Whole Exome and Transcriptome RNA-Sequencing Model for the Diagnosis of Prostate Cancer

GABPA-Mediated Expression of HPN-AS1 Facilitates Cell Apoptosis and Inhibits Cell Proliferation in Hepatocellular Carcinoma by Promoting eIF4A3 Degradation

Hepatocellular carcinoma (HCC) represents a common neoplasm that presents a substantial worldwide health challenge. Nevertheless, the involvement of HPN-AS1 in HCC remains unknown. The quantitative reverse transcription polymerase chain reaction (qRT-PCR) assay was utilized to measure HPN-AS1 expression in HCC. The GABPA effects on the HPN-AS1 promoter were analyzed through chromatin immunoprecipitation and luciferase reporter assays. Cell proliferation potential was determined by deploying CCK-8 assay, Ki-67 immunofluorescence staining, and colony formation assay. Cell apoptosis was detected using acridine orange/ethidium bromide staining and terminal deoxynucleotidyl transferase dUTP nick end labeling staining. Western blotting was utilized to measure the protein levels of proliferation factors and apoptosis regulators. HPN-AS1 binding to eIF4A3 was accessed by RNA-binding protein immunoprecipitation assay. HPN-AS1 was significantly downregulated in both HCC cells and tissues. Lower HPN-AS1 levels indicate a poorer HCC prognosis. Moreover, we found that GABPA functions as a transcription factor for HPN-AS1. Functional studies revealed that HPN-AS1 displayed inhibitory effects on HCC cell proliferation and promoted apoptosis. Mechanically, HPN-AS1 bound to and facilitated translation initiation factor eIF4A3 degradation. Loss of HPN-AS1 augmented eIF4A3 protein levels rather than eIF4A3 mRNA levels. Exogenous expression of eIF4A3 could restore eIF4A3 protein levels and reverse HPN-AS1 overexpression-induced cell proliferation inhibition and cell apoptosis. Our study elucidated that HPN-AS1 downregulation was mediated by GABPA. HPN-AS acted as a tumor suppressor within HCC through binding and facilitating eIF4A3 degradation. The study provides a novel insight into the biological function of HPN-AS1 in HCC, suggesting that HPN-AS1 could be a promising biomarker and a potential target for HCC diagnosis and treatment.

Screening of Lipid Metabolism-Related Genes as Diagnostic Indicators in Chronic Obstructive Pulmonary Disease

Objective

It has been observed that local and systemic disorders of lipid metabolism occur during the development of chronic obstructive pulmonary disease (COPD), but no specific mechanism has yet been identified.

Methods

The mRNA microarray dataset GSE76925 of COPD patients was downloaded from the Gene Expression Omnibus database and screened for differentially expressed genes (DEGs). Lipid metabolism-related genes (LMRGs) were extracted from the Kyoto Encyclopedia of Genes and Genomes database and Molecular Signature Database. The DEGs were intersected with LMRGs to obtain differentially expressed lipid metabolism-related genes (DeLMRGs). GO enrichment analysis and KEGG pathway analysis were performed on DeLMRGs, and protein-protein interaction networks were constructed and screened to identify hub genes. The GSE8581 validation set and further ELISA experiments were used to validate key DeLMRG expression.

Results

Differential analysis of dataset GSE76925 identified 587 DEGs, of which 62 genes were up-regulated and 525 were down-regulated. Taking the intersection of 587 DEGs with 1102 LMRGs, 20 DeLMRGs were obtained, including 1 up-regulated gene and 19 down-regulated genes. 10 hub genes were screened by cytohubba plugin, including 9 down-regulated genes PLA2G4A, HPGDS, LEP, PTGES3, LEPR, PLA2G2D, MED21, SPTLC1 and BCHE, as well as the only up-regulated gene PLA2G7. Validation of the identified 10 DeLMRGs using the validation set GSE8581 revealed that BCHE and PLA2G7 expression levels differed between the two groups. We further constructed the ceRNA network of BCHE and PLA2G7. Cell experiments also showed that PLA2G7 expression was up-regulated and BCHE expression was down-regulated in CSE-treated RAW264.7 and THP-1 cells.

Conclusion

Based on a comprehensive bioinformatic analysis of lipid metabolism genes, we identified BCHE and PLA2G7 as potentially significant biomarkers of COPD. These biomarkers may represent promising targets for COPD diagnosis and treatment.

Nanopore Sequencing Techniques: A Comparison of the MinKNOW and the Alignator Sequencers

Whole genome and transcriptome analyses represent powerful tools. Despite improvements in sequencing methodology, whole transcriptome analyses are still tedious, especially for methodologies producing long reads. Here, we compare the sequence data analysis software MinKNOW and our tool Alignator. Furthermore, we provide a walk-through from RNA isolation and preparation for MinION sequencing as well as insides in the processing of sequencing data using both tools.

