Bioinformatics for clinical next generation sequencing

Identification of hub genes affecting gestational diabetes mellitus based on GEO database

This research aimed to obtain gestational diabetes mellitus (GDM) related hub genes, providing new targets for clinical diagnosis and treatment of GDM. The microarray data of GSE9984 and GSE103552 were obtained from the Gene Expression Omnibus (GEO). The dataset GSE9984 contained placental gene expression profiles of 8 GDM patients and four healthy specimens. The dataset GSE103552 contained 20 specimens from GDM patients and 17 normal specimens. The differentially expressed genes (DEGs) were identified by GEO2R online analysis. DAVID database was applied to conduct functional enrichment analysis of the DEGs. The Search Tool for the Retrieval of Interacting Genes (STRING) database was adopted to acquire protein-protein interaction (PPI) networks. A total of 195 up-regulated and 371 down-regulated DEGs were selected in the GSE9984, and total of 191 up-regulated and 229 down-regulated DEGs were selected in the GSE103552. In the two datasets, 24 common differential genes were obtained and named co-DEGs. The Gene Ontology (GO) annotation analysis indicated the DEGs participated in multi-multicellular organism process, endocrine hormone secretion, long-chain fatty acid biosynthetic process, cell division, unsaturated fatty acid biosynthetic process, cell adhesion and cell recognition. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis suggested that GSE9984 and GSE103552 were related to vitamin digestion and absorption, tryptophan metabolism, steroid hormone biosynthesis, Ras signaling pathway, protein digestion and absorption, PPAR signaling pathway, PI3K-Akt signaling pathway, p53 signaling pathway. PPI was constructed in string database, and six hub genes were selected, including CCNB1, APOA2, AHSG and IGFBP1. Four critical genes were identified to be considered as therapeutic potential biomarkers of GDM, including CCNB1, APOA2, AHSG and IGFBP1.

GZMK Facilitates Experimental Rheumatoid Arthritis Progression by Interacting with CCL5 and Activating the ERK Signaling

Synovial over-proliferation is a key event in the progression of rheumatoid arthritis (RA) disease. Ferroptosis may be essential for maintaining the balance between synovial proliferation and death. This study aimed to investigate the molecular mechanisms mediating the activation and ferroptosis of collagen-induced arthritis (CIA)-synovial fibroblasts (SFs). Differentially expressed genes (DEGs) in the synovial tissues of CIA rats and normal rats were screened through sequencing. The GSE115662 dataset from the GEO database was analyzed and screened for DEGs. The viability, proliferation, migration, invasion, cell cycle, and apoptosis of CIA-SFs were analyzed by cell counting kit-8, 5-ethynyl-2'-deoxyuridine, flow cytometry, transwell migration, and invasion assays. The ferroptosis of CIA-SFs was assessed using matching reagent kits to detect indicators like reactive oxygen species, ferrous iron, malondialdehyde, glutathione, and superoxide dismutase. The interaction between Granzyme K (GZMK) and C-C motif chemokine 5 (CCL5) was determined by coimmunoprecipitation assay. We found abnormal GZMK expression in the GSE115662 database and mRNA sequencing data. GZMK was overexpressed in CIA-SFs, and GZMK promoted cell proliferation, migration, invasion, inflammation, and decreased cell apoptosis and ferroptosis in CIA-SFs. GZMK could interact with CCL5 to activate the ERK signaling. GZMK and CCL5 knockdown improved by reducing arthritis scores, redness and swelling of paws, and pathological changes in joint synovium of CIA rats. CCL5 overexpression reversed the effects of GZMK silencing on CIA-SFs cell proliferation, migration, invasion, apoptosis, and ferroptosis. We confirmed that GZMK accelerated experimental rheumatoid arthritis progression by interacting with CCL5 and activating the ERK signaling.

Development of a Tagmentation-Based Next-Generation Sequencing Clinical Assay as an Alternative to Capillary Electrophoresis-Based Sequencing

BACKGROUND

Next-generation sequencing (NGS) technology enables sample multiplexing for interrogation of multiple regions of interest (ROI). Leveraging this, together with access to affordable NGS platforms, we explored the practicality of moving capillary electrophoresis (CE), noncapillary electrophoresis and single-gene testing to NGS. In this work, we evaluated the iSeq 100's capacity to validate 89 samples at once.

METHODS

Genomic DNA was extracted from 89 archival samples of varying specimen types. Polymerase chain reaction (PCR) was done with in house primers, library preparation with the Nextera XT Library Preparation Kit and cleaning up with paramagnetic beads. The sequencing was performed on one Illumina iSeq 100 flow cell.

RESULTS

With our workflow, 88 out of 89 samples were accurately sequenced with variant alleles identified. One sample of the 88 samples was initially discordant because the primers used were in a heterozygous deletion region. Upon redesigning of primers, the sample proved concordant.

CONCLUSIONS

The iSeq-Nextera workflow proved accurate. However, variant allele frequencys generated by the Nextera are not precise.

Biostatistical Aspects of Whole Genome Sequencing Studies: Preprocessing and Quality Control

Rapid advances in high-throughput DNA sequencing technologies have enabled large-scale whole genome sequencing (WGS) studies. Before performing association analysis between phenotypes and genotypes, preprocessing and quality control (QC) of the raw sequence data need to be performed. Because many biostatisticians have not been working with WGS data so far, we first sketch Illumina's short-read sequencing technology. Second, we explain the general preprocessing pipeline for WGS studies. Third, we provide an overview of important QC metrics, which are applied to WGS data: on the raw data, after mapping and alignment, after variant calling, and after multisample variant calling. Fourth, we illustrate the QC with the data from the GENEtic SequencIng Study Hamburg-Davos (GENESIS-HD), a study involving more than 9000 human whole genomes. All samples were sequenced on an Illumina NovaSeq 6000 with an average coverage of 35× using a PCR-free protocol. For QC, one genome in a bottle (GIAB) trio was sequenced in four replicates, and one GIAB sample was successfully sequenced 70 times in different runs. Fifth, we provide empirical data on the compression of raw data using the DRAGEN original read archive (ORA). The most important quality metrics in the application were genetic similarity, sample cross-contamination, deviations from the expected Het/Hom ratio, relatedness, and coverage. The compression ratio of the raw files using DRAGEN ORA was 5.6:1, and compression time was linear by genome coverage. In summary, the preprocessing, joint calling, and QC of large WGS studies are feasible within a reasonable time, and efficient QC procedures are readily available.

Unlocking the potential of Molecular Tumor Boards: from cutting-edge data interpretation to innovative clinical pathways

The emerging era of precision medicine is characterized by an increasing availability of targeted anticancer therapies and by the parallel development of techniques to obtain more refined molecular data, whose interpretation may not always be straightforward. Molecular tumor boards gather various professional figures, in order to leverage the analysis of molecular data and provide prognostic and predictive insights for clinicians. In addition to healthcare development, they could also become a tool to promote knowledge and research spreading. A growing body of evidence on the application of molecular tumor boards to clinical practice is forming and positive signals are emerging, although a certain degree of heterogeneity exists. This work analyzes molecular tumor boards' potential workflows, figures involved, data sources, sample matrices and eligible patients, as well as available evidence and learning examples. The emerging concept of multi-institutional, disease-specific molecular tumor boards is also considered by presenting two ongoing nationwide experiences.

Identification of Key Genes Associated with Tumor Microenvironment Infiltration and Survival in Gastric Adenocarcinoma via Bioinformatics Analysis

OBJECTIVE

Gastric carcinoma (GC) is the fifth most commonly diagnosed cancer and the third leading cause of cancer-related deaths globally. The tumor microenvironment plays a significant role in the pathogenesis, prognosis, and response to immunotherapy. However, the immune-related molecular mechanisms underlying GC remain elusive. Bioinformatics analysis of the gene expression of GC and paracancerous healthy tissues from the same patient was performed to identify the key genes and signaling pathways, as well as their correlation to the infiltration of the tumor microenvironment (TME) by various immune cells related to GC development.

METHODS

We employed GSE19826, a gene expression profile from the Gene Expression Omnibus (GEO), for our analysis. Functional enrichment analysis of Differentially Expressed Genes (DEGs) was conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes database.

RESULTS

Cytoscape software facilitated the identification of nine hub DEGs, namely, FN1, COL1A1, COL1A2, THBS2, COL3A1, COL5A1, APOE, SPP1, and BGN. Various network analysis algorithms were applied to determine their high connectivity. Among these hub genes, FN1, COL1A2, THBS2, COL3A1, COL5A1, and BGN were found to be associated with a poor prognosis for GC patients. Subsequent analysis using the TIMER database revealed the infiltration status of the TME concerning the overexpression of these six genes. Specifically, the abovementioned genes demonstrated direct correlations with cancer-associated fibroblasts, M1 and M2 macrophages, myeloid-derived suppressor cells, and activated dendritic cells.

CONCLUSION

Our findings suggest that the identified hub genes, particularly BGN, FN1, COL1A2, THBS2, COL3A1, and COL5A1, play crucial roles in GC prognosis and TME cell infiltration. This comprehensive analysis enhances our understanding of the molecular mechanisms underlying GC development and may contribute to the identification of potential therapeutic targets and prognostic markers for GC patients.

