Construction of a risk score prognosis model based on hepatocellular carcinoma microenvironment

Zhang, Fa-Peng; Huang, Yipei; Luo, Weixin; Deng, Wanyu; Liu, Chaoqun; Xu, Lei-Bo

doi:10.3748/wjg.v26.i2.134

Cited by 45 publications

(38 citation statements)

References 96 publications

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“…Thereinto, a TP53-associated immune prognostic model established on two genes was named as "Long signature" [12], while a 10 gene-based signature that was associated with tumor microenvironments was named as "Pan signature" [13]. And a risk score prognostic model based on eight genes was named as "Zhang signature" [14].…”

Section: Signature Development and Reliability Evaluationmentioning

confidence: 99%

“…Although a number of HCC signatures have been established based on immune-related genes [12][13][14], a more comprehensive and reliable index is urgently demanded, which can simultaneously predict survival and the e cacy of immunotherapy for HCC patients. To this end, herein, based on the cancer genomics and bioinformatics, we established an immune-related gene-based prognostic index (IRGPI), followed by the validation of its reliability through several data sets.…”

Section: Introductionmentioning

confidence: 99%

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An immune-related gene signature for predicting survival and immunotherapy efficacy in hepatocellular carcinoma

Dai

Qiang

Lin

et al. 2020

Cancer Immunol Immunother

114

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Background: Hepatocellular carcinoma (HCC) ranks the fourth in terms of cancer-related mortality globally. Herein, in this research, we attempted to develop a novel immune-related gene signature that could predict survival and e cacy of immunotherapy for HCC patients. Methods: The transcriptomic and clinical data of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and GSE14520 datasets, followed by acquisition of immune-related genes from the ImmPort database. Afterwards, an immune-related gene-based prognostic index (IRGPI) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. Kaplan-Meier survival curves as well as time-dependent receiver operating characteristic (ROC) curve were performed to evaluate its predictive capability. Besides, both univariate and multivariate analysis on overall survival for the IRGPI and multiple clinicopathologic factors were carried out, followed by the construction of nomogram. Finally, we explored the possible correlation of IRGPI with immune cell in ltration or immunotherapy e cacy. Results: Analysis of 365 HCC samples identi ed 11 differentially expressed genes, which were selected to establish the IRGPI. Notably, it can predict survival of HCC patients more accurately than published biomarkers. Furthermore, IRGPI can predict the in ltration of immune cells in the tumor microenvironment of HCC, as well as the response of immunotherapy. Conclusion: Collectively, the currently established IRGPI can accurately predict survival, re ect the immune microenvironment, and predict the e cacy of immunotherapy among HCC patients.

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Section: Signature Development and Reliability Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An immune-related gene signature for predicting survival and immunotherapy efficacy in hepatocellular carcinoma

Dai

Qiang

Lin

et al. 2020

Cancer Immunol Immunother

114

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“…Therefore, many gene-related signatures have been developed for predicting prognosis of HCC. For instance, Zhang et al established a gene signature associated with HCC microenvironment and successfully verified it [ 7 ]. Other predictive signatures based on immune [ 8 ] and glycolysis [ 9 ] also play an important role in HCC prognosis.…”

Section: Introductionmentioning

confidence: 99%

A Novel Signature Based on mTORC1 Pathway in Hepatocellular Carcinoma

Zhang

2020

Journal of Oncology

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Background. mTORC1 signal pathway plays a role in the initiation and progression of hepatocellular carcinoma (HCC), but no relevant gene signature was developed. This research aimed to explore the potential correlation between the mTORC1 signal pathway and HCC and establish the related gene signature. Methods. HCC cases were retrieved from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. The genes included in mTORC1-associated signature were selected by performing univariate and multivariate Cox regression analyses and lasso regression analysis. The protein expression level of included genes was verified by The Human Protein Altas. Then, the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Moreover, the correlation between signature and immune cells infiltration was investigated. Furthermore, a nomogram was established and evaluated by C-index and calibration plot. Results. The signature was established with the six genes (ETF1, GSR, SKAP2, HSPD1, CACYBP, and PNP). Three genes (ETF1, GSR, and HSPD1) have verified their protein expression level in HCC. Under the grouping from signature, patients in the high-risk group showed worse survival than those in the low-risk group in both three datasets. The signature was found to be significantly associated with the infiltration of B cells, CD4+ T-cells, CD8+ T-cells, dendritic cells, macrophages, and neutrophils. The univariate and multivariate Cox regression analysis indicated that mTORC1-related signature could be the potential independent prognostic factor in HCC. Finally, the nomogram involving age, gender, stage, and signature has been established and verified. Conclusion. The mTORC1-associated gene signature established and validated in our research could be used as a potential prognostic factor in HCC.

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“…Ovarian cancer is among the most prevalent cancers in women ( Cheng et al, 2020 ). The TME has a major impact on tumor progression, recurrence, and metastasis, resulting in poor prognosis ( Chen et al, 2019 ; Huang et al, 2019 ; Zhang F.-P. et al, 2020 ). Previous studies have shown that the TME influences initiation and progression of OC and affects anti-tumor treatment ( Jiang et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Guo

Wang

et al. 2020

Front. Genet.

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Ovarian cancer (OC) is the deadliest gynecological cancer in women. Immune cell infiltration has a critical role in regulating carcinogenesis and prognosis in OC. To identify prognostic genes relevant to the tumor microenvironment in OC, we investigated the association between OC and gene expression profiles. Results obtained with the ESTIMATE R tool showed that immune score and stromal score were correlated with lymphatic invasion, and high immune score predicted a favorable prognosis. A total of 342 common differentially expressed genes were identified according to the two scores; these genes were mainly involved in immune response, extracellular region, and serine-type endopeptidase activity. Three immune-related prognostic genes were selected by univariate and multivariate Cox regression analysis. We further established a prognostic model and validated the prognostic value of three hub genes in different databases; our results showed that this model could accurately predict survival and evaluate prognosis independent of clinical characteristics. Three hub genes have prognostic value in OC. TIMER analysis revealed that the three genes were correlated with different immune cells. Low levels of macrophage infiltration and high levels of CD4+ T cell infiltration were associated with favorable survival outcomes. Arm-level gain of GYPC was correlated with neutrophils and dendritic cells. These findings indicate that CXCR4, GYPC, and MMP12 modulate prognosis via effects on the infiltration of immune cells. Thus, these genes represent potential targets for immune therapy in OC.

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Construction of a risk score prognosis model based on hepatocellular carcinoma microenvironment

Cited by 45 publications

References 96 publications

An immune-related gene signature for predicting survival and immunotherapy efficacy in hepatocellular carcinoma

An immune-related gene signature for predicting survival and immunotherapy efficacy in hepatocellular carcinoma

A Novel Signature Based on mTORC1 Pathway in Hepatocellular Carcinoma

Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

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