tools based on deep learning, outperformed all other tools, with AUCs of 0.972 and 0.924, respectively. For de novo and cryptic splice sites, SpliceAI outperformed all other tools and showed a sensitivity of 95.7% at an optimal cut-off of 0.02 score change. Our results show that deep learning algorithms, especially those of SpliceAI, are validated at a significantly higher rate than other in silico tools for clinically relevant NF1 variants. This suggests that deep learning algorithms outperform traditional probabilistic approaches and classical machine learning tools in predicting the de novo and cryptic splice sites.

Long non-coding RNA (LncRNA) emerges as a regulator in various diseases, including endometriosis (EM). This study aims to uncover the role of long non-coding RNA BRAF-activated non-protein coding RNA (lncRNA BANCR)-mediated competing endogenous RNA mechanism in endometrial stromal cell (ESC) proliferation and invasion in EM by regulating miR-15a-5p/TRIM59. ESCs were isolated from eutopic and ectopic endometrial tissues, followed by the determination of Cytokeratin 19 and Vimentin expressions in cells. Then, expressions of lncRNA BANCR, microRNA (miR)-15a-5p, and tripartite motif-containing 59 (TRIM59) in tissues and cells were determined by real-time quantitative polymerase chain reaction or Western blot assay, and cell proliferation and invasion were evaluated by cell counting kit-8 and transwell assays. After that, the subcellular localization of lncRNA BANCR and binding of miR-15a-5p to lncRNA BANCR or TRIM59 were analyzed. LncRNA BANCR was upregulated in ectopic endometrial tissues and ectopic ESCs (Ect-ESCs). Silencing lncRNA BANCR suppressed Ect-ESC proliferation and invasion. LncRNA BANCR inhibited miR-15a-5p to promote TRIM59 expression. miR-15a-5p downregulation or TRIM59 overexpression both reversed the effects of silencing lncRNA BANCR on Ect-ESC proliferation and invasion. In summary, our findings suggested that lncRNA BANCR facilitated Ect-ESC proliferation and invasion by inhibiting miR-15a-5p and promoting TRIM59.

Ischemic stroke (IS) is one of the leading causes of disability and mortality worldwide. This study aims to find the crucial exosomal miRNAs associated with IS by using bioinformatics methods, reveal potential biomarkers for IS, and investigate the association between the identified biomarker and immune cell pattern in the peripheral blood of IS patients. In this study, 3 up-regulated miRNAs (hsa-miR-15b-5p, hsa-miR-184, and hsa-miR-16-5p) miRNAs in the serum exosomes between IS patients and healthy controls from GEO database (GSE199942) and 25 down-regulated genes of peripheral blood mononuclear cells of IS patients from GSE22255 were obtained with the help of the R software. GO annotation and KEGG pathway enrichment analysis showed that the 25 down-regulated genes were associated with coenzyme metabolic process and were mainly enriched in the N-glycan biosynthesis pathway. Furthermore, we performed the LASSO algorithm to narrow down the above 25 intersected genes, and identified 8 key genes which had a good diagnostic value in discriminating IS patients from the healthy controls analyzed with ROC curve. CIBERSORT algorithm indicated that the abundance of M0 macrophages and resting mast cells was significantly lower than that of the control group. The spearman correlation analysis showed that STT3A was negatively correlated with the proportion of follicular helper T cells, activated NK cells and resting dendritic cells. Finally, GSE117064 showed that has-miR-16-5p was more advantageous for diagnosing stroke. In conclusion, hsa-miR-15b-5p, hsa-miR-184, and hsa-miR-16-5p are identified as specific related exosomal miRNAs for IS patients. These genes may provide new targets for the early identification of IS.