Nephroblastoma-specific dysregulated gene SNHG15 with prognostic significance: scRNA-Seq with bulk RNA-Seq data and experimental validation

Wilms tumor (WT) is the most common malignancy of the genitourinary system in children. Currently, the Integration of single-cell RNA sequencing (scRNA-Seq) and Bulk RNA sequencing (RNA-Seq) analysis of heterogeneity between different cell types in pediatric WT tissues could more accurately find prognostic markers, but this is lacking. RNA-Seq and clinical data related to WT were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Small nucleolar RNA host gene 15 (SNHG15) was identified as a risk signature from the TARGET dataset by using weighted gene co-expression network analysis, differentially expressed analysis and univariate Cox analysis. After that, the functional mechanisms, immunological and molecular characterization of SNHG15 were investigated at the scRNA-seq, pan-cancer, and RNA-seq levels using Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), ESTIMATE, and CIBERSORT. Based on scRNA-seq data, we identified 20 clusters in WT and annotated 10 cell types. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing M2 macrophages as hubs for intercellular communication. In addition, in vitro cellular experiments showed that siRNAs interfering with SNHG15 significantly inhibited the proliferation and migration of G401 cells and promoted the apoptosis of G401 cells compared with the control group. The effect of siRNAs interfering with SNHG15 on EMT-related protein expression was verified by Western blotting assay. Thus, our findings will improve our current understanding of the pathogenesis of WT, and they are potentially valuable in providing novel prognosis markers for the treatment of WT.

Analysis of Bulk Transcriptome Sequencing Data and in vitro Experiments Reveal SIN3A as a Potential Target for Diabetic Foot Ulcer

Background

Diabetic foot ulcers (DFUs) represent a severe complication of diabetes associated with reduced quality of life, lower limb amputations, hospitalizations, increased incidence, and mortality. Importantly, a significant number of pathogenic genes remain unexplored in DFUs.

Methods

A series of bioinformatics analyses were performed on publicly available bulk transcriptome sequencing datasets GSE134431 and GSE80178 to explore the transcriptomic changes in DFUs and select core genes for in vitro functional validation. In a focused examination, the differential expression analysis unveiled distinctions in gene expression patterns between DFUs and non-ulcerated diabetic skin tissues. Enriched functional annotations of differentially expressed genes were explored using the DAVID online tool. Protein-protein interaction analysis was conducted to investigate interactions among differentially expressed genes and select core genes. Knockdown or overexpression of core genes in HaCaT keratinocytes was performed to assess their impact on cell proliferation and migration.

Results

Ten core genes were identified. Cell Counting Kit-8 (CCK-8) and scratch assays demonstrated that downregulation of the core gene SIN3A significantly inhibited the migration and proliferation of HaCaT keratinocytes, while overexpression of SIN3A reversed the high-glucose-induced suppression of HaCaT cell viability and migration.

Conclusion

SIN3A expression is downregulated in DFUs. In vitro, SIN3A promotes the proliferation and migration of HaCaT keratinocytes, suggesting it may be a potential therapeutic target for DFUs.

S100 Calcium-Binding Protein A8 Functions as a Tumor-Promoting Factor in Renal Cell Carcinoma via Activating NF-κB Signaling Pathway

BACKGROUND

Renal cell carcinoma (RCC), arising from the renal tubular epithelium, is one of the most common types of genitourinary malignancies. Based on the Gene Expression Omnibus (GEO) database (GSE100666), S100 calcium-binding protein A8 (S100A8) was highly expressed in RCC tissues. S100A8, an inflammatory regulatory factor, has emerged as an important mediator associated with the occurrence and development of cancer.

MATERIALS AND METHODS

The Gene Expression Omnibus (GEO) database was used to identify the key genes and investigate the main signaling pathways in RCC. Human RCC samples and corresponding adjacent normal tissues were collected in our hospital. The expression of S100A8 in human RCC samples was detected using western blotting and immunohistochemical analysis. S100A8 overexpression or knockdown was mediated by using Lipofectamine 3000 in human renal cell carcinoma cell line 786-O and ACHN cells. Basic experiments, including MTT and cell apoptosis assays, were utilized for investigating the function of S100A8 in RCC. Furthermore, the levels of inflammation were also evaluated in 786-O and ACHN cells.

RESULTS

In the current study, we found that downregulation of S100A8 inhibited proliferation and promoted apoptosis in 786-O and ACHN RCC cells. Of note, S100A8 silencing downregulated the phosphorylation of NF-κB p65, thereby decreasing the levels of TNF-α, cleaved caspase1, and MMP9. By contrast, S100A8 upregulation could increase these expressions.

CONCLUSION

Overall, S100A8 knockdown restrained RCC malignant biological properties, which was associated with the deactivation of the NF-κB signaling pathway. This present study demonstrates new insights that S100A8 may be a potential therapeutic target in RCC.

Acute lung injury caused by sepsis: how does it happen?

Sepsis is a systemic inflammatory disease caused by severe infections that involves multiple systemic organs, among which the lung is the most susceptible, leaving patients highly vulnerable to acute lung injury (ALI). Refractory hypoxemia and respiratory distress are classic clinical symptoms of ALI caused by sepsis, which has a mortality rate of 40%. Despite the extensive research on the mechanisms of ALI caused by sepsis, the exact pathological process is not fully understood. This article reviews the research advances in the pathogenesis of ALI caused by sepsis by focusing on the treatment regimens adopted in clinical practice for the corresponding molecular mechanisms. This review can not only contribute to theories on the pathogenesis of ALI caused by sepsis, but also recommend new treatment strategies for related injuries.

Identification of novel potential homologous repair deficiency-associated genes in pancreatic adenocarcinoma via WGCNA coexpression network analysis and machine learning

Homologous repair deficiency (HRD) impedes double-strand break repair, which is a common driver of carcinogenesis. Positive HRD status can be used as theranostic markers of response to platinum- and PARP inhibitor-based chemotherapies. Here, we aimed to fully investigate the therapeutic and prognostic potential of HRD in pancreatic adenocarcinoma (PAAD) and identify effective biomarkers related to HRD using comprehensive bioinformatics analysis. The HRD score was defined as the unweighted sum of the LOH, TAI, and LST scores, and it was obtained based on the previous literature. To characterize PAAD immune infiltration subtypes, the "ConsensusClusterPlus" package in R was used to conduct unsupervised clustering. A WGCNA was conducted to elucidate the gene coexpression modules and hub genes in the HRD-related gene module of PAAD. The functional enrichment study was performed using Metascape. LASSO analysis was performed using the "glmnet" package in R, while the random forest algorithm was realized using the "randomForest" package in R. The prognostic variables were evaluated using univariate Cox analysis. The prognostic risk model was built using the LASSO approach. ROC curve and KM survival analyses were performed to assess the prognostic potential of the risk model. The half-maximal inhibitory concentration (IC50) of the PARP inhibitors was estimated using the "pRRophetic" package in R and the Genomics of Drug Sensitivity in Cancer database. The "rms" package in R was used to create the nomogram. A high HRD score indicated a poor prognosis and an advanced clinical process in PAAD patients. PAAD tumors with high HRD levels revealed significant T helper lymphocyte depletion, upregulated levels of cancer stem cells, and increased sensitivity to rucaparib, Olaparib, and veliparib. Using WGCNA, 11 coexpression modules were obtained. The red module and 122 hub genes were identified as the most correlated with HRD in PAAD. Functional enrichment analysis revealed that the 122 hub genes were mainly concentrated in cell cycle pathways. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were screened via LASSO analysis and a random forest algorithm, and they were validated using independent validation sets. No direct association between HRD and CKS1B, HJURP, or TPX2 has not been reported in the literature so far. Thus, these findings indicated that CKS1B, HJURP, and TPX2 have potential as diagnostic and prognostic biomarkers for PAAD. We constructed a novel HRD-related prognostic model that provides new insights into PAAD prognosis and immunotherapy. Based on bioinformatics analysis, we comprehensively explored the therapeutic and prognostic potential of HRD in PAAD. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were identified through the combination of WGCNA, LASSO analysis and a random forest algorithm. A novel HRD-related risk model that can predict clinical prognosis and immunotherapeutic response in PAAD patients was constructed.

Clinical and Genetic Features of Korean Inherited Arrhythmia Probands

BACKGROUND AND OBJECTIVES

Inherited arrhythmia (IA) is a more common cause of sudden cardiac death in Asian population, but little is known about the genetic background of Asian IA probands. We aimed to investigate the clinical characteristics and analyze the genetic underpinnings of IA in a Korean cohort.

METHODS

This study was conducted in a multicenter cohort of the Korean IA Registry from 2014 to 2017. Genetic testing was performed using a next-generation sequencing panel including 174 causative genes of cardiovascular disease.

