A new risk score based on twelve hepatocellular carcinoma-specific gene expression can predict the patients’ prognosis

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

doi:10.18632/aging.101563

Cited by 25 publications

(22 citation statements)

References 74 publications

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“…The rapid development of bioinformatics has produced numerous tools for the identification of biomarkers. 11 Weighted gene co-expression network analysis (WGCNA) is an effective tool that can be used to identify relevant patterns of the genes to determine relevant modules and key genes for specific diseases. 12 This algorithm has been extensively used to identify biomarkers at the transcriptional level.…”

Section: Introductionmentioning

confidence: 99%

Identification of key genes and novel immune infiltration-associated biomarkers of sepsis

et al. 2020

Innate Immun

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Sepsis is the major cause of mortality in the intensive care unit. The aim of this study was to identify the key prognostic biomarkers of abnormal expression and immune infiltration in sepsis. In this study, a total of 36 differentially expressed genes were identified to be mainly involved in a number of immune-related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The hub genes ( MMP9 and C3AR1) were significantly related to the prognosis of sepsis patients. The immune infiltration analysis indicated a significant difference in the relative cell content of naive B cells, follicular Th cells, activated NK cells, eosinophils, neutrophils and monocytes between sepsis and normal controls. Weighted gene co-expression network analysis and a de-convolution algorithm that quantifies the cellular composition of immune cells were used to analyse the sepsis expression data from the Gene Expression Omnibus database and to identify modules related to differential immune cells. CEBPB is the key immune-related gene that may be involved in sepsis. Gene set enrichment analysis revealed that CEBPB is involved in the processes of T cell selection, B cell–mediated immunity, NK cell activation and pathways of T cells, B cells and NK cells. Therefore, CEBPB may play a key role in the biological and immunological processes of sepsis.

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Section: Introductionmentioning

confidence: 99%

Identification of key genes and novel immune infiltration-associated biomarkers of sepsis

et al. 2020

Innate Immun

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“…In this study, we identified a molecular subtyping system of Asian HCC which could predict postoperative survival and recurrence. Unlike previous studies using only few genes 9,10 , our molecular signature was comprised of a large panel of 185 HCC-characteristic prognostic genes screened by a comprehensive analysis of the whole gene set profiles and follow-up information of each participant. This method made the identified molecular subtype more representative of the intrinsic heterogeneity of the cancer on the basis of its clinical significance.…”

Section: Discussionmentioning

confidence: 99%

Identification of a molecular subtyping system associated with the prognosis of Asian hepatocellular carcinoma patients receiving liver resection

Wang

et al. 2019

Sci Rep

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Hepatocellular carcinoma (HCC) remains a severe health issue worldwide, especially in Asia. To date, molecular classifications proposed for the overall survival (OS) or recurrence-free survival (RFS) prediction of Asian HCC patients after hepatectomy are quite few and limited in clinical practice. Here, we established a molecular subtyping system for Asian HCC to facilitate prognosis evaluation. Firstly, differentially expressed genes (DEGs) (FDR [false discovery rate] <0.05) between different types of liver cancer and non-tumor tissue were screened. Among the DEGs solely between HCC and non-tumor samples, 185 genes simultaneously significantly associated with the OS and RFS were identified as HCC-characteristic genes. The molecular subtypes were developed based on the expression profiles of the 185 genes in the training dataset (TCGA [The Cancer Genome Atlas] dataset) using non-negative matrix factorization (NMF) clustering method. Patients were then classified into Subtype1 and Subtype2 groups denoting unfavorable and favorable clinical outcome respectively. The robustness and effectiveness of the molecular subtype was confirmed in another independent dataset (GSE14520) by the same clustering approach and Kaplan-Meier analyses. Moreover, functional prediction analysis revealed that the identified molecular signature was involved in chemotaxis, apoptosis and cell development associated pathways. Besides, the molecular signature was closely related to the clinical characteristics including TNM stage, preoperative alpha-fetoprotein (AFP) level and TP53 mutation. Furthermore, integration of the molecular subtype and TNM stage was demonstrated to improve risk stratification. Taken together, our molecular subtyping system exhibited great utility and potential in prognosis prediction and therapeutic decision making of Asian HCC patients.

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“…Based on the multivariate Cox regression analysis results, we developed a prognostic risk score formula. The optimal prognostic mRNA expression levels were multiplied by the relative regression coefficients as follows [ 18 – 20 ]: …”

Section: Methodsmentioning

confidence: 99%

Identification and validation of a novel glycolysis-related gene signature for predicting the prognosis in ovarian cancer

Liu

Liang

et al. 2021

Cancer Cell Int

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Background Ovarian cancer (OC) is the most lethal gynaecological tumor. Changes in glycolysis have been proven to play an important role in OC progression. We aimed to identify a novel glycolysis-related gene signature to better predict the prognosis of patients with OC. Methods mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Genotype Tissue Expression (GTEx) database. The “limma” R package was used to identify glycolysis-related differentially expressed genes (DEGs). Then, a multivariate Cox proportional regression model and survival analysis were used to develop a glycolysis-related gene signature. Furthermore, the TCGA training set was divided into two internal test sets for validation, while the ICGC dataset was used as an external test set. A nomogram was constructed in the training set, and the relative proportions of 22 types of tumor-infiltrating immune cells were evaluated using the “CIBERSORT” R package. The enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined by single-sample gene set enrichment analysis (ssGSEA) with the “GSVA” R package. Finally, the expression and function of the unreported signature genes ISG20 and SEH1L were explored using immunohistochemistry, western blotting, qRT-PCR, proliferation, migration, invasion and xenograft tumor assays. Results A five-gene signature comprising ANGPTL4, PYGB, ISG20, SEH1L and IRS2 was constructed. This signature could predict prognosis independent of clinical factors. A nomogram incorporating the signature and three clinical features was constructed, and the calibration plot suggested that the nomogram could accurately predict the survival rate. According to ssGSEA, the signature was associated with KEGG pathways related to axon guidance, mTOR signalling, tight junctions, etc. The proportions of tumor-infiltrating immune cells differed significantly between the high-risk group and the low-risk group. The expression levels of ISG20 and SEH1L were lower in tumor tissues than in normal tissues. Overexpression of ISG20 or SEH1L suppressed the proliferation, migration and invasion of Caov3 cells in vitro and the growth of xenograft tumors in vivo. Conclusion Five glycolysis-related genes were identified and incorporated into a novel risk signature that can effectively assess the prognosis and guide the treatment of OC patients.

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A new risk score based on twelve hepatocellular carcinoma-specific gene expression can predict the patients’ prognosis

Cited by 25 publications

References 74 publications

Identification of key genes and novel immune infiltration-associated biomarkers of sepsis

Identification of key genes and novel immune infiltration-associated biomarkers of sepsis

Identification of a molecular subtyping system associated with the prognosis of Asian hepatocellular carcinoma patients receiving liver resection

Identification and validation of a novel glycolysis-related gene signature for predicting the prognosis in ovarian cancer

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