8DEstablishment and validation of a hypoxia-related signature predicting prognosis in hepatocellular carcinoma

Cai, Congbo; Yang, Lei; Zhou, Kena

doi:10.1186/s12876-021-02057-0

Cited by 7 publications

(3 citation statements)

References 49 publications

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“…The outcomes of HCC patients are closely correlated to the expression of a few genes in the tumor tissue, tumor stage, tumor size, serum indicators, and liver function. To predict the prognosis of HCC patients, several studies have concentrated on the possible relevance of gene signatures based on aberrant messenger ribonucleic acid (29)(30)(31). Using data from TCGA as the training set and data from ICGC as the validation set, we combined the gene expression data for HCC in this study.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of an autophagy-related prognostic model for predicting survival based on TCGA and ICGC database in hepatocellular carcinoma patients

An¹,

Du²,

Wang³

et al. 2022

J Gastrointest Oncol

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Background: There is accumulating evidence that autophagic activity is crucial to the development of hepatocellular carcinoma (HCC). Thus, we sought to develop a predictive model based on autophagy-related genes (ARGs) to forecast the prognosis of HCC patients.Methods: Based on expression data from The Cancer Genome Atlas (TCGA) and ARGs from Human Autophagy Database (HADb), the differentially expressed ARGs were screened. The prognosis-related ARGs were identified using a univariate Cox regression analysis. Using multivariate Cox regression analysis, a prognostic model was developed. To assess the predictive value of the model, receiver operating characteristic (ROC) curve, Kaplan-Meier curve, and multivariable Cox regression analyses were conducted.A data cohort gathered independently from the International Cancer Genome Consortium (ICGC) database further verified the model's predictive accuracy. The immune landscape was generated using the TIMER and CIBERSORT algorithms. Finally, the correlation between the prognostic signature and gene mutation status was analyzed by employing "maftools" package.Results: We identified a novel prediction model based on the ARGs of PLD1 and SLC36A1 with significant prognostic values for HCC in both univariate and multivariate Cox regression analysis, and patients were classified into high-or low-risk groups based on their risk scores. High-risk patients had significantly shorter overall survival (OS) times than low-risk patients (P=5e-4). According to the ROC curve analysis, the risk score had a higher predictive value than the other clinical characteristics. Prognostic nomograms were also performed to visualize the relationship between individual predictors and survival rates in patients with HCC. Further, an external independent cohort of ICGC patients provided additional confirmation of the predictive efficacy of the model. We subsequently analyzed the differential immune densities of the two groups and discovered that various immune cells, including naïve B cells, resting memory cluster of differentiation (CD)4 T cells, regulatory T cells, M2 macrophages, and neutrophils, had considerably larger infiltrating densities in the high-risk group than the low-risk group. Conclusions:We established a robust autophagy-related risk model having a certain prediction accuracy for predicting the prognosis of HCC patients. Our findings will contribute to the definition of prognosis and establishment of personalized treatment interventions for HCC patients.

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Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of an autophagy-related prognostic model for predicting survival based on TCGA and ICGC database in hepatocellular carcinoma patients

An¹,

Du²,

Wang³

et al. 2022

J Gastrointest Oncol

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“…The construction of a prognostic model based on the expression of multiple genes in PCA is more meaningful for predicting the prognosis of PCA patients. Previous studies have explored the predictive role of hypoxia-related prognostic models in the prognosis of hepatocellular carcinoma ( 13 ), glioma ( 14 ), and breast cancer ( 15 ) patients. This study aimed to develop a hypoxia-related prognostic model to predict the prognosis of PCA patients and assess the TME.…”

Section: Introductionmentioning

confidence: 99%

A bioinformatics analysis and an experimental validation of the hypoxia-related prognostic model