RESULTS

Among the 265 IA probands, idiopathic ventricular fibrillation (IVF) and Brugada Syndrome (BrS) was the most prevalent diseases (96 and 95 cases respectively), followed by long QT syndrome (LQTS, n=54). Two-hundred-sixteen probands underwent genetic testing, and 69 probands (31.9%) were detected with genetic variant, with yield of pathogenic or likely pathogenic variant as 6.4%. Left ventricular ejection fraction was significantly lower in genotype positive probands (54.7±11.3 vs. 59.3±9.2%, p=0.005). IVF probands showed highest yield of positive genotype (54.0%), followed by LQTS (23.8%), and BrS (19.5%).

CONCLUSIONS

There were significant differences in clinical characteristics and genetic yields among BrS, LQTS, and IVF. Genetic testing did not provide better yield for BrS and LQTS. On the other hand, in IVF, genetic testing using multiple gene panel might enable the molecular diagnosis of concealed genotype, which may alter future clinical diagnosis and management strategies.

G6PD drives glioma invasion by regulating SQSTM1 protein stability

Glioma is an incurable brain tumor with high recurrence due to the frequent invasion of neoplastic cells. Glucose-6-phosphate dehydrogenase (G6PD) is a critical enzyme in the pentose phosphate pathway (PPP) whose aberrant expression drives the pathogenesis of various cancers. Recent research has identified other moonlight modes of enzymes besides the well-known regulation of metabolic reprogramming. Here, we identified previously unexplored roles of G6PD in glioma via gene set variation analysis (GSVA) based on the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) database. Furthermore, survival analyses revealed that glioma patients with high G6PD expression had a worse outcome than patients with low G6PD expression (Hazard Ratio (95%CI): 2.96 (2.41, 3.64), p = 3.5E-22). Combined with functional assays, G6PD was shown to be related with the migration and invasion in glioma. G6PD knockdown could inhibit the migration in LN229 cells. And G6PD overexpression enhanced LN229 cell migration and invasion. Mechanically, the knockdown of G6PD reduced sequestosome 1 (SQSTM1) protein stability under cycloheximide (CHX) treatment. Moreover, the overexpression of SQSTM1 rescued the impaired migrated and invasive phenotypes in G6PD-silenced cells. Clinically, we validated the role of G6PD-SQSTM1 axis in glioma prognosis by constructing the multivariate cox proportional hazards regression model. These results define a pivotal function of G6PD in modulating SQSTM1 to promote glioma aggressiveness. And G6PD may be a prognostic biomarker and potential therapeutic target in glioma. G6PD-SQSTM1 axis may be a potential prognostic biomarker in glioma.

Coronary artery disease-associated immune gene RBP1 and its pan-cancer analysis

Purpose

To identify immune-related biomarkers in coronary artery disease (CAD), investigate their possible function in the immunological milieu of tumors, and initially investigate the mechanisms and therapeutic targets shared by CAD and cancer.

Methods

Download the CAD-related dataset GSE60681 from the GEO database. GSVA and WGCNA analyses were performed based on the GSE60681 dataset to identify the modules most pertinent to CAD, identify candidate hub genes and finally intersect the genes associated with immunity downloaded from the import database to find the hub genes. The GTEx, CCLE, and TCGA database were used to examine the expression of the hub gene in normal tissues, tumor cell lines, tumor tissues, and different tumor STAGES. One-factor cox and Kaplan-Meier analyses were performed to explore the prognosis of hub genes. Hub gene methylation levels in CAD and cancer were analyzed in the diseaseMeth 3.0 and ualcan databases, respectively. R package CiberSort processed the GSE60681 dataset to assess immune infiltration in CAD. TIMER2.0 evaluated hub genes with pan-cancer immune infiltration. The hub genes were analyzed for drug sensitivity and correlation with TMB, MSI, MMR, cancer-related functional status, and immune checkpoints in different tumors. Finally, GSEA was carried out on the crucial genes.

Results

WGCNA were used to pinpoint the green modules that were most closely related to CAD and intersections with immune-related genes were taken to remember the pivotal gene RBP1. RBP1 is hypermethylated in CAD and multiple cancers. Its expression levels in different cancers were associated with poor prognosis of cancer, with significant expression levels at higher stages of cancer staging. The immune infiltration results showed that RBP1 was closely associated with CAD and tumor-associated immune infiltration. The results indicated that RBP1 was strongly correlated with TMB, MSI, MMR, cancer-associated functional status, and immune checkpoints in various cancers. RBP1 was related to the sensitivity of six anticancer drugs. GSEA showed RBP1 was associated with immune cell activation, immune response, and cancer development.

Conclusion

RBP1 is a pivotal gene associated with immunity in CAD and pan-cancer and may mediate the development of CAD and cancer through immunity, making it a common therapeutic target for both.

CCNB2 as a potential biomarker of bladder cancer via the high throughput technology

Bladder cancer and oral squamous cell carcinoma (OSCC) seriously affect people's health. However, the relationship between bladder cancer and OSCC remains unclear. Got GSE138206, GSE146483, GSE184616, and bladder cancer datasets GSE65635, GSE100926 from Gene Expression Omnibus database. Weighted gene co-expression network analysis was used to identify the significant module. Functional enrichment analysis was performed via the Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes. Furthermore, the Gene Set Enrichment Analysis was also used to complete the enrichment analysis. Comparative Toxicogenomics Database found most relevant diseases to core genes. TargetScan is used to forecast analysis of microRNA and target genes. In Gene Ontology analysis, differentially expressed genes were mostly concentrated in cell differentiation, extrallular region, structural molecule activity, and actin binding. In Kyoto Encyclopedia of Genes and Genomes analysis, the differentially expressed genes were mainly enriched in PI3K-Akt signaling pathway, pathway in cancer, and extracellular matrix-receptor interaction. Seven hub genes (cyclin B2 [CCNB2], TK1, CDC20, PCNA, CKS1B, CDCA5, MCM4) were obtained. Hub genes (CCNB2, CDC20) are highly expressed in OSCC and bladder cancer samples. CCNB2 was one common oncogene of bladder cancer and OSCC.

Identification of key genes related to immune infiltration in cirrhosis via bioinformatics analysis

Cirrhosis is the most common subclass of liver disease worldwide and correlated to immune infiltration. However, the immune-related molecular mechanism underlying cirrhosis remains obscure. Two gene expression profiles GSE89377 and GSE139602 were investigated to identify differentially expressed genes (DEGs) related to cirrhosis. Enrichment analysis for DEGs was conducted. Next, the immune infiltration of DEGs was evaluated using CIBERSORT algorithm. The hub DEGs with tight connectivity were identified using the String and Cytoscape databases, and the expression difference of these hub genes between normal liver and cirrhosis samples was determined. Moreover, in order to evaluate the discriminatory ability of hub genes and obtained the area under the receiver operating characteristic curve values in the GSE89377 and GSE139602 datasets. Finally, the association between hub DEGs and immune cell infiltration was explored by Spearman method. Among the 299 DEGs attained, 136 were up-regulated and 163 were down-regulated. Then the enrichment function analysis of DEGs and CIBERSORT algorithm showed significant enrichment in immune and inflammatory responses. And four hub DEGs (ACTB, TAGLN, VIM, SOX9) were identified, which also showed a diagnostic value in the GSE89377 and GSE 139,602 datasets. Finally, the immune infiltration analysis indicated that, these hub DEGs were highly related to immune cells. This study revealed key DEGs involved in inflammatory immune responses of cirrhosis, which could be used as biomarkers for diagnosis or therapeutic targets of cirrhosis.

Next-Generation Sequencing Markup Language (NGSML): A Medium for the Representation and Exchange of NGS Data

With the increasing demand for low-cost high-throughput sequencing of large genomes, next-generation sequencing (NGS) technology has developed rapidly. NGS can not only be used in basic scientific research but also in clinical diagnostics and healthcare. Numerous software systems and tools have been developed to analyze NGS data, and various data formats have been produced to accommodate different sequencing equipment providers or analytical software. However, the data interoperability between these tools brings great challenges to researchers. A generic format that could be shared by most of the software and tools in the NGS field would make data interoperability and sharing easier. In this paper, we defined a general XML-based NGS markup language (NGSML) format for the representation and exchange of NGS data. We also developed a user-friendly GUI tool, NGSMLEditor, for presenting, creating, editing, and converting NGSML files. By using NGSML, various types of NGS data can be saved in one unified format. Compared with the unstructured plain text file, a structured data format based on XML technology solves the incompatibility of various NGS data formats. The NGSML specifications are freely available from http://www.sysbio.org.cn/NGSML. NGSMLEditor is open source under GNU GPL and can be downloaded from the website.

Identification of Potential Therapeutic Target Genes in Osteoarthritis

Objective

Osteoarthritis (OA), also known as joint failure, is characterized by joint pain and, in severe cases, can lead to loss of joint function in patients. Immune-related genes and immune cell infiltration play a crucial role in OA development. We used bioinformatics approaches to detect potential diagnostic markers and available drugs for OA while initially exploring the immune mechanisms of OA.

