Identification and Validation of Novel Immunogenic Cell Death- and DNA Damage Response-Related Molecular Patterns Correlated with Immune Status and Prognosis in Hepatocellular Carcinoma

Zhang, Xiaokai; Wen, Jie; Zhang, Guixiong; Fan, Wenzhe; Tan, Jizhou; Liu, Haikuan; Li, Jiaping

doi:10.1016/j.tranon.2022.101600

Cited by 5 publications

(2 citation statements)

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“…Different tumor microenvironments predicted different targeting and immunotherapy effects, and also predicted different prognosis [24,25]. We found that risk models based on ICD-related genes are closely related to the human HCC immune microenvironment.…”

Section: Discussionmentioning

confidence: 96%

A model based on Immunogenic cell death-related genes can predict prognosis and drug sensitivity in patients with hepatocellular carcinoma

Shi,

Zhang,

Han

et al. 2024

Preprint

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Background: Immunogenic cell death (ICD) can activate the immune system against cancer in host with immunocompetent. However, the prognostic role of ICD-related genes (IRGs) in hepatocellular carcinoma (HCC) is unknown.We aimed to establish a prognostic model based on IRGs, and to verify the relationship between this model and the immune microenvironment of HCC, and whether this model can predict the prognosis of patients with HCC. Methods: The Cancer Genome Atlas-Liver Hepatocellular Carcinoma(TCGA-LIHC), and The Liver Cancer, Riken Japan (LIRI-JP) were downloaded via public databases, and 34 IRGs were extracted from the literature. First, consensus clustering analysis was applied in TCGA-LIHC to classified LIHC samples into different clusters based on IRGs. Differentially expressed genes (DEGs) between LIHC and normal samples in TCGA-LIHC, and DEGs among clusters were respectively sifted out through differential expression analysis, and they were overlapped to obtain IRGs-DEGs. Next, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were implemented on IRGs-DEGs for identifing prognosis related genes, and HCC samples were divided into high/low risk groups via risk score. Moreover, independent prognostic factors were yielded via univariate and multivariate Cox analyses, and enrichment analysis was applied for exploring biological pathways related to the prognosis model. Finally, the role of prognostic model in immune microenvironment and chemotherapy of HCC were analyzed. Results: HCC samples were classified into two clusters in the light of IRGs, and cluster2 had a poorer survival state than that of cluster1. Totally 2197 DEGs in TCGA-LIHC and 112 DEGs between clusters were yielded, and they were intersected to get 72 IRGs-DEGs. Six prognosis-related genes, namely KRT20, MMP12, AGR2, CXCL5, CYP3A4, and MAGEA8 were finally identified via univariate Cox and LASSO analyses. Besides, the risk score and grade were found to be correlated with LIHC prognosis. Obviously, the prognostis model was related to immune, and metabolism related pathways like nitrogen metabolism, and adaptive immune response. Moreover, tumor immune dysfunction and exclusion (TIDE) score was sensibly lowly expressed in low risk group, suggesting that low risk group patients were more susceptible to immunotherapy. Ultimatly, high risk group was more sensitive to Camptothecin, Sorafenib and others, while low risk group was more responsive to Veliparib and Dabrafenib. Conclusion: Through bioinformatic analysis, a prognosis model (contained KRT20, MMP12, AGR2, CXCL5, CYP3A4, and MAGEA8) was constructed in HCC, contributing to studies related to prognosis and treatment of HCC.

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Section: Discussionmentioning

confidence: 96%

A model based on Immunogenic cell death-related genes can predict prognosis and drug sensitivity in patients with hepatocellular carcinoma

Shi,

Zhang,

Han

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…As illustrated in Fig. 6, the TIDE score of patients in the lowrisk group is signi cantly higher than that in the high-risk group, indicating that this group of patients has a poor effect on immune checkpoint blocking therapy (ICB) and a shorter survival time after receiving ICB treatment [21].Currently, chemotherapy is still an important method for treating HCC [22][23], and drug resistance is the main cause of treatment failure [24]. Therefore, we analyzed the resistance and sensitivity of chemotherapeutic drugs to validate the predictive ability of DECRLsig in determining treatment effectiveness.…”

Section: Discussionmentioning

confidence: 99%

A Novel Differentially Expressed Cuproptosis-Related lncRNAs Signature to predict the prognosis and immune characteristics of hepatocellular carcinoma

Yin

Gao

Sun

et al. 2023

Preprint

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Objective: According to a growing body of research, long non-coding RNAs (lncRNAs) participate in the progress of hepatocellular carcinoma (HCC). Cuproptosis is a distinct kind of programmed cell death, separating it from several other forms of programmed cell death that may be caused by genetic programming. Consequently, our aim was to investigate the relationship between Differentially Expressed Cuproptosis-Related lncRNAs (DECRLs) and clinical outcome and immune characteristics of HCC. Method: The Cancer Genome Atlas (TCGA) database was used to retrieve related data. The GSE101728 dataset was downloaded from the Gene Expression Omnibus (GEO) database. A list of cuproptosis-related genes (CRGs) was obtained from a recently published article in Science. Combined analysis of TCGA dataset and the GSE101728 dataset identified differentially expressed CRGs(DECRGs).We can obtain DECRLs via co-expression. Then, using DECRLs, we developed a risk prediction model using Cox regression analysis and the least absolute shrinkage selection operator (LASSO) regression analysis. To evaluate the diagnostic accuracy of this model, a Kaplan-Meier (K-M) survival analysis and a receiver operating characteristic (ROC) curve analysis were used. Next, principal component analysis (PCA) was carried out.Moreover, the relationships between the risk model and immune characteristics, somatic mutation, and drug sensitivity were also investigated. Finally Real-Time quantitative PCR(RT-qPCR) and Western Blot confirmed the expression of DECRGs or DECRLs in HCC. Results: Three high-risk DECRLs(AL031985.3,AC107959.3,MKLN1-AS) that can guide HCC prognosis and immune microenvironment were obtained through cox regression analysis.Immune functions such as APC co-inhibition,Type-II-IFN-Reponse,Parainflammation,MHC-class-I, and Tumor Immune Dysfunction and Exclusion(TIDE) score, and Tumor Mutation Burden(TMB) were significantly different in high-risk and low-risk groups.Moreover, this research also found that the IC50 values for 87 chemotherapeutic drugs varied widely across patients within high and low-risk groups.The expression of GLS at both mRNA and protein levels was significantly raised in HCC,and that of CDKN2A was dropped in HCC. The mRNA expression level of AL031985.3,AC107959.3 and MKLN1-AS was upregulated in HCC. Conclusion: The proposed 3-DECRLs that can predict clinical prognosis or guide the immune characteristics and drugs that may have a potential curative effect on HCC received in our research may play a major role in patient management and immunotherapy.

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The multifaceted potential of TPT1 as biomarker and therapeutic target

Miao,

Yang,

Yang

et al. 2024

Heliyon

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Identification and Validation of Novel Immunogenic Cell Death- and DNA Damage Response-Related Molecular Patterns Correlated with Immune Status and Prognosis in Hepatocellular Carcinoma

Cited by 5 publications

References 50 publications

A model based on Immunogenic cell death-related genes can predict prognosis and drug sensitivity in patients with hepatocellular carcinoma

A model based on Immunogenic cell death-related genes can predict prognosis and drug sensitivity in patients with hepatocellular carcinoma

A Novel Differentially Expressed Cuproptosis-Related lncRNAs Signature to predict the prognosis and immune characteristics of hepatocellular carcinoma

The multifaceted potential of TPT1 as biomarker and therapeutic target

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