Prognostic Implication of a Novel Metabolism-Related Gene Signature in Hepatocellular Carcinoma

Yuan, Chaoyan; Yuan, Mengqin; Ming-qian, Chen; Ouyang, Jinhua; Tan, Wei; Dai, Fangfang; Yang, Dongyong; Liu, Shiyi; Zheng, Y.; Zhou, Chunqin; Cheng, Yanxiang

doi:10.3389/fonc.2021.666199

Cited by 22 publications

(16 citation statements)

References 45 publications

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“…The RNA sequence data and related clinical information of 374 liver cancer patients (TCGA-LIHC) were acquired from the TCGA website ( https://portal.gdc.cancer.gov/repository ). The gene expression data were normalized by scale method using the “limma” package ( Yuan C. et al, 2021 ). After excluding the missing clinical information of patients, 370 HCC patients were randomly separated into the training and the test groups by the “caret” package.…”

Section: Methodsmentioning

confidence: 99%

Identification and Validation of Pyroptosis-Related Gene Signature to Predict Prognosis and Reveal Immune Infiltration in Hepatocellular Carcinoma

Song

2021

Front. Cell Dev. Biol.

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Background: Hepatocellular carcinoma (HCC) is characterized by a poor prognosis and accounts for the fourth common cause of cancer-related deaths. Recently, pyroptosis has been revealed to be involved in the progression of multiple cancers. However, the role of pyroptosis in the HCC prognosis remains elusive.Methods: The clinical information and RNA-seq data of the HCC patients were collected from the TCGA-LIHC datasets, and the differential pyroptosis-related genes (PRG) were firstly explored. The univariate Cox regression and consensus clustering were applied to recognize the HCC subtypes. The prognostic PRGs were then submitted to the LASSO regression analysis to build a prognostic model in the TCGA training cohort. We further evaluated the predictive model in the TCGA test cohort and ICGC validation cohort (LIRI-JP). The accuracy of prediction was validated using the Kaplan—Meier (K-M) and receiver operating characteristic (ROC) analyses. The single-sample gene set enrichment analysis (ssGSEA) was used to determine the differential immune cell infiltrations and related pathways. Finally, the expression of the prognostic genes was validated by qRT-PCR in vivo and in vitro.Results: We identified a total of 26 differential PRGs, among which three PRGs comprising GSDME, GPX4, and SCAF11 were subsequently chosen for constructing a prognostic model. This model significantly distinguished the HCC patients with different survival years in the TCGA training, test, and ICGC validation cohorts. The risk score of this model was confirmed as an independent prognostic factor. A nomogram was generated indicating the survival years for each HCC patient. The ssGSEA demonstrated several tumor-infiltrating immune cells to be remarkably associated with the risk scores. The qRT-PCR results also showed the apparent dysregulation of PRGs in HCC. Finally, the drug sensitivity was analyzed, indicating that Lenvatinib might impact the progression of HCC via targeting GSDME, which was also validated in human Huh7 cells.Conclusion: The PRG signature comprised of GSDME, GPX4, and SCAF11 can serve as an independent prognostic factor for HCC patients, which would provide further evidence for more clinical and functional studies.

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Section: Methodsmentioning

confidence: 99%

Identification and Validation of Pyroptosis-Related Gene Signature to Predict Prognosis and Reveal Immune Infiltration in Hepatocellular Carcinoma

Song

2021

Front. Cell Dev. Biol.

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“…The level of immune cell infiltration in the LUAD samples was quantified by inferencing the infiltrating cells in the tumor microenvironment (TME). We applied integrated bioinformatics methods, including MCPcounter, xCell, quanTIseq, CIBERSORTx, single-sample gene set enrichment analysis (GSEA) to evaluate differences of immune status among different molecular subtypes ( Finotello et al, 2019 ; Cai et al, 2021 ; Yuan et al, 2021 ). Furthermore, the ‘‘pRRophetic’’ R package was used to predict the chemotherapy response of each sample based on Genomics of Drug Sensitivity in Cancer (GDSC) 3 , and the correlation between molecular subtypes and immune checkpoint genes was analyzed ( Geeleher et al, 2014 ; Kuang et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

Liu

Zhao

et al. 2021

Front. Cell Dev. Biol.

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Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.

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“…Subsequently, 5 genes were selected, including TSPYL5 , KLF9 , GYPC , VTCN1 , and PGR . The risk score for each patient was performed as our previous article [ 24 ]: risk score = b gene (1) × E gene (1) + β gene (2) × E gene (2) + ⋯+ β gene ( n ) × E gene ( n ).…”

Section: Methodsmentioning

confidence: 99%

Integrated Bioinformatic Analysis of DNA Methylation and Immune Infiltration in Endometrial Cancer

Dai

Deng

et al. 2022

BioMed Research International

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Background. Endometrial cancer greatly threatens the health of female. Emerging evidences have demonstrated that DNA methylation and immune infiltration are involved in the occurrence and development of endometrial cancer. However, the mechanism and prognostic biomarkers of endometrial cancer are still unclear. In this study, we assess DNA methylation and immune infiltration via bioinformatic analysis. Methods. The latest RNA-Seq, DNA methylation data, and clinical data related to endometrial cancer were downloaded from the UCSC Xena dataset. The methylation-driven genes were selected, and then the risk score was obtained using “MethylMix” and “corrplot” R packages. The connection between methylated genes and the expression of screened driven genes were explored using “survminer” and “beeswarm” packages, respectively. Finally, the role of VTCN1in immune infiltration was analyzed using “CIBERSORT” package. Results. In this study, 179 upregulated genes, and 311 downregulated genes were identified and found to be related to extracellular matrix organization, cell–cell junctions, and cell adhesion molecular binding. The methylation-driven gene VTCN1 was selected, and patients classified to the hypomethylation and high expression group displayed poor prognosis. The VTCN1 gene exhibited highest correlation coefficient between methylation and expression. More importantly, the hypomethylation of promoter of VTCN1 led to its high expression, thereby induce tumor development by inhibiting CD8+ T cell infiltration. Conclusions. Overall, our study was the first to reveal the mechanism of endometrial cancer by assessing DNA methylation and immune infiltration via integrated bioinformatic analysis. In addition, we found a pivotal prognostic biomarker for the disease. Our study provides potential targets for the diagnosis and prognosis of endometrial cancer in the future.

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Prognostic Implication of a Novel Metabolism-Related Gene Signature in Hepatocellular Carcinoma

Cited by 22 publications

References 45 publications

Identification and Validation of Pyroptosis-Related Gene Signature to Predict Prognosis and Reveal Immune Infiltration in Hepatocellular Carcinoma

Identification and Validation of Pyroptosis-Related Gene Signature to Predict Prognosis and Reveal Immune Infiltration in Hepatocellular Carcinoma

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

Integrated Bioinformatic Analysis of DNA Methylation and Immune Infiltration in Endometrial Cancer

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