Uterine corpus endometrial carcinoma (UCEC) is frequently diagnosed among women worldwide. However, there are different prognostic outcomes because of heterogeneity. Thus, the aim of the current study was to identify a gene signature that can predict the prognosis of patients with UCEC. UCEC gene expression profiles were first downloaded from the The Cancer Genome Atlas (TCGA) database. After data processing and forward screening, 11 390 key genes were selected. The UCEC samples were randomly divided into training and testing sets. In total, 996 genes with prognostic value were then examined by univariate Cox survival analysis with a P‐value <0.01 in the training set. Next, using robust likelihood‐based survival modeling, we developed a six‐gene signature (CTSW, PCSK4, LRRC8D, TNFRSF18, IHH, and CDKN2A) with a prognostic function in UCEC. A prognostic risk score system was developed by multivariate Cox proportional hazard regression based on this six‐gene signature. According to the Kaplan‐Meier curve, patients in the high‐risk group had significantly poorer overall survival (OS) outcomes than those in the low‐risk group (log‐rank test P‐value <0.0001). This signature was further validated in the testing dataset and the entire TCGA dataset. In conclusion, we conducted an integrated study to develop a six‐gene signature for the prognostic prediction of patients with UCEC. Our findings may provide novel biomarkers for prognosis and have significant implications in the understanding of therapeutic targets for UCEC.

BackgroundNomogram, a visual clinical predictive model, provides a scientific basis for clinical decision making. Herein, we investigated 20 years of nomogram research responses, focusing on current and future trends and analytical challenges.MethodsWe mined data of scientific literature from the Core Collection of Web of Science, searching for the original articles with title “Nomogram*/Parton Table*/Parton Nomogram*”, published within January 1st, 2000 to December 30th, 2021. Data records were validated using HistCite Version and analyzed with a transformable statistical method, the Bibliometrix 3.0 package of R Studio.ResultsIn total, 4,176 original articles written by 19,158 authors were included from 915 sources. Annually, Nomogram publications are continually produced, which have rapidly grown since 2018. China published the most articles; however, its total citations ranked second after the United States. Both total citations and average article citations in the United States rank first globally, and a high degree of cooperation exists between countries. Frontiers in Oncology published the most papers (238); this number has grown rapidly since 2019. Journal of Urology had the highest H-index, with an average increase in publications over the past 20 years. Most research topics were tumor-related, among which tumor risk prediction and prognostic evaluation were the main contents. Research on prognostic assessment is more published and advanced, while risk prediction and diagnosis have good developmental prospects. Furthermore, nomogram of the urinary system has been highly developed. Following advancements in nomogram modeling, it has recently been applied to non-oncological subjects.ConclusionThis bibliometric analysis provides a comprehensive overview of the current nomogram status, which could enable better understanding of its development over the years, and provide global researchers a comprehensive analysis and structured information to help identify hot spots and gaps in future research.

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Hypoxia contributes to the maintenance of stem-like cells in glioblastoma (GBM), and activates vascular mimicry and tumor resistance to anti-angiogenesis treatments. The present study examined the expression patterns and biological significance of hypoxia-inducible protein 2 (HIG2, also known as HILPDA) in GBM. HIG2 was highly expressed in gliomas and was correlated with tumor grade, and high HIG2 expression independently predicted poor GBM patient prognosis. HIG2 was upregulated during hypoxia and by hypoxia mimics, and HIG2 knockdown in GBM cells inhibited cell proliferation and invasion. HIF1α bound to the HIG2 promoter and increased its expression in GBM cells, and HIG2 upregulated HIF1α expression. Reconstruction of a HIG2-related molecular network using bioinformatics methods revealed that HIG2 is closely correlated with angiogenesis genes, such as VEGFA, in GBM. HIG2 levels positively correlated with VEGFA in GBM samples. In addition, treatment of transplanted xenograft nude mice with bevacizumab (anti-angiogenesis therapy) resulted in HIG2 upregulation at late stages. We conclude that HIG2 is overexpressed in GBM and upregulated by hypoxia, and is a potential novel therapeutic target. HIG2 overexpression is an independent prognostic indicator and may promote tumor resistance to anti-angiogenesis treatments.