Efficacy of Integrating a Novel 16-Gene Biomarker Panel and Intelligence Classifiers for Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis

Long, Nguyen Phuoc; Park, Seongoh; Anh, Nguyễn Hoàng; Min, Jung Eun; Yoon, Sang Jun; Kim, Hyung Min; Nghi, Tran Diem; Lim, Dong Kyu; Park, Jeong Hill; Lim, Jong-Tae; Kwon, Sung Won

doi:10.3390/jcm8010050

Cited by 35 publications

(37 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent advances in high-throughput multi–omic technologies along with advanced bioinformatics and machine learning techniques have provided a new paradigm for the discovery of human disease biomarkers [10,46,58,59]. This perspective should remark the continued employment of multi-omic approaches and machine learning techniques under the guidance of big data and biological mechanisms in cancer biomarker research.…”

Section: Discussionmentioning

confidence: 99%

An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer

Long

Jung

Anh

et al. 2019

Cancers

Self Cite

View full text Add to dashboard Cite

Substantial alterations at the multi-omics level of pancreatic cancer (PC) impede the possibility to diagnose and treat patients in early stages. Herein, we conducted an integrative omics-based translational analysis, utilizing next-generation sequencing, transcriptome meta-analysis, and immunohistochemistry, combined with statistical learning, to validate multiplex biomarker candidates for the diagnosis, prognosis, and management of PC. Experiment-based validation was conducted and supportive evidence for the essentiality of the candidates in PC were found at gene expression or protein level by practical biochemical methods. Remarkably, the random forests (RF) model exhibited an excellent diagnostic performance and LAMC2, ANXA2, ADAM9, and APLP2 greatly influenced its decisions. An explanation approach for the RF model was successfully constructed. Moreover, protein expression of LAMC2, ANXA2, ADAM9, and APLP2 was found correlated and significantly higher in PC patients in independent cohorts. Survival analysis revealed that patients with high expression of ADAM9 (Hazard ratio (HR)OS = 2.2, p-value < 0.001), ANXA2 (HROS = 2.1, p-value < 0.001), and LAMC2 (HRDFS = 1.8, p-value = 0.012) exhibited poorer survival rates. In conclusion, we successfully explore hidden biological insights from large-scale omics data and suggest that LAMC2, ANXA2, ADAM9, and APLP2 are robust biomarkers for early diagnosis, prognosis, and management for PC.

show abstract

Section: Discussionmentioning

confidence: 99%

An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer

Long

Jung

Anh

et al. 2019

Cancers

Self Cite

View full text Add to dashboard Cite

show abstract

“…The technological advances of omics platforms and bioinformatic analyses have favoured a greater knowledge of the biological behaviour and dynamics of a variety of neoplastic entities including PDAC [10,108,109,110], favouring earlier diagnosis in asymptomatic patients [111,112]. In a time of special transcendence in oncology, PDAC remains to be a problem of significant magnitude.…”

Section: Discussionmentioning

confidence: 99%

Omics Approaches in Pancreatic Adenocarcinoma

González-Borja

Viúdez

Goñi

et al. 2019

Cancers

View full text Add to dashboard Cite

Pancreatic ductal adenocarcinoma, which represents 80% of pancreatic cancers, is mainly diagnosed when treatment with curative intent is not possible. Consequently, the overall five-year survival rate is extremely dismal—around 5% to 7%. In addition, pancreatic cancer is expected to become the second leading cause of cancer-related death by 2030. Therefore, advances in screening, prevention and treatment are urgently needed. Fortunately, a wide range of approaches could help shed light in this area. Beyond the use of cytological or histological samples focusing in diagnosis, a plethora of new approaches are currently being used for a deeper characterization of pancreatic ductal adenocarcinoma, including genetic, epigenetic, and/or proteo-transcriptomic techniques. Accordingly, the development of new analytical technologies using body fluids (blood, bile, urine, etc.) to analyze tumor derived molecules has become a priority in pancreatic ductal adenocarcinoma due to the hard accessibility to tumor samples. These types of technologies will lead us to improve the outcome of pancreatic ductal adenocarcinoma patients.

show abstract

“…Accordingly, GZMK + CD8 T cells were found to induce inflammation in the synovium of RA patients [31] and elevated expression of GZMK was proposed as a biomarker to discern RA patients from those suffering from the related osteoarthritis [32]. Our analysis also revealed genes influenced by Crohn's disease, another autoimmune disease.…”

Section: Gene Expression Snpmentioning

confidence: 82%

Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome

Porcu

Sadler

Lepik

et al. 2020

Preprint

View full text Add to dashboard Cite

Comparing transcript levels between healthy and diseased individuals allows the identification of differentially expressed genes, which may be causes, consequences or mere correlates of the disease under scrutiny. Here, we propose a bi-directional Transcriptome-Wide Mendelian Randomization (TWMR) approach that integrates summary-level data from GWAS and whole-blood eQTLs in a MR framework to investigate the causal effects between gene expression and complex traits. Whereas we have previously developed a TWMR approach to elucidate gene expression to trait causal effects, here we are adapting the method to shed light on the causal imprint of complex traits on transcript levels. We termed this new approach reverse TWMR (revTWMR). Integrating bi-directional causal effects between gene expression and complex traits enables to evaluate their respective contributions to the correlation between gene expression and traits. We uncovered that whole blood gene expression-trait correlation is mainly driven by causal effect from the phenotype on the expression rather than the reverse. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects (r=0.09, P=1.54x10-39 and r=0.09, P=1.19x10-34, respectively), but not detectably with expression-to-trait effects. Genes implicated by revTWMR confirmed known associations, such as rheumathoid arthritis and Crohn's disease induced changes in expression of TRBV and GBP2, respectively. They also shed light on how clinical biomarkers can influence their own levels. For instance, we observed that high levels of high-density lipoprotein (HDL) cholesterol lowers the expression of genes involved in cholesterol biosynthesis (SQLE, FDFT1) and increases the expression of genes responsible for cholesterol efflux (ABCA1, ABCG1), two key molecular pathways in determining HDL levels. Importantly, revTWMR is more robust to pleiotropy than polygenic risk score (PRS) approaches which can be misled by pleiotropic outliers. As one example, revTWMR revealed that the previously reported association between educational attainment PRS and STX1B is exclusively driven by a highly pleiotropic SNP (rs2456973), which is strongly associated with several hematological and anthropometric traits. In conclusion, our method disentangles the relationship between gene expression and phenotypes and reveals that complex traits have more pronounced impact on gene expression than the reverse. We demonstrated that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones.

show abstract

Efficacy of Integrating a Novel 16-Gene Biomarker Panel and Intelligence Classifiers for Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis

Cited by 35 publications

References 53 publications

An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer

An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer

Omics Approaches in Pancreatic Adenocarcinoma

Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome

Contact Info

Product

Resources

About