ArticleJournal of personalized medicine2021
LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 33 citations in OpenAlex.
- Decoding causal m6A: a bioinformatics roadmap for psychiatric disorders.Briefings in bioinformatics · 2026Review
- Precision Biomarker Identification in Gynecological Cancers Using Coexpression Networks and Attention-Based LSTM in Healthcare 4.0.Diagnostics (Basel, Switzerland) · 2026Article
- Machine Learning Models for Cancer Research: A Narrative Review of Bulk RNA-Seq Applications.International journal of molecular sciences · 2025Review
- The hidden dangers in routine medical complaints: uncovering patient harm.BMC health services research · 2025Article
- Genetic Architecture of Hock Joint Bumps in Pigs: Insights from ROH and GWAS Analyses.Animals : an open access journal from MDPI · 2025Article
- Wrangling Real-World Data: Optimizing Clinical Research Through Factor Selection with LASSO Regression.International journal of environmental research and public health · 2025Article
- Transcriptome profiling revealed multiple circadian rhythm-related genes associated with common gynecological cancers.Frontiers in oncology · 2025Article
- Copper Metabolism-Related Genes as Biomarkers in Colon Adenoma and Cancer.International journal of general medicine · 2025Article
- Precision Medicine: Transforming Cancer Research through Targeted Therapies.Current genomics · 2025Review
- Expression Profiles of Hypoxia-Related Genes of Cancers Originating from Anatomically Similar Locations Using TCGA Database Analysis.Medicines (Basel, Switzerland) · 2023Article
- A thirty-three gene-based signature predicts lymph node metastasis and prognosis in patients with gastric cancer.Heliyon · 2023Article
- Transcriptomic characterization and construction of M2 macrophage-related prognostic and immunotherapeutic signature in ovarian metastasis of gastric cancer.Cancer immunology, immunotherapy : CII · 2023Article
- Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning.Scientific reports · 2023Article
- Regulation of KInternational journal of molecular sciences · 2022Review
- A pretreatment transcriptomic signature that predicts outcomes of immunotherapy in melanoma.Heliyon · 2022Article
- Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models.Scientific reports · 2022Article
- Long non-coding RNAs PGM5-AS1 upregulates Decorin (DCN) to inhibit cervical cancer progression by sponging miR-4284.Bioengineered · 2022Article
- Molecular Subtypes and Prognostic Signature of Pyroptosis-Related lncRNAs in Glioma Patients.Frontiers in oncology · 2022Article
- Comprehensive pan-cancer analysis and the regulatory mechanism of AURKA, a gene associated with prognosis of ferroptosis of adrenal cortical carcinoma in the tumor micro-environment.Frontiers in genetics · 2022Article
- Identification of Prognostic Signature of Necroptosis-Related lncRNAs and Molecular Subtypes in Glioma.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
The aim of this study is to identify potential biomarkers for early diagnosis of gynecologic cancer in order to improve survival. Cervical cancer (CC) and endometrial cancer (EC) are the most common malignant tumors of gynecologic cancer among women in the world. As the underlying molecular mechanisms in both cervical and endometrial cancer remain unclear, a comprehensive and systematic bioinformatics analysis is required. In our study, gene expression profiles of GSE9750, GES7803, GES63514, GES17025, GES115810, and GES36389 downloaded from Gene Expression Omnibus (GEO) were utilized to analyze differential gene expression between cancer and normal tissues. A total of 78 differentially expressed genes (DEGs) common to CC and EC were identified to perform the functional enrichment analyses, including gene ontology and pathway analysis. KEGG pathway analysis of 78 DEGs indicated that three main types of pathway participate in the mechanism of gynecologic cancer such as drug metabolism, signal transduction, and tumorigenesis and development. Furthermore, 20 diagnostic signatures were confirmed using the least absolute shrink and selection operator (LASSO) regression with 10-fold cross validation. Finally, we used the GEPIA2 online tool to verify the expression of 20 genes selected by the LASSO regression model. Among them, the expression of PAMR1 and SLC24A3 in tumor tissues was downregulated significantly compared to the normal tissue, and found to be statistically significant in survival rates between the CC and EC of patients (
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