Construction of a 6-gene prognostic signature to assess prognosis of patients with pancreatic cancer

Yang, Jiayue; Wei, Shi; Yang, Cheng

doi:10.1097/md.0000000000022092

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Machine learning algorithms have been used to classify some cancer types, but not lung adenocarcinoma, in which NDUFS5, P2RY2, PRPF18, CCL24, ZNF813, MYL6, FLJ41941, POU5F1B, and SUV420H1 were associated with alive without disease [23]. The study identified MACF1 with FTSJ3, STAT1, STX2, CDX2 and RASSF4 that can be used as a signature to predict the overall survival of pancreatic cancer patients [84]. The poor response of low-grade serous ovarian carcinoma (LGSOC) to chemotherapy calls for a thorough genomic analysis to identify new treatment options.…”

Section: Pathway 2: Ctla4-cd8a-cd8bmentioning

confidence: 99%

Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways

Nguyen

2024

BMC Bioinformatics

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Background Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery from genetic data. Method We propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware. Results We demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation. Conclusion The model indicates new genetic biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks.

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Section: Pathway 2: Ctla4-cd8a-cd8bmentioning

confidence: 99%

Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways

Nguyen

2024

BMC Bioinformatics

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“…ssGSEA was used to establish m6A_score to determine the Similarly, a high m6A_score in the ICGC cohort correlated with poor prognosis (Figure 5(g)), and five-year survival AUC reached 0.71 (Figure 5(h)). We selected several gene features from recent studies, such as, Li et al's three-gene feature, 33 Yang et al's six-gene feature, 34 and Yan et al's four-gene feature. 35 Comparing these three gene characteristics with the AUC and C-index of m6AScore, they all have the best predictive performance for the five-year survival rate.…”

Section: The Clinical Significance Of M6a Regulator In Digestive Tumorsmentioning

confidence: 99%

Systematic analysis of molecular characterization and clinical relevance of m6A regulators in digestive system pan-cancers

Kou

Chai

Jin

et al. 2021

Exp Biol Med (Maywood)

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Digestive system tumors, which mainly include esophagus, stomach, colorectum, liver, pancreas, bile duct, and some other tumors, often have a poor prognosis. N6-methyladenosine (m6A) has critical functions in development and tumorigenesis and may help improve the molecular mechanisms of digestive system tumors. However, current understanding of the reconstitution of m6A in digestive system tumors is far from comprehensive. Herein, this study systematically analyzed multi-layered genomic characteristics and clinical relevance of m6A regulators in 1906 patients involving seven digestive system tumor types. We discovered that m6A regulators showed extensive genetic changes and highly consistent expression regulation. The m6A expression was closely related to the activity of cancer pathways. At the same time, we also identified m6A regulators significantly related to the common cancer pathways of digestive system tumors and specific cancer pathways of digestive tract and digestive glands. These cancer pathways may explain the prognostic differences of patients with digestive tract tumors. In addition, m6A regulators demonstrated strong potential in prognostic stratification and drug development, especially in multiple research cohorts on pancreatic cancer, pointing to a strong prognostic stratification capability of m6A regulators. Finally, a m6A scoring model significantly related to highly active ubiquitin-mediated proteolysis, mismatch repair, cell cycle, ebasal transcription factors was constructed and had a strong prognostic stratification ability in digestive gland tumors. The score showed a significant negative correlation with the tumor immune microenvironment. This study demonstrated that the similarities and difference of the action mechanism m6A regulators in the digestive tract and digestive gland tumor progression could guide potential drug development.

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“…The primary reasons for the low survival rate of pancreatic patients are that the disease remains asymptomatic until advanced stages due to the anatomical position of the pancreas in the retroperitoneum and the lack of valuable biomarkers for early stages can be considered as other reasons [6,7]. Although the primary root of pancreatic cancer has not been clearly identi ed, some genes have been previously shown to be associated with the various cancer subtypes, treatment responses, or poor prognosis in pancreatic cancer [8][9][10]. Many cancers cannot be cured in the advanced stage, therefore developing novel biomarkers for the early stage is a potential approach for diagnosis, prognosis, and treatment of pancreatic cancer [11].…”

Section: Introductionmentioning

confidence: 99%

Down regulation of Cathepsin W is associated with poor prognosis in Pancreatic cancer

Khojasteh-Leylakoohi

Mohit

Khalili-Tanha

et al. 2022

Preprint

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Background: Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on the identification of new biomarkers for the early diagnosis of PDAC and prediction of patient survival. Genome-wide RNA and microRNA sequencing were used using bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs) followed by validation in additional cohort of PDAC patients. Methods: genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA) to identify DEGs. We used Kaplan-Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest, (RF), Max Voting, Adaboost, Gradient boosting machines (GBM) and Extreme Gradient Boosting (XGB) techniques were used and Gradient boosting machines (GBM) were selected with 100 % accuracy for analysis. Moreover, protein-protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs and a combination of these obtained from machine learning algorithms and survival analysis. Results: Machine learning results showed 23 genes with negative regulation, 5 genes with positive regulation, 7 microRNAs with negative regulation and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of disease. In addition, the survival analysis results showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9 and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Conclusion: Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in pathogenesis of the diseases can be used for detection of patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC.

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Construction of a 6-gene prognostic signature to assess prognosis of patients with pancreatic cancer

Abstract: Supplemental Digital Content is available in the text

Cited by 5 publications

References 25 publications

Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways

Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways

Systematic analysis of molecular characterization and clinical relevance of m6A regulators in digestive system pan-cancers

Down regulation of Cathepsin W is associated with poor prognosis in Pancreatic cancer

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