Genetic variations in the PRKCG gene and osteosarcoma risk in a Chinese population: a case-control study

Genetic variants associated with osteosarcoma risk: a systematic review and meta-analysis

Osteosarcoma (OS) is the most common type of primary bone malignancy. Common genetic variants including single nucleotide polymorphisms (SNPs) have been associated with osteosarcoma risk, however, the results of published studies are inconsistent. The aim of this study was to systematically review genetic association studies to identify SNPs associated with osteosarcoma risk and the effect of race on these associations. We searched the Medline, Embase, Scopus from inception to the end of 2019. Seventy-five articles were eligible for inclusion. These studies investigated the association of 190 SNPs across 79 genes with osteosarcoma, 18 SNPs were associated with the risk of osteosarcoma in the main analysis or in subgroup analysis. Subgroup analysis displayed conflicting effects between Asians and Caucasians. Our review comprehensively summarized the results of published studies investigating the association of genetic variants with osteosarcoma susceptibility, however, their potential value should be confirmed in larger cohorts in different ethnicities.

Epidemiological Evidence for Associations Between Genetic Variants and Osteosarcoma Susceptibility: A Meta-Analysis

Background

Previous studies have showed that single nucleotide polymorphisms (SNPs) might be implicated in the pathogenesis of osteosarcoma (OS). Numerous studies involving SNPs with OS risk have been reported; these results, however, remain controversial and no comprehensive research synopsis has been performed till now.

Objective

This study seeks to clarify the relationships between SNPs and OS risk using a comprehensive meta-analysis, and assess epidemiological evidence of significant associations.

Methods

The PubMed, Web of Science, and Medline were used to screen for articles that evaluated the association between SNP and OS susceptibility in humans before 24 December 2021. Furthermore, we used Venice Criteria and a false positive report probability (FPRP) test to assess the grades of epidemiological evidence for the statistical relationships.

Results

We extracted useful data based on 43 articles, including 10,255 cases and 13,733 controls. Our results presented that 25 SNPs in 17 genes were significantly associated with OS risk. Finally, we graded strong evidence for 17 SNPs in 14 genes with OS risk (APE1 rs1760944, BCAS1 rs3787547, CTLA4 rs231775, ERCC3 rs4150506, HOTAIR rs7958904, IL6 rs1800795, IL8 rs4073, MTAP rs7023329 and rs7027989, PRKCG rs454006, RECQL5 rs820196, TP53 rs1042522, VEGF rs3025039, rs699947 and rs2010963, VMP1 rs1295925, XRCC3 rs861539), moderate for 14 SNPs in 12 genes and weak for 14 SNPs in 11 genes.

Conclusion

In summary, this study offered a comprehensive meta-analysis between SNPs and OS susceptibility, then evaluated the credibility of statistical relationships, and provided useful information to identify the appropriate candidate SNPs and design future studies to evaluate SNP factors for OS risk.

PRKCE non-coding variants influence on transcription as well as translation of its gene

Untranslated regions of the gene play a crucial role in gene expression regulation at mRNA and protein levels. Mutations at UTRs impact expression by altering transcription factor binding, transcriptional/translational efficacy, miRNA-mediated gene regulation, mRNA secondary structure, ribosomal translocation, and stability. PKCε, a serine/threonine kinase, is aberrantly expressed in numerous diseases such as cardiovascular disorders, neurological disorders, and cancers; its probable cause is unknown. Therefore, in the current study, the influence of PRKCE 5'-and 3'UTR variants was explored for their potential impact on its transcription and translation through several bioinformatics approaches. UTR variants data was obtained through different databases and initially evaluated for their regulatory function. Variants with regulatory function were then studied for their effect on PRKCE binding with transcription factors (TF) and miRNAs, as well as their impact on mRNA secondary structure. Study outcomes indicated the regulatory function of 73 5'UTR and 17 3'UTR variants out of 376. 5'UTR variants introduced AP1 binding sites and promoted the PRKCE transcription. Four 3'UTR variants introduced a circular secondary structure, increasing PRKCE translational efficacy. A region in 5'UTR position 45,651,564 to 45,651,644 was found where variants readily influenced the miRNA-PRKCE mRNA binding. The study further highlighted a PKCε-regulated feedback loop mechanism that induces the activity of TFs, promoting its gene transcription. The study provides foundations for experimentation to understand these variants' role in diseases. These variants can also serve as the genetic markers for different diseases' diagnoses after validation at the cell and population levels.