A novel tumor mutational burden-based risk model predicts prognosis and correlates with immune infiltration in ovarian cancer

Tumor mutational burden (TMB) has been reported to determine the response to immunotherapy, thus affecting the patient’s prognosis in many cancers. However, it is unclear whether TMB or TMB-related signature could be used as prognostic indicators for ovarian cancer (OC), as its potential association with immune infiltration remains poorly understood. Therefore, this study aimed to develop a novel TMB-related risk model (TMBrisk) to predict the prognosis of OC patients on the basis of exploring TMB-related genes, and to explore the potential association between TMB/TMBrisk and immune infiltration. The mutational landscape, TMB scores, and correlations between TMB and clinical characteristics and immune infiltration were investigated in The Cancer Genome Atlas (TCGA)-OV cohort. Differentially expressed gene (DEG) analyses and weighted gene co-expression network analysis (WGCNA) were performed to derive TMB-related genes. TMBrisk was constructed by Cox regression and further validated in Gene Expression Omnibus (GEO) datasets. The mRNA and protein expression levels and biological functions of TMBrisk hub genes were verified through Gene Expression Profiling Interactive Analysis (GEPIA), GSCA Lite, the Human Protein Atlas (HPA) database, and RT-qPCR. TMBrisk-related biological phenotypes were analyzed in function enrichment and tumor immune infiltration signature. Potential therapeutic regimens were inferred utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). According to our results, higher TMB was associated with better survival and higher CD8+ T cell, regulatory T cell, and NK cell infiltration. TMBrisk was developed based on CBWD1, ST7L, RFX5-AS1, C3orf38, LRFN1, LEMD1, and HMGB1. High TMBrisk was identified as a poor factor for prognosis in TCGA and GEO datasets; the high-TMBrisk group comprised more higher-grade (G2 and G3) and advanced clinical stage (stage III/IV) tumors. Meanwhile, higher TMBrisk was associated with an immunosuppressive phenotype, with less infiltration of a majority of immunocytes and less expression of several genes of the human leukocyte antigen (HLA) family. Moreover, a nomogram containing TMBrisk showed a strong predictive ability demonstrated by time-dependent ROC analysis. Overall, this novel TMB-related risk model (TMBrisk) could predict prognosis, evaluate immune infiltration, and discover new therapeutic regimens in OC, which is very promising in clinical promotion.

A transcriptome-wide association study identifies novel candidate susceptibility genes for prostate cancer risk

A large proportion of heritability for prostate cancer risk remains unknown. Transcriptome-wide association study combined with validation comparing overall levels will help to identify candidate genes potentially playing a role in prostate cancer development. Using data from the Genotype-Tissue Expression Project, we built genetic models to predict normal prostate tissue gene expression using the statistical framework PrediXcan, a modified version of the unified test for molecular signatures and Joint-Tissue Imputation. We applied these prediction models to the genetic data of 79 194 prostate cancer cases and 61 112 controls to investigate the associations of genetically determined gene expression with prostate cancer risk. Focusing on associated genes, we compared their expression in prostate tumor vs normal prostate tissue, compared methylation of CpG sites located at these loci in prostate tumor vs normal tissue, and assessed the correlations between the differentiated genes' expression and the methylation of corresponding CpG sites, by analyzing The Cancer Genome Atlas (TCGA) data. We identified 573 genes showing an association with prostate cancer risk at a false discovery rate (FDR) ≤ 0.05, including 451 novel genes and 122 previously reported genes. Of the 573 genes, 152 showed differential expression in prostate tumor vs normal tissue samples. At loci of 57 genes, 151 CpG sites showed differential methylation in prostate tumor vs normal tissue samples. Of these, 20 CpG sites were correlated with expression of 11 corresponding genes. In this TWAS, we identified novel candidate susceptibility genes for prostate cancer risk, providing new insights into prostate cancer genetics and biology.

Tumor Mutation Burden-Associated LINC00638/miR-4732-3p/ULBP1 Axis Promotes Immune Escape via PD-L1 in Hepatocellular Carcinoma

Tumor mutation burden (TMB) is associated with immune infiltration, while its underlying mechanism in hepatocellular carcinoma (HCC) remains unclear. A long noncoding RNA (lncRNA)-related competitive endogenous RNA (ceRNA) network can regulate various tumor behaviors, and research about its correlation with TMB and immune infiltration is warranted. Data were downloaded from TCGA and ArrayExpress databases. Cox analysis and machine learning algorithms were employed to establish a lncRNA-based prognostic model for HCC. We then developed a nomogram model to predict overall survival and odds of death for HCC patients. The association of this prognostic model with TMB and immune infiltration was also analyzed. In addition, a ceRNA network was constructed by using DIANA-LncBasev2 and the starBase database and verified by luciferase reporter and colocalization analysis. Multiplex immunofluorescence was applied to determine the correlation between ULBP1 and PD-L1. An eight-lncRNA (SLC25A30-AS1, HPN-AS1, LINC00607, USP2-AS1, HCG20, LINC00638, MKLN1-AS and LINC00652) prognostic score model was constructed for HCC, which was highly associated with TMB and immune infiltration. Next, we constructed a ceRNA network, LINC00638/miR-4732-3p/ULBP1, that may be responsible for NK cell infiltration in HCC with high TMB. However, patients with high ULBP1 possessed a poorer prognosis. Using multiplex immunofluorescence, we found a significant correlation between ULBP1 and PD-L1 in HCC, and patients with high ULBP1 and PD-L1 had the worst prognosis. In brief, the eight-lncRNA model is a reliable tool to predict the prognosis of HCC patients. The LINC00638/miR-4732-3p/ULBP1 axis may regulate immune escape via PD-L1 in HCC with high TMB.

