Identification of an eight-lncRNA prognostic model for breast cancer using WGCNA network analysis and a Cox‑proportional hazards model based on L1-penalized estimation

Liu, Zhenbin; Li, Menghu; Hua, Qi; Li, Yanfang; Wang, Gang

doi:10.3892/ijmm.2019.4303

Cited by 35 publications

(31 citation statements)

References 42 publications

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“…In order to predict the DEmRNA co-expressed lncRNA connected to survival, we performed the single factor Cox analysis by R package "survival" [50], risk model was calculated as previously reported [51]. According to the best risk model obtained by multivariate Cox analysis, the survival score was performed, and the average number of risk scores of each sample of TCGA-GBM data was also calculated.…”

Section: Single and Multivariate Factor Cox Analysis Roc And Survivamentioning

confidence: 99%

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Identification of HMG-box family establishes the significance of SOX6 in the malignant progression of glioblastoma

Jiang

Yang

Chen

et al. 2020

Aging

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Glioblastoma multiforme (GBM) is the most malignant neuroepithelial primary brain tumor and its mean survival time is 15 months after diagnosis. This study undertook to investigate the genome-wide and transcriptome-wide analyses of human high mobility group box (HMG-box) TF (transcript factor) families / HOX, TOX, FOX, HMG and SOX gene families, and their relationships to GBM. According to the TCGA-GBM profile analysis, differentially expressed HOX, FOX, HMG and SOX gene families (62 DEmRNA) were found in this study. We also analyzed DEmRNA (HMG-box related genes) co-expressed eight DElncRNA in GBM, and constructed a ceRNA network analysis as well. We constructed 50 DElncRNA-DEmiRNA-DEmRNA (HMG-box related genes) pairs between GBM and normal tissues. Then, risk genes SOX6 and SOX21 expression were correlated with immune infiltration levels in GBM. SOX6 also had a strong association with MAPT, GSK3B, FYN and DPYSL4, suggesting that they might be functional members in GBM.

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Section: Single and Multivariate Factor Cox Analysis Roc And Survivamentioning

confidence: 99%

“…Gene structures were drawn by GSDS v2.0 [50]. Motifs reported on SOX protein data via MEME v5.0.5 [51]. SOX secondary structure was built by Secondary structure by NPS@: Network Protein Sequence Analysis online service [52].…”

Section: Example: Genome-wide Retrieval and Identification Of Sox Genmentioning

confidence: 99%

Identification of HMG-box family establishes the significance of SOX6 in the malignant progression of glioblastoma

Jiang

Yang

Chen

et al. 2020

Aging

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“…Distinguished from other analysis method, WGCNA hierarchical clustering methods focused on the whole genome information instead of previous selected genes to overview of the signature of gene networks in phenotypes which can avoid bias and subject judgement 12 . Weighted Gene Co‐expression Network Analysis has been widely used in the study of multiple diseases 13–15 . By constructing a co‐expression network of genes and an identification module, WGCNA can investigate hub genes closely related to clinical phenotypes, which will provide us a beacon of hope for discovering new molecular biomarkers and therapeutic targets in GBM.…”

Section: Introductionmentioning

confidence: 99%

Construction of co‐expression modules related to survival by WGCNA and identification of potential prognostic biomarkers in glioblastoma

Zhou

Guo

Liu

et al. 2021

J Cellular Molecular Medi

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Glioblastoma (GBM) is a malignant brain tumour with poor prognosis. The potential pathogenesis and therapeutic target are still need to be explored. Herein, TCGA expression profile data and clinical information were downloaded, and the WGCNA was conducted. Hub genes which closely related to poor prognosis of GBM were obtained. Further, the relationship between the genes of interest and prognosis of GBM, and immune microenvironment were analysed. Patients from TCGA were divided into high‐ and low‐risk group. WGCNA was applied to the high‐ and low‐risk group and the black module with the lowest preservation was identified which could distinguish the prognosis level of these two groups. The top 10 hub genes which were closely related to poor prognosis of patients were obtained. GO analysis showed the biological process of these genes mainly enriched in: Cell cycle, Progesterone‐mediated oocyte maturation and Oocyte meiosis. CDCA5 and CDCA8 were screened out as the genes of interest. We found that their expression levels were closely related to overall survival. The difference analysis resulted from the TCGA database proved both CDCA5 and CDCA8 were highly expressed in GBM. After transfection of U87‐MG cells with small interfering RNA, it revealed that knockdown of the CDCA5 and CDCA8 could influence the biological behaviours of proliferation, clonogenicity and apoptosis of GBM cells. Then, single‐gene analysis was performed. CDCA5 and CDCA8 both had good correlations with genes that regulate cell cycle in the p53 signalling pathway. Moreover, it revealed that high amplification of CDCA5 was correlated with CD8 + T cells while CDCA8 with CD4 + T cells in GBM. These results might provide new molecular targets and intervention strategy for GBM.

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“…Since that study, various models have been constructed to predict prognoses in cancer patients. [46][47][48] Nevertheless, there have been few prediction models combining lncRNA information with CRC clinical features. In our study, we identified a prognostic model with two CRC lncRNAs, and we constructed a nomogram and risk classification system.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis

Shi

Guo

Yang

et al. 2022

Cancer Biotherapy and Radiopharmaceuticals

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Background: Knowledge about the prognostic role of long noncoding RNA (lncRNA) in colorectal cancer (CRC) is limited. Therefore, we constructed a lncRNA-related prognostic model based on data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Materials and Methods: CRC transcriptome and clinical data were downloaded from the GSE20916 dataset and the TCGA database, respectively. R software was used for data processing and analysis. The differential lncRNA expression within the two datasets was first screened, and then intersections were measured. Cox regression and the Kaplan-Meier method were used to evaluate the effects of various factors on prognosis. The area under the curve (AUC) of the receiver operating characteristic curve and a nomogram based on multivariate Cox analysis were used to estimate the prognostic value of the lncRNA-related model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to elucidate the significantly involved biological functions and pathways. Results: A total of 11 lncRNAs were crossed. The univariate Cox analysis screened out two lncRNAs, which were analyzed in the multivariate Cox analysis. A nomogram based on the two lncRNAs and other clinicopathological risk factors was constructed. The AUC of the nomogram was 0.56 at 3 years and 0.71 at 5 years. The 3-year nomogram model was compared with the ideal model, which showed that some indices of the 3-year model were consistent with the ideal model, suggesting that our model was highly accurate. The GO and KEGG enrichment analyses showed that positive regulation of secretion by cells, positive regulation of secretion, positive regulation of exocytosis, endocytosis, and the calcium signaling pathway were differentially enriched in the two-lncRNA-associated phenotype. Conclusions: A two-lncRNA prognostic model of CRC was constructed by bioinformatics analysis. The model had moderate prediction accuracy, LncRNA BBOX1-AS1 and lncRNA FOXP4-AS1 were identified as prognostic biomarkers.

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Identification of an eight-lncRNA prognostic model for breast cancer using WGCNA network analysis and a Cox‑proportional hazards model based on L1-penalized estimation

Cited by 35 publications

References 42 publications

Identification of HMG-box family establishes the significance of SOX6 in the malignant progression of glioblastoma

Identification of HMG-box family establishes the significance of SOX6 in the malignant progression of glioblastoma

Construction of co‐expression modules related to survival by WGCNA and identification of potential prognostic biomarkers in glioblastoma

Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis

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