ArticleMedical science monitor : international medical journal of experimental and clinical research2018
Comprehensive Bioinformatic Analysis Genes Associated to the Prognosis of Liposarcoma.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Artificial intelligence applied to musculoskeletal oncology: a systematic review.Skeletal radiology · 2022Pooled it
- [NIP7 upregulates the expression of ubiquitin-conjugating enzyme E2 C to promote tumor growth in anaplastic thyroid cancer].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2025Article
- Comprehensive Analysis of a Six-Gene Signature Predicting Survival and Immune Infiltration of Liposarcoma Patients and Deciphering Its Therapeutic Significance.International journal of molecular sciences · 2024Article
- Exploration and analysis of differentially expressed genes in Epstein-Barr virus negative and positive plasmablastic lymphoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2023Article
- Upregulation of GTPBP4 Promotes the Proliferation of Liver Cancer Cells.Journal of oncology · 2021Article
- Development and validation of a novel prognostic model for long-term overall survival in liposarcoma patients: a population-based study.The Journal of international medical research · 2020Article
- Prognostic Value of a Three-DNA Methylation Biomarker in Patients with Soft Tissue Sarcoma.Journal of oncology · 2020Article
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Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND Liposarcoma is the most common type of soft tissue sarcoma, but its molecular mechanism is poorly defined. This study aimed to identify genes crucial to the pathogenesis of liposarcoma and to explore their functions, related pathways, and prognostic value. MATERIAL AND METHODS Differentially expressed genes (DEGs) in the GSE59568 dataset were screened. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to investigate the DEGs at the functional level. Protein-protein interaction (PPI) networks and module analysis were applied to identify hub genes from among the DEGs. The GSE30929 dataset was used to validate the relationship between hub genes and the distant recurrence-free survival (DRFS) of liposarcoma patients using Cox model analysis. RESULTS A total of 1111 DEGs were identified. GO and KEGG pathway analysis indicated that the DEGs were mainly associated with lipopolysaccharides and pathways in cancer. The PPI network and module analysis identified 10 hub genes from the DEG network. The Cox model identified 3 genes (NIP7, RPL10L, and MCM2) significantly associated with DRFS. The risk score calculated by the Cox model of the NIP7-RPL10L-MCM2 signature could largely predict the 1-, 3-, and 5-year DRFS of liposarcoma patients, and the prognostic value was even higher for subtypes of liposarcoma. CONCLUSIONS This study identified genes that might play critical roles in liposarcoma pathogenesis as well as a 3-gene-based signature that could be used as a candidate prognostic biomarker for patients with liposarcoma.
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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.