Roles of the Immune/Methylation/Autophagy Landscape on Single-Cell Genotypes and Stroke Risk in Breast Cancer Microenvironment

This study sought to perform integrative analysis of the immune/methylation/autophagy landscape on breast cancer prognosis and single-cell genotypes. Breast Cancer Recurrence Risk Score (BCRRS) and Breast Cancer Prognostic Risk Score (BCPRS) were determined based on 6 prognostic IMAAGs obtained from the TCGA-BRCA cohort. BCRRS and BCPRS, respectively, were used to construct a risk prediction model of overall survival and progression-free survival. Predictive capacity of the model was evaluated using clinical data. Analysis showed that BCRRS is associated with a high risk of stroke. In addition, PPI and drug-ceRNA networks based on differences in BCPRS were constructed. Single cells were genotyped through integrated scRNA-seq of the TNBC samples based on clustering results of BCPRS-related genes. The findings of this study show the potential regulatory effects of IMAAGs on breast cancer tumor microenvironment. High AUCs of 0.856 and 0.842 were obtained for the OS and PFS prognostic models, respectively. scRNA-seq analysis showed high expression levels of adipocytes and adipose tissue macrophages (ATMs) in high BCPRS clusters. Moreover, analysis of ligand-receptor interactions and potential regulatory mechanisms were performed. The LINC00276&MALAT1/miR-206/FZD4-Wnt7b pathway was also identified which may be useful in future research on targets against breast cancer metastasis and recurrence. Neural network-based deep learning models using BCPRS-related genes showed that these genes can be used to map the tumor microenvironment. In summary, analysis of IMAAGs, BCPRS, and BCRRS provides information on the breast cancer microenvironment at both the macro- and microlevels and provides a basis for development of personalized treatment therapy.

Rapid Detection Method for Pathogenic Candida Captured by Magnetic Nanoparticles and Identified Using SERS via AgNPs. Int J Nanomedicine 2021;16:941-950. [PMID: 33603361 PMCID: PMC7884937 DOI: 10.2147/ijn.s285339]

Purpose

Candidemia infection is common in the clinic and has a high mortality rate. Candida albicans, Candida tropicalis, and Candida krusei are very important and common pathogenic species. Candida is difficult to isolate from clinical samples and culture, and immunological detection cannot distinguish these related strains. Furthermore, Candida has a complex cell wall, which causes difficulties in the extraction of DNA for nucleic acid detection. The purpose of this study was to establish a protocol for the direct identification of Candida from serum.

Materials and Methods

We synthesized Fe₃O₄@PEI (where PEI stands for polyethylenimine) magnetic nanoparticles to capture Candida and prepared positively charged silver nanoparticles (AgNPs⁺) as the substrate for surface-enhanced Raman scattering (SERS). Candida was directly identified from serum by SERS detection.

Results

Orthogonal partial least squares discriminant analysis (OPLS-DA) was used as the multivariate analysis tool. Principal component analysis confirmed that this method can clearly distinguish common Candida. After 10-fold cross-validation, the accuracy of training data in this model was 100% and the accuracy of test data was 99.8%, indicating that the model has good classification ability.

Conclusion

The detection could be completed within 40 minutes using Fe₃O₄@PEI and AgNPs⁺ prepared in advance. This is the first time that Fe₃O₄@PEI was used in the detection of Candida by SERS. We report the first rapid method to identify fungi directly from serum without breaking the cell wall to extract DNA from the fungi.

Streamlining Quantitative Analysis of Long RNA Sequencing Reads

Transcriptome analyses allow for linking RNA expression profiles to cellular pathways and phenotypes. Despite improvements in sequencing methodology, whole transcriptome analyses are still tedious, especially for methodologies producing long reads. Currently, available data analysis software often lacks cost- and time-efficient workflows. Although kit-based workflows and benchtop platforms for RNA sequencing provide software options, e.g., cloud-based tools to analyze basecalled reads, quantitative, and easy-to-use solutions for transcriptome analysis, especially for non-human data, are missing. We therefore developed a user-friendly tool, termed Alignator, for rapid analysis of long RNA reads requiring only FASTQ files and an Ensembl cDNA database reference. After successful mapping, Alignator generates quantitative information for each transcript and provides a table in which sequenced and aligned RNA are stored for further comparative analyses.

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk

It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.