Methods

The training set GSE55235 and validation set GSE51588 and GSE55457 were obtained from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified by the limma package. Gene set enrichment analysis (GSEA) was performed on the GSE55235 dataset using the cluster profiler package. At the same time, DEGs were analyzed by gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). In addition, protein-protein interaction (PPI) analysis was performed on the common DEGs of the three datasets using the STRING database. Proteins with direct linkage were identified as hub genes, and the relation of hub genes was subsequently analyzed using the GOSemSim package. Hub genes' expression profiles and diagnostic capabilities (ROC curves) were analyzed and validated using three datasets. In addition, we performed RT-qPCR to validate the levels of hub genes. The immune microenvironment was analyzed using the CIBERSORT package, and the relationship between hub genes and immune cells was evaluated. In addition, we used a linkage map (CMAP) database to identify available drug candidates. Finally, the GSEA of hub genes was used to decipher the potential pathways corresponding to hub genes.

Results

Three hub genes (CX3CR1, MYC, and TLR7) were identified. CX3CR1 and TLR7 were highly expressed in patients with OA, whereas the expression of MYC was low. The results of RT-qPCR validation were consistent with those obtained using datasets. Among these genes, CX3CR1 and TLR7 can be used as diagnostic markers. It was found that CX3CR1, MYC, and TLR7 affect the immune microenvironment of OA via different immune cells. In addition, we identified a potential drug for the treatment of OA. Altogether, CX3CR1, MYC, and TLR7 affect the immune response of OA through multiple pathways.

Conclusion

CX3CR1, MYC, and TLR7 are associated with various immune cells and are the potential diagnostic markers and therapeutic targets for OA.

Multiple Myeloma: Bioinformatic Analysis for Identification of Key Genes and Pathways

Multiple myeloma (MM) is a hematological malignancy in which monoclonal plasma cells multiply in the bone marrow and monoclonal immunoglobulins are overproduced in older people. Several molecular and cytogenetic advances allow scientists to identify several genetic and chromosomal abnormalities that cause the disease. The comprehension of the pathophysiology of MM requires an understanding of the characteristics of malignant clones and the changes in the bone marrow microenvironment. This study aims to identify the central genes and to determine the key signaling pathways in MM by in silico approaches. A list of 114 differentially expressed genes (DEGs) is important in the prognosis of MM. The DEGs are collected from scientific publications and databases (https://www.ncbi.nlm.nih.gov/). These data are analyzed by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) software (https://string-db.org/) through the construction of protein-protein interaction (PPI) networks and enrichment analysis of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, by CytoHubba, AutoAnnotate, Bingo Apps plugins in Cytoscape software (https://cytoscape.org/) and by DAVID database (https://david.ncifcrf.gov/). The analysis of the results shows that there are 7 core genes, including TP53; MYC; CDND1; IL6; UBA52; EZH2, and MDM2. These top genes appear to play a role in the promotion and progression of MM. According to functional enrichment analysis, these genes are mainly involved in the following signaling pathways: Epstein-Barr virus infection, microRNA pathway, PI3K-Akt signaling pathway, and p53 signaling pathway. Several crucial genes, including TP53, MYC, CDND1, IL6, UBA52, EZH2, and MDM2, are significantly correlated with MM, which may exert their role in the onset and evolution of MM.

Potential of Mitochondrial Ribosomal Genes as Cancer Biomarkers Demonstrated by Bioinformatics Results

Next-generation sequencing and bioinformatics analyses have clearly revealed the roles of mitochondrial ribosomal genes in cancer development. Mitochondrial ribosomes are composed of three RNA components encoded by mitochondrial DNA and 82 specific protein components encoded by nuclear DNA. They synthesize mitochondrial inner membrane oxidative phosphorylation (OXPHOS)-related proteins and participate in various biological activities via the regulation of energy metabolism and apoptosis. Mitochondrial ribosomal genes are strongly associated with clinical features such as prognosis and foci metastasis in patients with cancer. Accordingly, mitochondrial ribosomes have become an important focus of cancer research. We review recent advances in bioinformatics research that have explored the link between mitochondrial ribosomes and cancer, with a focus on the potential of mitochondrial ribosomal genes as biomarkers in cancer.

Identification of a pyroptosis-related prognostic signature in breast cancer

Background

The relationship between pyroptosis and cancer is complex. It is controversial that whether pyroptosis represses or promotes tumor development. This study aimed to explore prognostic molecular characteristics to predict the prognosis of breast cancer (BRCA) based on a comprehensive analysis of pyroptosis-related gene expression data.

Methods

RNA-sequcing data of BRCA were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Ominibus (GEO) datasets. First, pyroptosis-related differentially expressed genes (DEGs) between normal and tumor tissues were identified from the TCGA database. Based on the DEGs, 1053 BRCA patients were divided into two clusters. Second, DEGs between the two clusters were used to construct a signature by a least absolute shrinkage and selection operator (LASSO) Cox regression model, and the GEO cohort was used to validate the signature. Various statistical methods were applied to assess this gene signature. Finally, Single-sample gene set enrichment analysis (ssGSEA) was employed to compare the enrichment scores of 16 types of immune cells and 13 immune-related pathways between the low- and high-risk groups. We calculated the tumor mutational burden (TMB) of TCGA cohort and evaluated the correlations between the TMB and riskscores of the TCGA cohort. We also compared the TMB between the low- and high-risk groups.

Results

A total of 39 pyroptosis-related DEGs were identified from the TCGA-breast cancer dataset. A prognostic signature comprising 16 genes in the two clusters of DEGs was developed to divide patients into high-risk and low-risk groups, and its prognostic performance was excellent in two independent patient cohorts. The high-risk group generally had lower levels of immune cell infiltration and lower activity of immune pathway activity than did the low-risk group, and different risk groups revealed different proportions of immune subtypes. The TMB is higher in high-risk group compared with low-risk group. OS of low-TMB group is better than that of high-TMB group.

Conclusion

A 16-gene signature comprising pyroptosis-related genes was constructed to assess the prognosis of breast cancer patients and its prognostic performance was excellent in two independent patient cohorts. The signature was found closely associated with the tumor immune microenvironment and the potential correlation could provide some clues for further studies. The signature was also correlated with TMB and the mechanisms are still warranted.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09526-z.

Identification of Significant Modules and Targets of Xian-Lian-Jie-Du Decoction Based on the Analysis of Transcriptomics, Proteomics and Single-Cell Transcriptomics in Colorectal Tumor

Purpose

Colorectal cancer (CRC) remains the third most common tumor worldwide. Ulcerative colitis (UC) could cause chronic inflammation and ulcers in the colon and rectum. UC is a risk factor for a high incidence of CRC, and the incidence of UC-associated CRC (UC-CRC) is still increasing. Chinese medicine prescription, Xian-Lian-Jie-Du decoction (XLJDD), has been proven its efficacy in some UC-CRC patients. However, the mechanism of XLJDD in treating UC-CRC remains unknown. This study aimed to investigate the mechanism of XLJDD in treating UC-CRC.

Methods

We constructed an AOM/DSS mouse model that could simulate the various stages of UC-CRC in humans. XLJDD and its 5 main components are used to treat the AOM/DSS model, respectively. With the power of high-throughput sequencing technology, we described the mechanism of XLJDD from transcriptomics, proteomics, and single-cell transcriptomics.

Results

Our results showed that XLJDD could effectively suppress the occurrence and development of colorectal tumors. Using the weighted correlation network analysis (WGCNA), several mRNA and protein modules that respond to XLJDD have been identified. Moreover, two essential genes, Mfsd2a and Ccdc85c, were caught our attention. They were prognostic markers in CRC patients, and their expression could be significantly modulated by XLJDD, showing their potential as effective targets of XLJDD. In addition, we also discovered that XLJDD could affect the cell composition of the colorectal tumor environment, especially in the infiltration of B cells.

Conclusion

We demonstrated that XLJDD could prevent the initiation and development of colorectal tumors by modulating the expression of Mfsd2a and Ccdc85c and reducing the infiltration of B cells in the tumor microenvironment of colorectal tumor.

Analysis of Novel Variants Associated with Three Human Ovarian Cancer Cell Lines

Background:

Identification of mutations is of great significance in cancer research, as it can contribute to development of therapeutic strategies and prevention of cancer formation. Ovarian cancer is one of the leading cancer-related causes of death in Taiwan. Accumulation of genetic mutations can lead to cancer.

Objective:

We utilized whole-exome sequencing to explore cancer-associated missense variants in three human ovarian cancer cell lines derived from Taiwanese patients.

Methods:

We use (i) cell line whole-exome sequencing data, (ii) 188 patients’ whole-exome sequencing data, and (iii) use of in vitro experiments to verify predicted variant results. We establish an effective analysis workflow for discovery of novel ovarian cancer variants, comprising three steps: (i) use of public databases and in-house hospital data to select novel variants (ii) investigation of protein structural stability caused by genetic mutations, and (iii) use of in vitro experiments to verify predictions.

Results:

Our study enumerated 296 novel variants by imposing specific criteria and using sophisticated bioinformatics tools for further analysis. Eleven and 54 missense novel variants associated with cancerous and non-cancerous genes, respectively, were identified. We show that 13 missense mutations affect the stability of protein 3D structure, while 11 disease-causing novel variants were confirmed by PCR sequencing. Among these, ten variants were predicted to be pathogenic, while the pathogenicity of one was uncertain.