Zhou¹,

Zhang²,

Ni³

et al. 2023

J Gastrointest Oncol

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Background Hypoxia plays an important role in the development of pancreatic cancer (PCA). However, there is few research on the application of hypoxia molecules in predicting the prognosis of PCA. We aimed to establish a prognostic model based on hypoxia-related genes (HRGs) for PCA to discover new biomarkers, and to reveal the potential of this prognostic model for evaluating the tumor microenvironment (TME). Methods Univariate Cox regression analysis was used to identify HRGs associated with overall survival (OS) of PCA samples. A hypoxia-related prognostic model was established based on least absolute shrinkage and selection operator (LASSO) regression analysis in The Cancer Genome Atlas (TCGA) cohort. The model was validated in the Gene Expression Omnibus (GEO) datasets. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to estimate the infiltration of immune cells. A wound healing assay and transwell invasion assay were used to explore the biological functions of target genes in PCA. Results A total of 18 HRGs were differentially expressed between the tumor and normal pancreatic tissue, 4 ( BHLHE40 , ENO1 , SDC4 , and TGM2 ) of which were selected to construct a prognostic model. According to this model, patients in the high-risk group had a less favorable prognosis. Furthermore, the proportion of M0 macrophages was significantly higher in high-risk tissue-type patients, whereas naïve B cells, plasma cells, CD8 + T cells, and activated CD4 + memory T cells were significantly lower. The expression of BHLHE40 in PCA cells was significantly up-regulated under hypoxic conditions. Moreover, BHLHE40 was shown to regulate the transcription and expression of the downstream target gene TLR3 . The wound healing assay and transwell invasion assay indicated that BHLHE40 mediated PCA cell migration and invasion by targeting the downstream gene TLR3 . Conclusions The hypoxia-related prognostic model established by the expression pattern of 4 HRGs can be used to predict the prognosis and assess the TME of PCA patients. Mechanically, activation of the BHLHE40/TLR3 axis is responsible for the promoted invasion and migration of PCA cells in a hypoxic environment.

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“…Dao et al identified and validated a reliable hypoxia-related survival score in IDH-mutant glioma stem cells based on five HRGs (LYVE1, FAM162A, WNT6, OTP, and PLOD), which was significantly related to the survival of patients with glioma [ 11 ]. Cai et al constructed and validated a prognostic model for hepatocellular carcinoma (HCC) composed of three hypoxia genes (ENO1, UGP2, and TPI1), which was shown to be effective for the prognosis of HCC patients [ 12 ]. Although previous study indicated that downregulated hypoxia transcriptome in vitro was closely related to the depressed prognosis in BC [ 13 ], the prognostic values of HRGs in BC was still unclear and attract us to further study on it.…”

Section: Introductionmentioning

confidence: 99%

Development of an Independent Prognostic Signature Based on Three Hypoxia-Related Genes for Breast Cancer

Wang

Guo

Zhang

et al. 2022

Computational and Mathematical Methods in Medicine

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Background. Hypoxia was considered to be a prognostic indicator in a variety of solid tumors. This study aims at identifying the hypoxia-related genes (HRGs) in breast cancer (BC) and the feasibility of HRGs as a prognostic indicator. Methods. We downloaded the mRNA expression data of BC patients from TCGA and GEO databases. The LASSO Cox regression analysis was applied to screen the hub HRGs to establish a prognostic Risk Score. The independence of Risk Score was assessed by multivariate Cox regression analysis. And the immune checkpoint analysis was also performed. In addition, we also detected the expression level of hub HRGs in MCF-10A cells, MCF-7 cells, and SK-BR-3 cells by RT-qPCR. Results. Three HRGs were identified as hub genes with prognostic value in BC, including CA9, PGK1, and SDC1. The Risk Score constructed by these three genes could efficiently distinguish the prognosis of different BC patients and has been shown to be an independent prognostic indicator. In the high-risk group, patients had lower overall survival and poorer prognosis. In addition, the expression levels of five immune checkpoints (PD1, CTLA4, TIGIT, LAG3, and TIM3) in the high-risk group were significantly higher than those in the low-risk group. Moreover, the expression levels of PGK1 and SDC1 in BC cells were significantly increased. Conclusion. In this study, we established an efficiently model based on three optimal HRGs (CA9, PGK1, and SDC1) could clearly distinguish the prognosis of different BC patients.

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8DEstablishment and validation of a hypoxia-related signature predicting prognosis in hepatocellular carcinoma

Cited by 7 publications

References 49 publications

Comprehensive analysis of an autophagy-related prognostic model for predicting survival based on TCGA and ICGC database in hepatocellular carcinoma patients

Comprehensive analysis of an autophagy-related prognostic model for predicting survival based on TCGA and ICGC database in hepatocellular carcinoma patients

A bioinformatics analysis and an experimental validation of the hypoxia-related prognostic model

Development of an Independent Prognostic Signature Based on Three Hypoxia-Related Genes for Breast Cancer

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