Identification and Validation of an Immune-related Prognostic Signature for Hepatocellular Carcinoma

Xia, Xinxin; Tang, Ping; Liu, Hui; Li, Yuejun

doi:10.14218/jcth.2021.00017

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…Although other studies associated with mining immune-related prognostic biomarkers have been published, they tend to lack precision, which impacts the amount of confidence in the techniques and results ( 38 , 39 ). For example, screening differentially expressed genes through a single dataset could cause result bias.…”

Section: Discussionmentioning

confidence: 99%

Identification of immune-related genes and patient selection for hepatocellular carcinoma immunotherapy

Chen¹,

Luo²,

Ye³

et al. 2023

Transl Cancer Res

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Background: Hepatocellular carcinoma (HCC) is a malignant disease with a poor prognosis. Among the treatment strategies for HCC, tumor immunotherapy (TIT) is a promising research hotspot, in which identifying novel immune-related biomarkers and selecting suitable patient population are urgent issues to be solved.Methods: In this study, an abnormal expression map of HCC cell genes was constructed using public high-throughput data from 7,384 samples (3,941 HCC vs. 3,. Through singlecell RNA sequencing (scRNA-seq) cell trajectory analysis, the genes defined as potential drivers of HCC cell differentiation and development were selected. By screening for both immune-related genes and those associated with high differentiation potential in HCC cell development, a series of target genes were identified. Coexpression analysis was performed using Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) to find the specific candidate genes involved in similar biological processes.Subsequently, nonnegative matrix factorization (NMF) was conducted to select patients suitable for HCC immunotherapy based on the coexpression network of candidate genes.Results: HSP90AA1, CDK4, HSPA8, HSPH1, and HSPA5 were identified as promising biomarkers for prognosis prediction and immunotherapy of HCC. Through the use of our molecular classification system, which was based on a function module containing 5 candidate genes, patients with specific characteristics were found to be suitable candidates for TIT.Conclusions: These findings provide new insights into the selection of candidate biomarkers and patient populations for future HCC immunotherapy.

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Section: Discussionmentioning

confidence: 99%

Identification of immune-related genes and patient selection for hepatocellular carcinoma immunotherapy

Chen¹,

Luo²,

Ye³

et al. 2023

Transl Cancer Res

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“…AKT1, as a downstream effector of PI3K signaling, is evolved in the invasion, migration, apoptosis inhibition, autophagy regulation, and epithelial-to-mesenchymal transition (EMT) of GC cells. [41][42][43][44][45][46][47][48] For STAT3, its dysregulation may initiate tumorigenesis by suppressing cell death and promoting tumor cell growth angiogenesis and EMT. 49,50 Thus, it is speculated that AKT1 and STAT3 may be the key targets modulated by SJZD in GC patients.…”

Section: Discussionmentioning

confidence: 99%

Systemic Analysis of the Anticancer Effects of Sijunzi Decoction on Gastric Cancer Based on Network Pharmacology

Wang

Liao

et al. 2022

Natural Product Communications

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Objective: Sijunzi decoction (SJZD) has been used for alleviating peptic ulcer or gastric discomfort, and treating spleen disorders since the Song Dynasty, but its pharmacological effect on human gastric cancer (GC) is still unclear. In this research, a network pharmacology-based strategy was applied to explore active ingredients, potential targets, and molecular mechanisms of SJZD against GC. Methods: The active compounds and potential targets of SJZD, as well as GC-associated gene targets, were retrieved from publicly available databases. Bioinformatics approaches were used to assess the network interaction, functional regulation, and signaling pathways between SJZD ingredients and GC targets. The anticancer effects of SJZD against GC were verified in vivo by a mouse subcutaneous model. Results: The results of network analysis showed that quercetin was the most active ingredient in SJZD. Several prominent target genes of SJZD were identified, such as AKT1 and STAT3. Gene ontology analysis revealed that the core anti-GC targets of SJZD included transcription factor activity and kinase activity. Pathway enrichment analysis indicated that GC patients could be benefited from SJZD treatment via modulation of signaling pathways related to endocrine system, cancer, and infectious disease. Furthermore, in vivo experiments showed that high-dose SJZD could inhibit GC xenograft tumor growth, reduce GC cell proliferation, induce GC cell apoptosis, and decrease the expression of p-AKT1 and p-STAT3. Conclusions: Taken together, our results suggest that SJZD can serve as an effective adjuvant therapeutic agent for GC patients.

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“…In recent years, studies on multi-gene prognostic markers for HCC have been reported, such as lncRNAs signature, metabolic-related gene signature, m6A-related gene signature, immune-related gene signature, etc. ( 32 - 34 ). These studies remind researchers that multi-gene combinations may become more reliable prognostic markers for HCC, despite the fact that they have not yet been utilized to guide clinical treatment.…”

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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Identification and Validation of an Immune-related Prognostic Signature for Hepatocellular Carcinoma

Cited by 5 publications

References 44 publications

Identification of immune-related genes and patient selection for hepatocellular carcinoma immunotherapy

Identification of immune-related genes and patient selection for hepatocellular carcinoma immunotherapy

Systemic Analysis of the Anticancer Effects of Sijunzi Decoction on Gastric Cancer Based on Network Pharmacology

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

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