Conclusion:

We confirm that novel variant genes play a crucial role in ovarian cancer patients, with 11 novel variants that may promote progression and development of ovarian cancer.

Rebalance of the Polyamine Metabolism Suppresses Oxidative Stress and Delays Senescence in Nucleus Pulposus Cells

Intervertebral disk degeneration (IDD) is a major cause of low back pain that becomes a prevalent age-related disease. However, the pathophysiological processes behind IDD are rarely known. Here, we used bioinformatics analysis based on the microarray datasets (GSE34095) to identify the differentially expressed genes (DEGs) as biomarkers and therapeutic targets in degenerated discs. From the previous studies, oxidative stress has been notified as a positive inducement of IDD, which causes DNA damage and accelerates cell senescence. Polyamine oxidase (PAOX), a member of the observed 1057 DEGs, is involved in polyamine metabolism and influences the oxidative balance in cells. However, it is uncertain if the IDD is implicated in the dysregulation of PAOX and polyamine metabolism. This study firstly verified the PAOX upregulation in human degenerated disc samples and applied an IL-1β-induced nucleus pulposus (NP) cell degeneration model to demonstrate that spermidine supplementation balanced polyamine metabolism and delayed NP cell senescence. Moreover, we confirmed that spermidine/N-acetylcysteine supplementation or Cdkn2a gene deletion stabilized the polyamine metabolism, suppressed oxidative stress, and therefore delayed the progress of IDD in older mice. Collectively, our study highlights the role of polyamine metabolism in IDD and foresees spermidine would be the advanced therapeutical drug for IDD.

Galaxy Is a Suitable Bioinformatics Platform for the Molecular Diagnosis of Human Genetic Disorders Using High-Throughput Sequencing Data Analysis. Five Years of Experience in a Clinical Laboratory

BACKGROUND

To date, the usage of Galaxy, an open-source bioinformatics platform, has been reported primarily in research. We report 5 years' experience (2015 to 2020) with Galaxy in our hospital, as part of the "Assistance Publique-Hôpitaux de Paris" (AP-HP), to demonstrate its suitability for high-throughput sequencing (HTS) data analysis in a clinical laboratory setting.

METHODS

Our Galaxy instance has been running since July 2015 and is used daily to study inherited diseases, cancer, and microbiology. For the molecular diagnosis of hereditary diseases, 6970 patients were analyzed with Galaxy (corresponding to a total of 7029 analyses).

RESULTS

Using Galaxy, the time to process a batch of 23 samples-equivalent to a targeted DNA sequencing MiSeq run-from raw data to an annotated variant call file was generally less than 2 h for panels between 1 and 500 kb. Over 5 years, we only restarted the server twice for hardware maintenance and did not experience any significant troubles, demonstrating the robustness of our Galaxy installation in conjunction with HTCondor as a job scheduler and a PostgreSQL database. The quality of our targeted exome sequencing method was externally evaluated annually by the European Molecular Genetics Quality Network (EMQN). Sensitivity was mean (SD)% 99 (2)% for single nucleotide variants and 93 (9)% for small insertion-deletions.

CONCLUSION

Our experience with Galaxy demonstrates it to be a suitable platform for HTS data analysis with vast potential to benefit patient care in a clinical laboratory setting.

The Interpretation of Sequence Variants in Myeloid Neoplasms

OBJECTIVES

To provide an overview of the challenges encountered during the interpretation of sequence variants detected by next-generation sequencing (NGS) in myeloid neoplasms, as well as the limitations of the technology with the goal of preventing the over- or undercalling of alterations that may have a significant effect on patient management.

METHODS

Review of the peer-reviewed literature on the interpretation, reporting, and technical challenges of NGS assays for myeloid neoplasms.

RESULTS

NGS has been integrated widely and rapidly into the standard evaluating of myeloid neoplasms. Review of the literature reveals that myeloid sequence variants are challenging to detect and interpret. Large insertions and guanine-cytosine-heavy areas prove technically challenging while frameshift and truncating alterations may be classified as variants of uncertain significance by tertiary analysis informatics pipelines due to their absence in the literature and databases.

CONCLUSIONS

The analysis and interpretation of NGS results in myeloid neoplasia are challenging due to the varied number of detectable gene alterations. Familiarity with the genomic landscape of myeloid malignancies and knowledge of the tools available for the interpretation of sequence variants are essential to facilitate translation into clinical and therapy decisions.

Modelling the bioinformatics tertiary analysis research process

Background

With the advancements of Next Generation Techniques, a tremendous amount of genomic information has been made available to be analyzed by means of computational methods. Bioinformatics Tertiary Analysis is a complex multidisciplinary process that represents the final step of the whole bioinformatics analysis pipeline. Despite the popularity of the subject, the Bioinformatics Tertiary Analysis process has not yet been specified in a systematic way. The lack of a reference model results into a plethora of technological tools that are designed mostly on the data and not on the human process involved in Tertiary Analysis, making such systems difficult to use and to integrate.

Methods

To address this problem, we propose a conceptual model that captures the salient characteristics of the research methods and human tasks involved in Bioinformatics Tertiary Analysis. The model is grounded on a user study that involved bioinformatics specialists for the elicitation of a hierarchical task tree representing the Tertiary Analysis process. The outcome was refined and validated using the results of a vast survey of the literature reporting examples of Bioinformatics Tertiary Analysis activities.

Results

The final hierarchical task tree was then converted into an ontological representation using an ontology standard formalism. The results of our research provides a reference process model for Tertiary Analysis that can be used both to analyze and to compare existing tools, or to design new tools.

Conclusions

To highlight the potential of our approach and to exemplify its concrete applications, we describe a new bioinformatics tool and how the proposed process model informed its design.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04310-5.

The upregulated expression of RFC4 and GMPS mediated by DNA copy number alteration is associated with the early diagnosis and immune escape of ESCC based on a bioinformatic analysis

Esophageal squamous cell carcinoma (ESCC) is a malignant tumor that commonly occurs worldwide. Usually, Asia, especially China, has a high incidence of esophageal cancer. ESCC often has a poor outcome because of a late diagnosis and lack of effective treatments.

To build foundations for the early diagnosis and treatment of ESCC, we used the gene expression datasets GSE20347 and GSE17351 from the GEO database and a private dataset to uncover differentially expressed genes (DEGs) and key genes in ESCC. Notably, we found that replication factor C subunit 4 (RFC4) and guanine monophosphate synthase (GMPS) were upregulated but have been rarely studied in ESCC. In particular, to the best of our knowledge, our study is the first to explore GMPS and ESCC. Furthermore, we found that high levels of RFC4 and GMPS expression may result from an increase in DNA copy number alterations. Furthermore, RFC4 and GMPS were both upregulated in the early stage and early nodal metastases of esophageal carcinoma. The expression of RFC4 was strongly correlated with GMPS. In addition, we explored the relationship between RFC4 and GMPS expression and tumor-infiltrating immune cells (TILs) in esophageal carcinoma. The results showed that the levels of RFC4 and GMPS increased with a decrease in some tumor-infiltrating cells. Upregulated RFC4 and GMPS with high TILs indicate a worse prognosis.

In summary, our study shows that RFC4 and GMPS have potential as biomarkers for the early diagnosis of ESCC and may played a crucial role in the process of tumor immunity in ESCC.

Identification of Biomarkers for Cervical Cancer Radiotherapy Resistance Based on RNA Sequencing Data

Cervical cancer as a common gynecological malignancy threatens the health and lives of women. Resistance to radiotherapy is the primary cause of treatment failure and is mainly related to difference in the inherent vulnerability of tumors after radiotherapy. Here, we investigated signature genes associated with poor response to radiotherapy by analyzing an independent cervical cancer dataset from the Gene Expression Omnibus, including pre-irradiation and mid-irradiation information. A total of 316 differentially expressed genes were significantly identified. The correlations between these genes were investigated through the Pearson correlation analysis. Subsequently, random forest model was used in determining cancer-related genes, and all genes were ranked by random forest scoring. The top 30 candidate genes were selected for uncovering their biological functions. Functional enrichment analysis revealed that the biological functions chiefly enriched in tumor immune responses, such as cellular defense response, negative regulation of immune system process, T cell activation, neutrophil activation involved in immune response, regulation of antigen processing and presentation, and peptidyl-tyrosine autophosphorylation. Finally, the top 30 genes were screened and analyzed through literature verification. After validation, 10 genes (KLRK1, LCK, KIF20A, CD247, FASLG, CD163, ZAP70, CD8B, ZNF683, and F10) were to our objective. Overall, the present research confirmed that integrated bioinformatics methods can contribute to the understanding of the molecular mechanisms and potential therapeutic targets underlying radiotherapy resistance in cervical cancer.

Ten-gene signature reveals the significance of clinical prognosis and immuno-correlation of osteosarcoma and study on novel skeleton inhibitors regarding MMP9

OBJECTIVES

This study aimed to identify novel targets in the carcinogenesis, therapy and prognosis of osteosarcoma from genomic level, together with screening ideal lead compounds with potential inhibition regarding MMP-9.