Coxsackievirus B5 (CVB5) is the causative agent of hand, foot, and mouth disease (HFMD) that can cause neurological complications and fatalities. Circular RNA (circRNA) has been shown to play an important role in regulating pathogenic processes. However, the functions of circRNA in response to CVB5 infection remain unclear. In our research, RNA-seq was employed to analyze the expression profiles of circRNAs in SH-SY5Y cells with or without CVB5 infection. Out of 5,665 circRNAs identified to be expressed in SH-SY5Y cells, 163 circRNAs were found to be differentially expressed significantly. Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that the differentially expressed circRNAs were mainly involved in ubiquitin-mediated proteolysis and signaling pathways during CVB5 infection. Additionally, RT-qPCR was used to validate the RNA-seq data, and a circRNA–miRNA–mRNA interaction network was constructed based on two circRNAs, such as hsa_circ_0008378 and novel_circ_0014617, which were associated with the regulation of innate immune response in host cells. Additionally, we confirmed the two circRANs up-regulated the key factors in the IFN-I signaling pathway, hampering viral replication. Our data provide a new perspective that facilitates further understanding of the virus-host mechanism.

Background. Non-small cell lung cancer (NSCLC) is one of the most prevalent cancers, accounting for around 80% of total lung cancer cases worldwide. Exploring the function and mechanism of circRNAs could provide insights into the diagnosis and treatment for NSCLC. Methods. In this study, we collected tumor tissues and adjacent normal tissues from NSCLC patients to detect the expression level of circPTN and analyzed the association of its expression level with the clinicopathological parameter of NSCLC patients. Moreover, the functional engagement of circPTN in NSCLC cells was examined by cell counting kit-8 (CCK-8) cell proliferation assay, transwell migration and invasion assays, and tube formation assay. Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting (WB) analysis were used to detect gene and protein expression, respectively. The molecular targets of cicrPTN were predicted using starBase online resources, which was validated by RNA immunoprecipitation (RIP) and dual-luciferase reporter assay. Results. Compared with adjacent normal tissues, there was a remarkable increase of the circPTN levels in NSCLC tissues. A high level of circPTN expression was associated with more lymph node metastasis (LNM) and advanced TNM stages. Functionally, circPTN knockdown inhibited the proliferation, migration, and invasion and tube formation ability of NSCLC cells. We further demonstrated that circPTN regulated the malignant phenotype of NSCLC cells through targeting the miR-432-5p/E2F2 axis. Conclusion. Together, our results suggest that circPTN, which is upregulated in NSCLC tissues, could serve as a prognostic marker for NSCLC patients. circPTN regulates the malignant progression of NSCLC cells through targeting the miR-432-5p/E2F2 axis, which may be employed as a potential strategy for the management of NSCLC.

Microbial genes and their product were diverse and beneficial for heavy metal bioremediation from the contaminated sites. Screening of genes and gene products plays a significant role in the detoxification of pollutants. Understanding of the promoter region and its regulatory elements is a vital implication of microbial genes. To the best of our knowledge, there is no in silico study reported so far on mer gene families used for heavy metal bioremediation. The motif distribution was observed densely upstream of the TSSs (transcription start sites) between +1 and -350 bp and sparsely distributed above -350 bp, according to the current study. MEME identified the best common candidate motifs of TFs (transcription factors) binding with the lowest e value (7.2e-033) and is the most statistically significant candidate motif. The EXPREG output of the 11 TFs with varying degrees of function such as activation, repression, transcription, and dual purposes was thoroughly examined. Data revealed that transcriptional gene regulation in terms of activation and repression was observed at 36.4% and 54.56%, respectively. This shows that most TFs are involved in transcription gene repression rather than activation. Likewise, EXPREG output revealed that transcriptional conformational modes, such as monomers, dimers, tetramers, and other factors, were also analyzed. The data indicated that most of the transcriptional conformation mode was dual, which accounts for 96%. CpG island analysis using online and offline tools revealed that the gene body had fewer CpG islands compared to the promoter regions. Understanding the common candidate motifs, transcriptional factors, and regulatory elements of the mer operon gene cluster using a machine learning approach could help us better understand gene expression patterns in heavy metal bioremediation.