ArticleWorld journal of surgical oncology2018
Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods.
Article in World journal of surgical oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Identification of key hub genes in pancreatic ductal adenocarcinoma: an integrative bioinformatics study.Frontiers in bioinformatics · 2025Article
- Low ligation of the inferior mesenteric artery in robotic mid-low rectal cancer surgery: a comparative study from a single-center.Journal of robotic surgery · 2024Article
- Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES).BMC bioinformatics · 2024Article
- Systems biology approach: identification of hub genes, signaling pathways, and molecular docking of COL1A1 gene in cervical insufficiency.In silico pharmacology · 2024Article
- High tie sigmoidectomy syndrome.Techniques in coloproctology · 2023Article
- Molecular Markers of Pancreatic Cancer: A 10-Year Retrospective Review of Molecular Advances.Cureus · 2022Review
- Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients.Molecular medicine (Cambridge, Mass.) · 2022Article
- Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure.IEEE journal of translational engineering in health and medicine · 2022Article
- Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy.Frontiers in cardiovascular medicine · 2022Article
- An Integrated Data Analysis of mRNA, miRNA and Signaling Pathways in Pancreatic Cancer.Biochemical genetics · 2021Article
- ITGA2, LAMB3, and LAMC2 may be the potential therapeutic targets in pancreatic ductal adenocarcinoma: an integrated bioinformatics analysis.Scientific reports · 2021Article
- Integration of gene expression data identifies key genes and pathways in colorectal cancer.Medical oncology (Northwood, London, England) · 2021Article
- NMFNA: A Non-negative Matrix Factorization Network Analysis Method for Identifying Modules and Characteristic Genes of Pancreatic Cancer.Frontiers in genetics · 2021Article
- Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis.PeerJ · 2021Article
- Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO.Frontiers in bioengineering and biotechnology · 2021Article
- Identification of potential crucial genes associated with the pathogenesis and prognosis of pancreatic adenocarcinoma.Oncology letters · 2020Article
- Development of a four-gene prognostic model for pancreatic cancer based on transcriptome dysregulation.Aging · 2020Article
- miR-216a-mediated upregulation of TSPAN1 contributes to pancreatic cancer progression via transcriptional regulation of ITGA2.American journal of cancer research · 2020Article
- Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer.Molecular medicine (Cambridge, Mass.) · 2019Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundPancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future.
methodsCombination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms.
resultsAfter quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively.
conclusionsAll the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.