METHODS

Gene expression profiles from GSE12865, GSE14359, GSE33382, GSE36001 and GSE99671 were obtained respectively from GEO database. Differentially expressed genes were identified, and functional enrichment analysis, such as GO, KEGG, GSEA, PPI were performed to make a comprehensive understanding of the hub genes. Next, a series of high-precision computational techniques were conducted to screen potential lead compounds targeting MMP9, including virtual screening, ADME, toxicity prediction, and accurate docking analysis.

RESULTS

10 genes, MMP9, CD74, SPP1, CXCL12, TYROBP, FCER1G, HCLS1, ARHGDIB, LAPTM5 and IGF1R were identified as hub genes in the initiation of osteosarcoma. Machine learning, multivariate Cox analysis, ssGSEA and survival analysis demonstrated that these genes had values in prognosis, immune-correlation and targeted treatment. Tow novel compounds, ZINC000072131515 and ZINC000004228235, were screened as potential inhibitor regarding MMP9, and they could bind to MMP9 with favorable interaction energy and high binding affinity. Meanwhile, they were precited to be efficient and safe drugs with low-ames mutagenicity, none weight evidence of carcinogenicity, as well as non-toxic with liver.

CONCLUSIONS

This study revealed the significance of 10-gene signature in the development of osteosarcoma. Besides, drug candidates identified in this study provided a solid basis on MMP9 inhibitors' development.

Screening of Candidate Pathogenic Genes for Spontaneous Abortion using Whole Exome Sequencing

BACKGROUND

Spontaneous abortion is a common disease in obstetrics and reproduction.

OBJECTIVE

This study aimed to screen candidate pathogenic genes for spontaneous abortion using whole-exome sequencing.

METHODS

Genomic DNA was extracted from abortion tissues of spontaneous abortion patients and sequenced using the Illumina HiSeq2500 high-throughput sequencing platform. Whole exome sequencing was performed to select harmful mutations, including SNP and insertion and deletion sites, associated with spontaneous abortion. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and gene fusion analyses were performed. MUC3A and PDE4DIP were two novel mutation genes that were screened and verified by PCR in abortion tissues of patients.

RESULTS

A total of 83,633 SNPs and 13,635 Indel mutations were detected, of which 29172 SNPs and 3093 Indels were screened as harmful mutations. The 7 GO-BP, 4 GO-CC, 9 GO-MF progress, and 3 KEGG pathways were enriched in GO and KEGG pathway analyses. A total of 746 gene fusion mutations were obtained, involving 492 genes. MUC3A and PDE4DIP were used for PCR verification because of their high number of mutation sites in all samples.

CONCLUSION

There are extensive SNPs and Indel mutations in the genome of spontaneous abortion tissues, and the effect of these gene mutations on spontaneous abortion needs further experimental verification.

LncRNA HAS2-AS1 Promotes Glioblastoma Proliferation by Sponging miR-137

GBM (Glioblastoma multiform) is the most malignant tumor type of the central nervous system and has poor diagnostic and clinical outcomes. LncRNAs (Long non-coding RNAs) have been reported to participate in multiple biological and pathological processes, but their underlying mechanism remains poorly understood. Here, we aimed to explore the role of the lncRNA HAS2-AS1 (HAS2 antisense RNA 1) in GBM. GSE103227 was analyzed, and qRT-PCR was performed to measure the expression of HAS2-AS1 in GBM. FISH (Fluorescence in situ hybridization) was performed to verify the localization of HAS2-AS1. The interaction between HAS2-AS1 and miR-137 (microRNA-137) was predicted by LncBook and miRcode followed by dual-luciferase reporter assays, and the relationships among HAS2-AS1, miR-137 and LSD1 (lysine-specific demethylase 1) were assessed by WB (western blot) and qRT-PCR. Colony formation and CCK-8 (cell counting kit-8) assays were performed as functional tests. In vivo, nude mice were used to confirm the function of HAS2-AS1. HAS2-AS1 expression was upregulated in GBM cell lines, and HAS2-AS1 was localized mainly in the cytoplasm. In vitro, high HAS2-AS1 expression promoted proliferation, and knockdown of HAS2-AS1 significantly inhibited proliferation. Furthermore, HAS2-AS1 functioned as a ceRNA (competing endogenous RNA) of miR-137, leading to the disinhibition of its downstream target LSD1. The miR-137 level was downregulated by HAS2-AS1 overexpression and upregulated by HAS2-AS1 knockdown. In a subsequent study, LSD1 expression was negatively regulated by miR-137, while miR-137 reversed the LSD1 expression levels caused by HAS2-AS1. These results were further supported by the nude mouse tumorigenesis experiment; compared with xenografts with high HAS2-AS1 expression, the group with low levels of HAS2-AS1 exhibited suppressed proliferation and better survival. We conclude that lncRNA HAS2-AS1 promotes proliferation by functioning as a miR-137 decoy to increase LSD1 levels and thus might be a possible biomarker for GBM.

Identification of Disease-Specific Hub Biomarkers and Immune Infiltration in Osteoarthritis and Rheumatoid Arthritis Synovial Tissues by Bioinformatics Analysis

Background

Osteoarthritis (OA) and rheumatoid arthritis (RA) are well-known cause of joint disability. Although they have shown the analogous clinical features involving chronic synovitis that progresses to cartilage and bone destruction, the pathogenesis that initiates and perpetuates synovial lesions between RA and OA remains elusive.

Objective

This study is aimed at identifying disease-specific hub genes, exploring immune cell infiltration, and elucidating the underlying mechanisms associated with RA and OA synovial lesion.

Methods

Gene expression profiles (GSE55235, GSE55457, GSE55584, and GSE12021) were selected from Gene Expression Omnibus for analysis. Differentially expressed genes (DEGs) were identified by the “LIMMA” package in Bioconductor. The DEGs were identified by Gene Ontology (GO) and KEGG pathway analysis. A protein-protein interaction network was constructed to identify candidate hub genes by using STRING and Cytoscape. Hub genes were identified by validating from GSE12021. Furthermore, we employed the CIBERSORT website to assess immune cell infiltration between OA and RA. Finally, we explored the correlation between the levels of hub genes and relative proportion of immune cells in OA and RA.

Results

We identified 68 DEGs which were mainly enriched in immune response and chemokine signaling pathway. Six hub genes with a cutoff of AUC > 0.80 by ROC analysis and relative expression of P < 0.05 were identified successfully. Compared with OA, the RA synovial tissues consisted of a higher proportion of 7 immune cells, whereas 4 immune cells were found in relatively lower proportion (P < 0.05). In addition, the levels of 6 hub genes were closely associated with relative proportion of 11 immune cells in OA and RA.

Conclusions

We used bioinformatics analysis to identify hub genes and explored immune cell infiltration of immune microenvironment in synovial tissues. Our results should offer insights into the underlying molecular mechanisms of synovial lesion and provide potential target for immune-based therapies of OA and RA.

Relationship between hepatocellular carcinoma and depression via online database analysis

There may be a mutually reinforcing relationship between hepatocellular carcinoma (HCC) and depression, but the mechanism is unknown. This study used bioinformatics to evaluate the relationship between HCC and depression at the genetic level. Genes associated with HCC and depression were obtained from pubmed2ensemble. Overlapping genes were annotated by gene ontology (GO) function and enriched by Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway. The cluster-1 genes obtained by Cytoscape were analyzed by GEPIA for expression and overall survival in HCC and, finally, introduced target genes to DGIdb to get associated drugs. A total of 199 genes were found to be in common between HCC and depression. GO term enrichment analysis on DAVID found the top-6 biological processes to be mainly associated with cell death and apoptosis. The top-6 cellular component terms are extracellular. The top-6 of molecular function terms are mainly associated with receptor binding. The top-6 pathways enriched by KEGG are mainly related to inflammatory response. IGF1, VEGFA, and SERPINE1 had statistical differences in expression and 10-year survival rate. There are total 45 drugs that act on VEGFA and SERPINE1. Based on our findings, we hypothesize that the mechanism of the interaction between HCC and depression may be related to cell death or apoptosis. Further studies are needed to verify this hypothesis.