Integrated Analysis to Identify a Redox-Related Prognostic Signature for Clear Cell Renal Cell Carcinoma

The imbalance of the redox system has been shown to be closely related to the occurrence and progression of many cancers. However, the biological function and clinical significance of redox-related genes (RRGs) in clear cell renal cell carcinoma (ccRCC) are unclear. In our current study, we downloaded transcriptome data from The Cancer Genome Atlas (TCGA) database of ccRCC patients and identified the differential expression of RRGs in tumor and normal kidney tissues. Then, we identified a total of 344 differentially expressed RRGs, including 234 upregulated and 110 downregulated RRGs. Fourteen prognosis-related RRGs (ADAM8, CGN, EIF4EBP1, FOXM1, G6PC, HAMP, HTR2C, ITIH4, LTB4R, MMP3, PLG, PRKCG, SAA1, and VWF) were selected out, and a prognosis-related signature was constructed based on these RRGs. Survival analysis showed that overall survival was lower in the high-risk group than in the low-risk group. The area under the receiver operating characteristic curve of the risk score signature was 0.728 at three years and 0.759 at five years in the TCGA cohort and 0.804 at three years and 0.829 at five years in the E-MTAB-1980 cohort, showing good predictive performance. In addition, we explored the regulatory relationships of these RRGs with upstream miRNA, their biological functions and molecular mechanisms, and their relationship with immune cell infiltration. We also established a nomogram based on these prognostic RRGs and performed internal and external validation in the TCGA and E-MTAB-1980 cohorts, respectively, showing an accurate prediction of ccRCC prognosis. Moreover, a stratified analysis showed a significant correlation between the prognostic signature and ccRCC progression.

Development of an AmpliSeqTM Panel for Next-Generation Sequencing of a Set of Genetic Predictors of Persisting Pain

Background: Many gene variants modulate the individual perception of pain and possibly also its persistence. The limited selection of single functional variants is increasingly being replaced by analyses of the full coding and regulatory sequences of pain-relevant genes accessible by means of next generation sequencing (NGS).

Methods: An NGS panel was created for a set of 77 human genes selected following different lines of evidence supporting their role in persisting pain. To address the role of these candidate genes, we established a sequencing assay based on a custom AmpliSeq^TM panel to assess the exomic sequences in 72 subjects of Caucasian ethnicity. To identify the systems biology of the genes, the biological functions associated with these genes were assessed by means of a computational over-representation analysis.

Results: Sequencing generated a median of 2.85 ⋅ 10⁶ reads per run with a mean depth close to 200 reads, mean read length of 205 called bases and an average chip loading of 71%. A total of 3,185 genetic variants were called. A computational functional genomics analysis indicated that the proposed NGS gene panel covers biological processes identified previously as characterizing the functional genomics of persisting pain.

Conclusion: Results of the NGS assay suggested that the produced nucleotide sequences are comparable to those earned with the classical Sanger sequencing technique. The assay is applicable for small to large-scale experimental setups to target the accessing of information about any nucleotide within the addressed genes in a study cohort.

