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

Long, Nguyen Phuoc; Jung, Kyung Hee; Anh, Nguyễn Hoàng; Zhang, Pengcheng; Nghi, Tran Diem; Park, Seongoh; Yoon, Sang Jun; Min, Jung Eun; Kim, Hyung Min; Lim, Joo Han; Kim, Joon Mee; Lim, Jong-Tae; Lee, Sanghyuk; Hong, Soon‐Sun; Kwon, Sung Won

doi:10.3390/cancers11020155

Cited by 43 publications

(31 citation statements)

References 56 publications

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“…In contrast to previous studies that used a transcriptomic diagnostic signature based on complicated procedures of data normalization and parameter fitting ( Klett et al, 2018 ; Long et al, 2019 ), we analyzed the relative expression of gene pairs, instead of the expression of single genes, to differentiate PC from benign lesions. Our transcriptional REO-based signature employed relative ranking of gene expression by identifying multiple gene pairs and can be used without confounding from the batch effect and use of different sequencing platforms ( Eddy et al, 2010 ).…”

Section: Discussionmentioning

confidence: 99%

Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer

Zhou

Meng

et al. 2020

Front. Mol. Biosci.

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It is currently difficult for pathologists to diagnose pancreatic cancer (PC) using biopsy specimens because samples may have been from an incorrect site or contain an insufficient amount of tissue. Thus, there is a need to develop a platform-independent molecular classifier that accurately distinguishes benign pancreatic lesions from PC. Here, we developed a robust qualitative messenger RNA signature based on within-sample relative expression orderings (REOs) of genes to discriminate both PC tissues and cancer-adjacent normal tissues from non-PC pancreatitis and healthy pancreatic tissues. A signature comprising 12 gene pairs and 17 genes was built in the training datasets and validated in microarray and RNA-sequencing datasets from biopsy samples and surgically resected samples. Analysis of 1,007 PC tissues and 257 non-tumor samples from nine databases indicated that the geometric mean of sensitivity and specificity was 96.7%, and the area under receiver operating characteristic curve was 0.978 (95% confidence interval, 0.947–0.994). For 20 specimens obtained from endoscopic biopsy, the signature had a diagnostic accuracy of 100%. The REO-based signature described here can aid in the molecular diagnosis of PC and may facilitate objective differentiation between benign and malignant pancreatic lesions.

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Section: Discussionmentioning

confidence: 99%

Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer

Zhou

Meng

et al. 2020

Front. Mol. Biosci.

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“…In recent years, some previous works have revealed CHGA as a novel biomarker for PC [ 30 – 32 ]. ITGB6 and LAMC2 had been reported to be associated with activation of the EMT, cell adhesion, TGF β , PI3K-AKT, and MAPK pathways [ 33 – 39 ], which are all involved in PC tumorigenesis. Additionally, these pathways were also enriched in our study ( Figure 1 and Supplementary Figure 2 ).…”

Section: Discussionmentioning

confidence: 99%

Establishment and Investigation of a Multiple Gene Expression Signature to Predict Long-Term Survival in Pancreatic Cancer

Zhang

Yin

et al. 2020

BioMed Research International

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Pancreatic cancer remains a lethal type of cancer with poor prognosis. Molecular classification enables in-depth, precise prognostic assessment. This study is aimed at identifying a robust and simple mRNA signature to predict the overall survival (OS) of pancreatic cancer (PC) patients. Differentially expressed genes (DEGs) between 45 paired pancreatic tumor samples and adjacent healthy tissues were selected. For risk determination, a LASSO Cox regression model with DEGs was used to generate the OS-associated risk score formula for the training cohort containing 177 PC patients. Another five independent datasets were used as the testing cohort to determine the predictive efficiency for further validation. In total, 441 DEGs were selected after considering the enrichment of classical pathways, such as EMT, cell cycle, cell adhesion, and PI3K-AKT. A five-gene signature for risk discrimination was established with high efficacy using LASSO Cox regression in the training group. External validation showed that patients identified by the gene expression signature to be in the high-risk group had poorer prognosis compared with the low-risk patients. Further investigation identified the differential epigenetic modification patterns of the five genes, which indicated their roles in tumor progression and their effect on therapy. In conclusion, we constructed a robust five-gene expression signature that could predict the OS of PC patients, offering a new insight for risk discrimination in daily clinical practice.

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“…Additionally, the opaque methods usually yield accurate predictions but are more complicated to interpret than the transparent ones. As a solution, algorithms such as LIME and iBreakDown show promise for explaining the predictions of any complex, black-box model [134][135][136][137]. It is worth mentioning that the so-called explainable prediction models are most beneficial from the datasets that have a small number of features from input data.…”

Section: Toward Reproducible Data Analysis and Interpretation In Clinmentioning

confidence: 99%

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

et al. 2020

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Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional “pre-pre-” and “post-post-” analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

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

Cited by 43 publications

References 56 publications

Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer

Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer

Establishment and Investigation of a Multiple Gene Expression Signature to Predict Long-Term Survival in Pancreatic Cancer

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

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