A Combined Long Noncoding RNA Signature as a Candidate Prognostic Biomarker for Ovarian Cancer

Li, Hui; Wang, Shuoer; Yao, Qianlan; Liu, Yan; Yang, Jing; Xu, Ling; Yang, Gong

doi:10.3389/fonc.2021.624240

Cited by 2 publications

(2 citation statements)

References 30 publications

(29 reference statements)

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“…The samples were divided into three categories (vascular H, vascular M and vascular L) by the K-MEANS method. Patients’ OS and RS were analyzed using KM methods and plotted using the “survminer” package [ 13 ]. The log-rank test was employed to compare survival differences with p value < 0.05.…”

Section: Methodsmentioning

confidence: 99%

“…LncRNAs were divided into two groups by median: high-risk and low-risk groups. To clarify the specific molecular mechanism of these lncRNAs in HCC, GO and KEGG analyses were performed to compare the difference between high-risk and low-risk groups [ 13 ]. It was considered as significant differences in gene enrichment when | NES |≥ 1, FDR q -value < 0.25 and NOM p value < 0.01.…”

Section: Methodsmentioning

confidence: 99%

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Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma

et al. 2023

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Hepatocellular carcinoma (HCC) is a major cause of cancer-related death due to early metastasis or recurrence. Tumor angiogenesis plays an essential role in the tumorigenesis of HCC. Accumulated studies have validated the crucial role of lncRNAs in tumor angiogenesis. Here, we established an angiogenesis-related multi-lncRNAs risk model based on the machine learning for HCC prognosis prediction. Firstly, a total of 348 differential expression angiogenesis-related lncRNAs were identified by correlation analysis. Then, 20 of these lncRNAs were selected through univariate cox analysis and used for in-depth study of machine learning. After 1,000 random sampling cycles calculating by random forest algorithm, four lncRNAs were found to be highly associated with HCC prognosis, namely LUCAT1, AC010761.1, AC006504.7 and MIR210HG. Subsequently, the results from both the training and validation sets revealed that the four lncRNAs-based risk model was suitable for predicting HCC recurrence. Moreover, the infiltration of macrophages and CD8 T cells were shown to be closely associated with risk score and promotion of immune escape. The reliability of this model was validated by exploring the biological functions of lncRNA MIR210HG in HCC cells. The results showed that MIR210HG silence inhibited HCC growth and migration through upregulating PFKFB4 and SPAG4. Taken together, this angiogenesis-related risk model could serve as a reliable and promising tool to predict the prognosis of HCC.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma

et al. 2023

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Construction of Ovarian Cancer Prognostic Model Based on the Investigation of Ferroptosis-Related lncRNA

Yang

Wang

et al. 2023

Biomolecules

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(1) Background: Ovarian cancer (OV) has the high mortality rate among gynecological cancers worldwide. Inefficient early diagnosis and prognostic prediction of OV leads to poor survival in most patients. OV is associated with ferroptosis, an iron-dependent form of cell death. Ferroptosis, believed to be regulated by long non-coding RNAs (lncRNAs), may have potential applications in anti-cancer treatments. In this study, we aimed to identify ferroptosis-related lncRNA signatures and develop a novel model for predicting OV prognosis. (2) Methods: We downloaded data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, and Gene Expression Omnibus (GEO) databases. Prognostic lncRNAs were screened by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was constructed. The model’s predictive ability was evaluated by Kaplan–Meier (KM) survival analysis and receiver operating characteristic (ROC) curves. The expression levels of these lncRNAs included in the model were examined in normal and OV cell lines using quantitative reverse transcriptase polymerase chain reaction. (3) Results: We constructed an 18 lncRNA prognostic prediction model for OV based on ferroptosis-related lncRNAs from TCGA patient samples. This model was validated using TCGA and GEO patient samples. KM analysis showed that the prognostic model was able to significantly distinguish between high- and low-risk groups, corresponding to worse and better prognoses. Based on the ROC curves, our model shows stronger prediction precision compared with other traditional clinical factors. Immune cell infiltration, immune checkpoint expression levels, and Tumor Immune Dysfunction and Exclusion analyses are also insightful for OV immunotherapy. (4) Conclusions: The prognostic model constructed in this study has potential for improving our understanding of ferroptosis-related lncRNAs and providing a new tool for prognosis and immune response prediction in patients with OV.

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A Combined Long Noncoding RNA Signature as a Candidate Prognostic Biomarker for Ovarian Cancer

Cited by 2 publications

References 30 publications

Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma

Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma

Construction of Ovarian Cancer Prognostic Model Based on the Investigation of Ferroptosis-Related lncRNA

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