Prognostic value of lncRNA HOTAIR in colorectal cancer : a meta-analysis

Chen, Shuangqian; Zhang, Chunxiao; Mao, Feng

doi:10.1515/med-2020-0012

Cited by 22 publications

(10 citation statements)

References 43 publications

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“…36 Some lncRNAs, such as lncRNA HOTAIR, lncRNA PVT1, circulating lncRNA DANCER, and others, can act as prognostic factors in CRC. 25,37,38 Although there have been many studies on the associations between lncRNAs and CRC, the roles and mechanisms of lncRNAs in CRC and their clinical applications still need extensive exploration. Recently, studies have reported different genetic models for predicting human cancer.…”

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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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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“…Through further processing of deleting missing items in the clinicopathological data, a total of 392 cases of TCGA-CRC and 550 cases of GSE39582 were obtained ( Table 1 ). The metabolism-related genes are obtained from the GSEA website: First, download all KEGG pathway related genes (KEGG gene sets, gene symbols option) from the GSEA website ( https://www.gsea-msigdb.org/gsea/downloads.jsp ), which contains a total of 186 KEGG pathways (41 pathways are metabolically related, and 145 pathways are non-metabolically related); then extract the metabolic pathways All genes in are metabolic-related genes ( S1 Table ), which are used as metabolic gene expression data sets for subsequent analysis [ 16 – 18 ].…”

Section: Methodsmentioning

confidence: 99%

The role of multiple metabolic genes in predicting the overall survival of colorectal cancer: A study based on TCGA and GEO databases

Shi

et al. 2021

PLoS ONE

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The recent advances in gene chip technology have led to the identification of multiple metabolism-related genes that are closely associated with colorectal cancer (CRC). Nevertheless, none of these genes could accurately diagnose or predict CRC. The prognosis of CRC has been made by previous prognostic models constructed by using multiple genes, however, the predictive function of multi-gene prognostic models using metabolic genes for the CRC prognosis remains unexplored. In this study, we used the TCGA-CRC cohort as the test dataset and the GSE39582 cohort as the experimental dataset. Firstly, we constructed a prognostic model using metabolic genes from the TCGA-CRC cohort, which were also associated with CRC prognosis. We analyzed the advantages of the prognostic model in the prognosis of CRC and its regulatory mechanism of the genes associated with the model. Secondly, the outcome of the TCGA-CRC cohort analysis was validated using the GSE39582 cohort. We found that the prognostic model can be employed as an independent prognostic risk factor for estimating the CRC survival rate. Besides, compared with traditional clinical pathology, it can precisely predict CRC prognosis as well. The high-risk group of the prognostic model showed a substantially lower survival rate as compared to the low-risk group. In addition, gene enrichment analysis of metabolic genes showed that genes in the prognostic model are enriched in metabolism and cancer-related pathways, which may explain its underlying mechanism. Our study identified a novel metabolic profile containing 11 genes for prognostic prediction of CRC. The prognostic model may unravel the imbalanced metabolic microenvironment, and it might promote the development of biomarkers for predicting treatment response and streamlining metabolic therapy in CRC.

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“…Overall, compelling evidence points to a potential role of HOTAIR as a biomarker for CRC prediction and diagnosis, due to its detection in bodily fluids [ 99 , 100 ], prognosis [ 101 ], and as a potential therapeutic target for the development of novel strategies to target its overexpression [ 94 , 98 ]. So far, a patent has been filed for HOTAIR in gastric cancer diagnosis [ 12 ], encouraging the development of novel applications.…”

Section: Validated Lncrna Candidates In Crcmentioning

confidence: 99%

Discovery and Validation of Clinically Relevant Long Non-Coding RNAs in Colorectal Cancer

Snyder

Iraola-Guzmán

Saus

et al. 2022

Cancers

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Colorectal cancer (CRC) is the third most prevalent cancer worldwide, with nearly two million newly diagnosed cases each year. The survival of patients with CRC greatly depends on the cancer stage at the time of diagnosis, with worse prognosis for more advanced cases. Consequently, considerable effort has been directed towards improving population screening programs for early diagnosis and identifying prognostic markers that can better inform treatment strategies. In recent years, long non-coding RNAs (lncRNAs) have been recognized as promising molecules, with diagnostic and prognostic potential in many cancers, including CRC. Although large-scale genome and transcriptome sequencing surveys have identified many lncRNAs that are altered in CRC, most of their roles in disease onset and progression remain poorly understood. Here, we critically review the variety of detection methods and types of supporting evidence for the involvement of lncRNAs in CRC. In addition, we provide a reference catalog that features the most clinically relevant lncRNAs in CRC. These lncRNAs were selected based on recent studies sorted by stringent criteria for both supporting experimental evidence and reproducibility.

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Prognostic value of lncRNA HOTAIR in colorectal cancer : a meta-analysis

Cited by 22 publications

References 43 publications

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

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

The role of multiple metabolic genes in predicting the overall survival of colorectal cancer: A study based on TCGA and GEO databases

Discovery and Validation of Clinically Relevant Long Non-Coding RNAs in Colorectal Cancer

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