Construction of liver hepatocellular carcinoma-specific lncRNA-miRNA-mRNA network based on bioinformatics analysis

Liver hepatocellular carcinoma (LIHC) is one of the major causes of cancer-related death worldwide with increasing incidences, however there are very few studies about the underlying mechanisms and pathways in the development of LIHC. We obtained LIHC samples from The Cancer Genome Atlas (TCGA) to screen differentially expressed mRNAs, lncRNAs, miRNAs and driver mutations. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses and protein–protein interaction (PPI) network were performed. Moreover, we constructed a competing endogenous lncRNAs-miRNAs-mRNAs network. Finally, cox proportional hazards regression analysis was used to identify important prognostic differentially expressed genes. Total of 1284 mRNAs, 123 lncRNAs, 47 miRNAs were identified within different tissues of LIHC patients. GO analysis indicated that upregulated and downregulated differentially expressed mRNAs (DEmRNAs) were mainly associated with cell division, DNA replication, mitotic sister chromatid segregation and complement activation respectively. Meanwhile, KEGG terms revealed that upregulated and downregulated DEmRNAs were primarily involved in DNA replication, Metabolic pathways, cell cycle and Metabolic pathways, chemical carcinogenesis, retinol metabolism pathway respectively. Among the DERNAs, 542 lncRNAs-miRNAs-mRNAs pairs were predicted to construct a ceRNA regulatory network including 35 DElncRNAs, 26 DEmiRNAs and 112 DEmRNAs. In the Kaplan‐Meier analysis, total of 43 mRNAs, 14 lncRNAs and 3 miRNAs were screened out to be significantly correlated with overall survival of LIHC. The mutation signatures were analyzed and its correlation with immune infiltrates were evaluated using the TIMER in LIHC. Among the mutation genes, TTN mutation is often associated with poor immune infiltration and a worse prognosis in LIHC. This work conducted a novel lncRNAs-miRNAs-mRNAs network and mutation signatures for finding potential molecular mechanisms underlying the development of LIHC. The biomarkers also can be used for predicting prognosis of LIHC.

Germline Structural Variations in Cancer Predisposition Genes

In addition to single nucleotide variations and small-scale indels, structural variations (SVs) also contribute to the genetic diversity of the genome. SVs, such as deletions, duplications, amplifications, or inversions may also affect coding regions of cancer-predisposing genes. These rearrangements may abrogate the open reading frame of these genes or adversely affect their expression and may thus act as germline mutations in hereditary cancer syndromes. With the capacity of disrupting the function of tumor suppressors, structural variations confer an increased risk of cancer and account for a remarkable fraction of heritability. The development of sequencing techniques enables the discovery of a constantly growing number of SVs of various types in cancer predisposition genes (CPGs). Here, we provide a comprehensive review of the landscape of germline SV types, detection methods, pathomechanisms, and frequency in CPGs, focusing on the two most common cancer syndromes: hereditary breast- and ovarian cancer and gastrointestinal cancers. Current knowledge about the possible molecular mechanisms driving to SVs is also summarized.

Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The efficacy of immunotherapy usually depends on the interaction of immunomodulation in the tumor microenvironment (TME). This study aimed to explore the potential stromal-immune score-based prognostic genes related to immunotherapy in HCC through bioinformatics analysis.

Methods: ESTIMATE algorithm was applied to calculate the immune/stromal/Estimate scores and tumor purity of HCC using the Cancer Genome Atlas (TCGA) transcriptome data. Functional enrichment analysis of differentially expressed genes (DEGs) was analyzed by the Database for Annotation, Visualization, and Integrated Discovery database (DAVID). Univariate and multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed for prognostic gene screening. The expression and prognostic value of these genes were further verified by KM-plotter database and the Human Protein Atlas (HPA) database. The correlation of the selected genes and the immune cell infiltration were analyzed by single sample gene set enrichment analysis (ssGSEA) algorithm and Tumor Immune Estimation Resource (TIMER).

Results: Data analysis revealed that higher immune/stromal/Estimate scores were significantly associated with better survival benefits in HCC within 7 years, while the tumor purity showed a reverse trend. DEGs based on both immune and stromal scores primarily affected the cytokine–cytokine receptor interaction signaling pathway. Among the DEGs, three genes (CASKIN1, EMR3, and GBP5) were found most significantly associated with survival. Moreover, the expression levels of CASKIN1, EMR3, and GBP5 genes were significantly correlated with immune/stromal/Estimate scores or tumor purity and multiple immune cell infiltration. Among them, GBP5 genes were highly related to immune infiltration.

Conclusion: This study identified three key genes which were related to the TME and had prognostic significance in HCC, which may be promising markers for predicting immunotherapy outcomes.

The Effects of Autophagy-Related Genes and lncRNAs in Therapy and Prognosis of Colorectal Cancer

Cellular autophagy plays an important role in the occurrence and development of colorectal cancer (CRC). Whether autophagy-related genes and lncRNAs can be used as ideal markers in CRC is still controversial. The purpose of this study is to identify novel treatment and prognosis markers of CRC. We downloaded transcription and clinical data of CRC from the GEO (GSE40967, GSE12954, GSE17536) and TCGA database, screened for differentially autophagy-related genes (DEAGs) and lncRNAs, constructed prognostic model, and analyzed its relationship with immune infiltration. TCGA and GEO datasets (GSE12954 and GSE17536) were used to validate the effect of the model. Oncomine database and Human Protein Atlas verified the expression of DEAGs. We obtained a total of 151 DEAGs in three verification sets collaboratively. Then we constructed a risk prognostic model through Lasso regression to obtain 15 prognostic DEAGs from the training set and verified the risk prognostic model in three verification sets. The low-risk group survived longer than the high-risk group. Age, gender, pathological stage, and TNM stage were related to the prognostic risk of CRC. On the other hand, BRAF status, RFS event, and tumor location are considered as most significant risk factors of CRC in the training set. Furthermore, we found that the immune score of the low-risk group was higher. The content of CD8 + T cells, active NK cells, macrophages M0, macrophages M1, and active dendritic cells was noted more in the high-risk group. The content of plasma cells, resting memory CD4 + T cells, resting NK cells, resting mast cells, and neutrophil cells was higher in the low-risk group. After all, the Oncomine database and immunohistochemistry verified that the expression level of most key autophagy-related genes was consistent with the results that we found. In addition, we obtained six lncRNAs co-expressed with DEAGs from the training set and found that the survival time was longer in the low-risk group. This finding was verified in the verification set and showed same trend to the results mentioned above. In the final analysis, these results indicate that autophagy-related genes and lncRNAs can be used as prognostic and therapeutic markers for CRC.

Molecular genetic testing strategies used in diagnostic flow for hereditary endocrine tumour syndromes

INTRODUCTION

Although current guidelines prefer the use of targeted testing or small-scale gene panels for identification of genetic susceptibility of hereditary endocrine tumour syndromes, next generation sequencing based strategies have been widely introduced into every day clinical practice. The application of next generation sequencing allows rapid testing of multiple genes in a cost effective manner. Increasing knowledge about these techniques and the demand from health care providers and society, shift the molecular genetic testing towards using high-throughput approaches.

PURPOSE

In this expert opinion, the authors consider the molecular diagnostic workflow step by step, evaluating options and challenges of gathering family information, pre- and post-test genetic counselling, technical and bioinformatical analysis related issues and difficulties in clinical interpretation focusing on molecular genetic testing of hereditary endocrine tumour syndromes.

RESULT AND CONCLUSION

Considering all these factors, a diagnostic genetic workflow is also proposed for selection of the best approach for testing of patients with hereditary genetic tumour syndromes in order to minimalize difficult interpretation, unwanted patient anxiety, unnecessary medical interventions and cost. There are potential benefits of utilizing high throughput approaches however, important limitations have to be considered and should discussed towards the clinicians and patients.

Data mining combined with experiments to validate CEP55 as a prognostic biomarker in colorectal cancer

INTRODUCTION

Colorectal cancer (CRC) is a common tumor with high morbidity and mortality. Current specific diagnosis regarding CRC remains complicated and costly, and specific diagnostic biomarkers are lacking.

METHODS

To find potential diagnostic and prognostic biomarkers for CRC, we screened and analyzed many CRC sequencing data by The Cancer Genome Atlas Program and Gene Expression Omnibus, and validated that CEP55 may be a potential diagnostic biomarker for CRC by molecular cytological experiments and immunohistochemistry, among others.

RESULTS

We found that CEP55 is upregulated in CRC tissues and tumor cells and can promote CRC proliferation and metastasis by activating the p53/p21 axis and that CEP55 mutations in tumor patients result in worse overall survival and disease-free survival time. Besides, we also found that genes, such as CDK1, CCNB1, NEK2, KIF14, CDCA5, and RFC3 were upregulated in tumors, and their mutations would affect the prognosis of CRC patients, but these results await for more experimental evidence.

CONCLUSION

Our study validates CEP55 as a potential diagnostic and prognostic biomarker for CRC, and we also provide multiple genes and potential molecular mechanisms that may serve as diagnostic and prognostic markers for CRC.

Biological functions and prognostic value of RNA Binding Proteins in clear cell Renal Cell Carcinoma

Early detection and accurate evaluation were both critical to improving the prognosis of clear cell Renal Cell Carcinoma (ccRCC) patients. More importantly, RNA Binding Proteins (RBPs) play a vital role in the tumorigenesis and progression of numerous cancers. However, the relationship between RBPs and ccRCC is still unclear. Exploring the potential biological functions of RBPs in ccRCC and establishing a prognostic signature to predict the survival probability remains meaningful. In this study, transcriptome profiling and the corresponding clinical information were obtained from the TCGA database, GEO database, and ICGC database. By using the "edgeR" R package, 200 DERBPs were found, including 128 up-regulated and 72 down-regulated RBPs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DERBPs were mainly involved in regulating transcriptional processes and metabolism. Furthermore, there were 4 hub genes (RPS2, RPS14, RPS20, and RPLP0) were found in the PPI network, which may play vital biological roles among those DERBPs. Then we used LASSO regression to construct a prognostic signature and validated the signature in the GEO and ICGC cohort. The time-dependent receiver operating characteristic (ROC) curve showed that the signature could accurately predict the prognosis of ccRCC patients. Then we established a nomogram, and the calibration curve and ROC curve showed that the nomogram could accurately predict 1-year, 3-year, and 5-year overall survival (OS) of ccRCC patients (The AUC value: 0.871, 0.829, and 0.816). In conclusion, we constructed a 10-RBPs-based prognostic signature integrating clinical parameters to predict the prognosis of ccRCC patients. The prognostic signature based on the differentially expressed RBPs (DERBPs) might serve as promising diagnostic and prognostic biomarkers in ccRCC.

