A three-long non-coding RNA-expression-based risk score system can better predict both overall and recurrence-free survival in patients with small hepatocellular carcinoma

Gu, Jingxian; Zhang, Xing; Miao, Runchen; Ma, Xiaojie; Xiang, Xiaohong; Fu, Yunong; Chang, Liu; Niu, Wenquan; Qu, Kai

doi:10.18632/aging.101497

Cited by 41 publications

(51 citation statements)

References 42 publications

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“…Several previous studies have also constructed risk scoring systems to predict the prognosis of patients with HCC (24)(25)(26)(27)(28)(29)(30)(31). However, those risk scoring systems were based on DElncRNAs between HCC and normal samples, while the risk scoring systems in the present study are based on the DElncRNAs between HCC Table IX.…”

Section: Discussionmentioning

confidence: 87%

Risk scoring based on expression of long non‑coding RNAs can effectively predict survival in hepatocellular carcinoma patients with or without fibrosis

et al. 2020

Oncol Rep

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Section: Discussionmentioning

confidence: 87%

Risk scoring based on expression of long non‑coding RNAs can effectively predict survival in hepatocellular carcinoma patients with or without fibrosis

et al. 2020

Oncol Rep

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“…Low‐purity gliomas were characterized by intensive local immune phenotypes and correlated with a poor prognosis . Therefore, we applied the ESTIMATE algorithm to predict tumor purity using gene expression profiles and found a significant increase in ESTIMATE scores in the high‐risk group (Figure ), indicating that a greater presence of inflammatory microenvironment components is associated with progressive tumorigenesis …”

Section: Discussionmentioning

confidence: 99%

“…GO analysis and KEGG pathway analysis were carried out in DAVID (http://david.abcc.ncifcrf.gov/home.jsp) for functional annotation of the genes positively and negatively correlated with risk score in the two cohorts . GSEA (http://www.broadinstitute.org/gsea/index.jsp) was carried out to determine whether confirmed gene sets were significantly distinct between the two groups (high risk score vs low risk score) . We evaluated tumor purity of each sample using the ESTIMATE algorithm, which reflects the enrichment of stromal and immune cell gene signatures in a transcriptional profile .…”

Section: Methodsmentioning

confidence: 99%

“…A risk signature was formulated according to the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm. [28][29][30] The penalty parameter λ was chosen based on 10-fold cross-validation within the training set, which produced the minimum mean cross-validated error for the Cox model. Based on this, 30 genes and their regression coefficients were obtained.…”

Section: Bioinformatics Analysismentioning

confidence: 99%

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Amino acid metabolism‐related gene expression‐based risk signature can better predict overall survival for glioma

et al. 2018

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Metabolic reprogramming has been proposed to be a hallmark of cancer. Aside from the glycolytic pathway, the metabolic changes of cancer cells primarily involve amino acid metabolism. However, in glioma, the characteristics of the amino acid metabolism‐related gene set have not been systematically profiled. In the present study, RNA sequencing expression data from 309 patients in the Chinese Glioma Genome Atlas database were included as a training set, while another 550 patients within The Cancer Genome Atlas database were used to validate. Consensus clustering of the 309 samples yielded two robust groups. Compared with Cluster1, Cluster2 correlated with a better clinical outcome. We then developed an amino acid metabolism‐related risk signature for glioma. Our results showed that patients in the high‐risk group had dramatically shorter overall survival than low‐risk counterparts in any subgroup, stratified by isocitrate dehydrogenase and 1p/19q status based on the 2016 World Health Organization classification guidelines. The 30‐gene signature showed better prognostic value than the traditional factors “age” and “grade” by analyzing the receiver operating characteristic curve with areas under curve of 0.966, 0.692, 0.898 and 0.975, 0.677, 0.885 for 3‐ and 5‐year survival, respectively. Moreover, univariate and multivariate analysis showed that the 30‐gene signature was an independent prognostic factor for glioma. Furthermore, Gene Ontology analysis and Gene Set Enrichment Analysis showed that tumors with a high risk score correlated with various aspects of the malignancy of glioma. In summary, we demonstrated a novel amino acid metabolism‐related risk signature for predicting prognosis for glioma.

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“…We used GSE9891 with the largest sample size as training set to screen survival-related lncRNAs through univariate Cox PHR analysis. 18,19 The lncRNAs with p values less than 0.05 were obtained for further analysis. Stage and grade as common tumor phenotype have a close correlation with prognosis.…”

Section: Construction Of Lncrna Expression Through Re-annotationmentioning

confidence: 99%

Identification of pathological grade and prognosis‐associated lncRNA for ovarian cancer

Chen

et al. 2019

J of Cellular Biochemistry

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Ovarian carcinoma (OC) is one of the most common malignant tumors in female genitals. In recent years, the therapeutic effect of OC has been significantly improved through the application of effective chemotherapy regimen. However, the 5‐year survival rate is also lower than 30% due to high rate of relapse. So, it is needed to screen reliable predictive and prognostic markers of OC. Ovarian cancer gene expression data and corresponding clinical data used were downloaded from Gene Expression Omnibus database. Weighted gene expression network analysis (WGCNA) and Cox proportional hazards regression (PHR) were used to screen Pathological Grade and Prognosis‐associated long noncoding RNA (lncRNA). Kaplan‐Meier analysis and receiver operating characteristic curves analysis were performed to evaluate the predictive ability of the selected lncRNA. Gene Ontology (GO) enrichment and Gene Set Enrichment Analysis (GSEA) enrichment analysis methods were used to explore the possible mechanisms of the selected lncRNA affecting the development of OC. Five reliably lncRNAs (LINC00664, LINC00667, LINC01139, LINC01419, and LOC286437) was identified through a series of bioinformatics methods. In testing cohorts, we found that the five lncRNAs in predicting the risk of OC recurrence is robustness, and multivariate Cox PHR analysis indicate that the five lncRNAs is an independent risk factor for OC recurrence. Moreover, GO and GSEA enrichment analysis showed that the five lncRNAs are involved in multiple ovarian cancer occurrence mechanism. In summary, all these findings indicated that the five lncRNAs can effectively predict the risk of recurrence of ovarian cancer.

show abstract

A three-long non-coding RNA-expression-based risk score system can better predict both overall and recurrence-free survival in patients with small hepatocellular carcinoma

Cited by 41 publications

References 42 publications

Risk scoring based on expression of long non‑coding RNAs can effectively predict survival in hepatocellular carcinoma patients with or without fibrosis

Risk scoring based on expression of long non‑coding RNAs can effectively predict survival in hepatocellular carcinoma patients with or without fibrosis

Amino acid metabolism‐related gene expression‐based risk signature can better predict overall survival for glioma

Identification of pathological grade and prognosis‐associated lncRNA for ovarian cancer

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