Identify Cross Talk Between Circadian Rhythm and Coronary Heart Disease by Multiple Correlation Analysis

Disorder in circadian rhythm has been revealed as a risk factor for coronary heart disease. Several studies in molecular biology established a gene interaction network using coronary heart susceptibility genes and the circadian rhythm pathway. However, cross talk between genes was mostly discovered in single gene pairs. There might be combination sets of genes intergraded as a unit to regulate the network. To resolve multiple variables in coronary heart susceptibility genes controlling circadian rhythm pathways, a multiple correlation analysis was applied to the transcriptome. Nine genes, including CUGBP, Elav-like family member (CELF); sodium leak channel, nonselective (NALCN); protein phosphatase 2 regulatory subunit B gamma (PPP2R2C); tubulin alpha 1c (TUBA1C); microtubule-associated protein 4 (MAP4); cofilin 1 (CFL1); myosin heavy chain 7 (MYH7); QKI, KH domain containing RNA binding (QKI); and maternal embryonic leucine zipper kinase (MELK), from coronary heart susceptibility were identified to predict the outcome of a linear combination of circadian rhythm pathway genes with R factor more than 0.7. G protein subunit alpha o1 (GNAO1), protein kinase C gamma (PRKCG), RBX, and G protein subunit beta 1 (GNB1) in the circadian rhythm pathway are characterized as combination variables to coexpress with coronary heart susceptibility genes.

Genetic susceptibility to bone and soft tissue sarcomas: a field synopsis and meta-analysis

Background

The genetic architecture of bone and soft tissue sarcomas susceptibility is yet to be elucidated. We aimed to comprehensively collect and meta-analyze the current knowledge on genetic susceptibility in these rare tumors.

Methods

We conducted a systematic review and meta-analysis of the evidence on the association between DNA variation and risk of developing sarcomas through searching PubMed, The Cochrane Library, Scopus and Web of Science databases. To evaluate result credibility, summary evidence was graded according to the Venice criteria and false positive report probability (FPRP) was calculated to further validate result noteworthiness. Integrative analysis of genetic and eQTL (expression quantitative trait locus) data was coupled with network and pathway analysis to explore the hypothesis that specific cell functions are involved in sarcoma predisposition.

Results

We retrieved 90 eligible studies comprising 47,796 subjects (cases: 14,358, 30%) and investigating 1,126 polymorphisms involving 320 distinct genes. Meta-analysis identified 55 single nucleotide polymorphisms (SNPs) significantly associated with disease risk with a high (N=9), moderate (N=38) and low (N=8) level of evidence, findings being classified as noteworthy basically only when the level of evidence was high. The estimated joint population attributable risk for three independent SNPs (rs11599754 of ZNF365/EGR2, rs231775 of CTLA4, and rs454006 of PRKCG) was 37.2%. We also identified 53 SNPs significantly associated with sarcoma risk based on single studies.Pathway analysis enabled us to propose that sarcoma predisposition might be linked especially to germline variation of genes whose products are involved in the function of the DNA repair machinery.

Conclusions

We built the first knowledgebase on the evidence linking DNA variation to sarcomas susceptibility, which can be used to generate mechanistic hypotheses and inform future studies in this field of oncology.

Kernel differential subgraph reveals dynamic changes in biomolecular networks

Many major diseases, including various types of cancer, are increasingly threatening human health. However, the mechanisms of the dynamic processes underlying these diseases remain ambiguous. From the holistic perspective of systems science, complex biological networks can reveal biological phenomena. Changes among networks in different states influence the direction of living organisms. The identification of the kernel differential subgraph (KDS) that leads to drastic changes is critical. The existing studies contribute to the identification of a KDS in networks with the same nodes; however, networks in different states involve the disappearance of some nodes or the appearance of some new nodes. In this paper, we propose a new topology-based KDS (TKDS) method to explore the core module from gene regulatory networks with different nodes in this process. For the common nodes, the TKDS method considers the differential value (D-value) of the topological change. For the different nodes, TKDS identifies the most similar gene pairs and computes the D-value. Hence, TKDS discovers the essential KDS, which considers the relationships between the same nodes as well as different nodes. After applying this method to non-small cell lung cancer (NSCLC), we identified 30 genes that are most likely related to NSCLC and extracted the KDSs in both the cancer and normal states. Two significance functional modules were revealed, and gene ontology (GO) analyses and literature mining indicated that the KDSs are essential to the processes in NSCLC. In addition, compared with existing methods, TKDS provides a unique perspective in identifying particular genes and KDSs related to NSCLC. Moreover, TKDS has the potential to predict other critical disease-related genes and modules.