High expression levels of DEF6 predicts a poor prognosis for patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant tumors and early detection contributes to a better prognosis. Finding new biomarkers for the diagnosis or treatment remains meaningful. DEF6 guanine nucleotide exchange factor (DEF6) is upregulated in ccRCC compared to normal controls, but the relationship between DEF6 expression and prognosis in ccRCC is unclear. Moreover, the potential biological functions of DEF6 in ccRCC remains unclear. In the present study, the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), TISIDB and the clinical database of the Peking University First Hospital were used to analyze DEF6 expression in ccRCC. Immunohistochemistry (IHC), western blotting and reverse transcription-quantitative PCR were used to examine the DEF6 protein and mRNA expression levels in cell lines and clinical samples. Subsequently, the Kaplan-Meier method and Cox regression analyses were used to determine the impact of DEF6 expression on the overall survival of patients alongside other clinical variables in both the TCGA database and the present clinical database. The results showed that both DEF6 mRNA and protein expression levels were upregulated in ccRCC compared to normal controls. The Kaplan-Meier survival analysis showed that patients with high DEF6 expression had poor prognoses from both the TCGA database and the present clinical database. Univariate survival analysis and multivariate survival analysis revealed that DEF6 could be an independent prognostic factor for ccRCC. Additionally, bioinformatics analysis indicated that differentially expressed genes related to DEF6 expression influenced ccRCC by regulating the tumor immune microenvironment. In conclusion, overexpression of DEF6 is significantly correlated with a poor prognosis for patients with ccRCC and DEF6 may influence the biological processes involved with ccRCC by regulating the immune microenvironment.

[RETRACTED: High-throughput sequencing in diagnostics and prevention of herpes simplex virus infection (Herpesviridae, Alphaherpesvirinae, Simplexvirus, Human alphaherpesvirus 1)]

RETRACTEDHerpes simplex viruses types 1 (HSV-1) and 2 (HSV-2) are among the most common viruses in the human population. The clinical manifestations of HSV infection vary widely, which necessitates reliable molecular methods for the timely diagnosis of herpes virus infection, as well as for detection of mutations in the genes responsible for drug resistance. PCR is often unable to detect HSV isolates with nucleotide substitutions at the primer binding site. Sanger sequencing of the whole genome reveals mutations mainly at the consensus level, which accumulate at advanced stages of viral infection. High-throughput sequencing (HTS, next generation sequencing) offers an obvious advantage both in early diagnosis of herpes virus infection and identification of HSV variants.

De Novo Assembly-Based Analysis of RPGR Exon ORF15 in an Indigenous African Cohort Overcomes Limitations of a Standard Next-Generation Sequencing (NGS) Data Analysis Pipeline

RPGR exon ORF15 variants are one of the most frequent causes for inherited retinal disorders (IRDs), in particular retinitis pigmentosa. The low sequence complexity of this mutation hotspot makes it prone to indels and challenging for sequence data analysis. Whole-exome sequencing generally fails to provide adequate coverage in this region. Therefore, complementary methods are needed to avoid false positives as well as negative results. In this study, next-generation sequencing (NGS) was used to sequence long-range PCR amplicons for an IRD cohort of African ancestry. By developing a novel secondary analysis pipeline based on de novo assembly, we were able to avoid the miscalling of variants generated by standard NGS analysis tools. We identified pathogenic variants in 11 patients (13% of the cohort), two of which have not been reported previously. We provide a novel and alternative end-to-end secondary analysis pipeline for targeted NGS of ORF15 that is less prone to false positive and negative variant calls.

The Community Oncology and Academic Medical Center Alliance in the Age of Precision Medicine: Cancer Genetics and Genomics Considerations

Recent public policy, governmental regulatory and economic trends have motivated the establishment and deepening of community health and academic medical center alliances. Accordingly, community oncology practices now deliver a significant portion of their oncology care in association with academic cancer centers. In the age of precision medicine, this alliance has acquired critical importance; novel advances in nucleic acid sequencing, the generation and analysis of immense data sets, the changing clinical landscape of hereditary cancer predisposition and ongoing discovery of novel, targeted therapies challenge community-based oncologists to deliver molecularly-informed health care. The active engagement of community oncology practices with academic partners helps with meeting these challenges; community/academic alliances result in improved cancer patient care and provider efficacy. Here, we review the community oncology and academic medical center alliance. We examine how practitioners may leverage academic center precision medicine-based cancer genetics and genomics programs to advance their patients' needs. We highlight a number of project initiatives at the City of Hope Comprehensive Cancer Center that seek to optimize community oncology and academic cancer center precision medicine interactions.

Molecular genetic diagnostics of hypogonadotropic hypogonadism: from panel design towards result interpretation in clinical practice

Congenital hypogonadotropic hypogonadism (CHH) is a clinically and genetically heterogeneous congenital disease. Symptoms cover a wide spectrum from mild forms to complex phenotypes due to gonadotropin-releasing hormone (GnRH) deficiency. To date, more than 40 genes have been identified as pathogenic cause of CHH. These genes could be grouped into two major categories: genes controlling development and GnRH neuron migration and genes being responsible for neuroendocrine regulation and GnRH neuron function. High-throughput, next-generation sequencing (NGS) allows to analyze numerous gene sequences at the same time. Nowadays, whole exome or whole genome datasets could be investigated in clinical genetic diagnostics due to their favorable cost-benefit. The increasing genetic data generated by NGS reveal novel candidate genes and gene variants with unknown significance (VUSs). To provide clinically valuable genetic results, complex clinical and bioinformatics work are needed. The multifaceted genetics of CHH, the variable mode of inheritance, the incomplete penetrance, variable expressivity and oligogenic characteristics further complicate the interpretation of the genetic variants detected. The objective of this work, apart from reviewing the currently known genes associated with CHH, was to summarize the advantages and disadvantages of the NGS-based platforms and through the authors' own practice to guide through the whole workflow starting from gene panel design, performance analysis and result interpretation. Based on our results, a genetic diagnosis was clearly identified in 21% of cases tested (8/38).

Update on molecular companion diagnostics - a future in personalized medicine beyond Sanger sequencing

INTRODUCTION

The merging of molecular diagnostics with personalized medicine has led to a surge in development of molecular-based companion diagnostics. Companion diagnostics, defined as 'a medical device, often an in vitro device, which provides information that is essential for the safe and effective use of a corresponding drug or biological product', are key to the appropriate utilization of several pharmacotherapies; primarily in the area of oncology.

AREAS COVERED

While most molecular companion diagnostics are targeted toward oncology, the potential to multiplex assays will contribute to an expansion in the applications of companion diagnostics for an increasing menu of disease states and conditions including areas such as infectious disease, cardiology, and hematology.

EXPERT OPINION

With this innovation comes the responsibility to ensure molecular companion diagnostic devices are robust and controlled against the detrimental effects of false positive/negative results. Additional important considerations, such as paired development with pharmaceutical companies and adherence to Food and Drug Administration and/or European Union guidelines, must be addressed. While the current number of companion diagnostics is relatively small, as molecular assays continue to be developed as companion diagnostics the world of personalized medicine will advance to meet the needs of an expanding portion of the patient population.

Review of current status of molecular diagnosis and characterization of monogenic diabetes mellitus: a focus on next-generation sequencing

Introduction: Monogenic diabetes is a subset of diabetes characterized by the presence of single-gene mutations and includes neonatal diabetes mellitus and maturity-onset diabetes of the young. Due to the genetic etiology of monogenic diabetes, molecular genetic testing can be used for diagnosis and classification.Areas covered: In addition to first-generation molecular analyses, many large clinical laboratories are transitioning to multiplexed next-generation sequencing panels to simultaneously assess patients for several of the most common genetic mutations seen in monogenic diabetes. With expanded development and adoption of next-generation sequencing panels, particularly in reference to laboratory settings, diagnostic testing for monogenic diabetes has the potential to be more accessible to the patient population.Expert opinion: Although molecular diagnostic testing is becoming increasingly prevalent, it is crucial to identify patients most likely to benefit from molecular testing versus those whose disease can be diagnosed and characterized with more traditional, less costly laboratory analyses. The continuous evolution of clinical molecular testing will be echoed in the clinical laboratory analysis of monogenic diabetes and continue to improve the diagnostic capabilities for monogenic diabetes mellitus.