Dataset Information

A precise prognostic signature in CTNNB1-mutant hepatocellular carcinoma: Prognosis prediction and precision treatment exploration.

ABSTRACT:

Background

CTNNB1 mutates in most hepatocellular carcinoma (HCC) which is the most familiar form of liver cancer with high heterogeneity. It is critical to create a specific prognostication methodology and to investigate additional treatment options for CTNNB1-mutant HCCs.

Methods

A total of 926 samples in five independent cohorts were enrolled in this study, including 127 CTNNB1-mutant samples and 75 estimated CTNNB1-mutant samples. The prognostic signature was constructed by LASSO-Cox regression and evaluated by bioinformatics analyses. The selection of possible drug targets and agents was produced based on the expression profiles and drug sensitivity data of cancer cell lines in two databases.

Results

A prognostic signature based on 15 genes categorized the CTNNB1-mutant HCCs into two groups with different risks. Compared to low-risk patients, high-risk patients had significantly inferior prognoses. ROC curve and multivariate analysis also indicated the superior performance of our signature on the prognosis estimation, particularly in CTNNB1-mutant HCCs. Besides, the nomogram was constructed according to the prognostic signature with excellent predictive performance confirmed by the calibration curve. Subsequently, we suggested that AT-7519 and PHA-793887 might be potential drug agents for high-risk patients.

Conclusion

We established a 15-gene prognostic model, particularly in HCCs with CTNNB1 mutations with good predictive efficiency. Besides, we explored the potential drug targets and agents for patients with high risk. Our findings offered a fresh idea for personalized prognosis management in HCCs with CTNNB1 mutations and threw new insight for precise treatment in HCCs as well.

SUBMITTER: Wang J

PROVIDER: S-EPMC10730442 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

A precise prognostic signature in CTNNB1-mutant hepatocellular carcinoma: Prognosis prediction and precision treatment exploration.

Wang Junying J Zhu Guangyu G

Heliyon 20231114 12

<h4>Background</h4>CTNNB1 mutates in most hepatocellular carcinoma (HCC) which is the most familiar form of liver cancer with high heterogeneity. It is critical to create a specific prognostication methodology and to investigate additional treatment options for CTNNB1-mutant HCCs.<h4>Methods</h4>A total of 926 samples in five independent cohorts were enrolled in this study, including 127 CTNNB1-mutant samples and 75 estimated CTNNB1-mutant samples. The prognostic signature was constructed by LAS ...[more]

PMID: 38125518

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Project description:BackgroundThe prognosis of hepatocellular carcinoma (HCC) patients remains poor. Identifying prognostic markers to stratify HCC patients might help to improve their outcomes.MethodsSix gene expression profiles (GSE121248, GSE84402, GSE65372, GSE51401, GSE45267 and GSE14520) were obtained for differentially expressed genes (DEGs) analysis between HCC tissues and non-tumor tissues. To identify the prognostic genes and establish risk score model, univariable Cox regression survival analysis and Lasso-penalized Cox regression analysis were performed based on the integrated DEGs by robust rank aggregation method. Then Kaplan-Meier and time-dependent receiver operating characteristic (ROC) curves were generated to validate the prognostic performance of risk score in training datasets and validation datasets. Multivariable Cox regression analysis was used to identify independent prognostic factors in liver cancer. A prognostic nomogram was constructed based on The Cancer Genome Atlas (TCGA) dataset. Finally, the correlation between DNA methylation and prognosis-related genes was analyzed.ResultsA twelve-gene signature including SPP1, KIF20A, HMMR, TPX2, LAPTM4B, TTK, MAGEA6, ANX10, LECT2, CYP2C9, RDH16 and LCAT was identified, and risk score was calculated by corresponding coefficients. The risk score model showed a strong diagnosis performance to distinguish HCC from normal samples. The HCC patients were stratified into high-risk and low-risk group based on the cutoff value of risk score. The Kaplan-Meier survival curves revealed significantly favorable overall survival in groups with lower risk score (P < 0.0001). Time-dependent ROC analysis showed well prognostic performance of the twelve-gene signature, which was comparable or superior to AJCC stage at predicting 1-, 3-, and 5-year overall survival. In addition, the twelve-gene signature was independent with other clinical factors and performed better in predicting overall survival after combining with age and AJCC stage by nomogram. Moreover, most of the prognostic twelve genes were negatively correlated with DNA methylation in HCC tissues, which SPP1 and LCAT were identified as the DNA methylation-driven genes.ConclusionsWe identified a twelve-gene signature as a robust marker with great potential for clinical application in risk stratification and overall survival prediction in HCC patients.

Project description:Hepatocellular carcinoma (HCC), the most frequently occurring type of cancer, is strongly associated with metabolic disorders. In this study, we aimed to characterize the metabolic features of HCC and normal tissue adjacent to the tumor (NAT). By using samples from The Cancer Genome Atlas (TCGA) liver cancer cohort and comparing 85 well-defined metabolic pathways obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG), 70 and 7 pathways were found to be significantly downregulated and upregulated, respectively, in HCC, revealing that tumor tissue lacks the ability to maintain normal metabolic levels. Through unsupervised hierarchical clustering of metabolic pathways, we found that metabolic heterogeneity correlated with prognosis in HCC samples. Thus, using the least absolute shrinkage and selection operator (LASSO) and filtering independent prognostic genes by the Cox proportional hazards model, a six-gene-based metabolic score model was constructed to enable HCC classification. This model showed that high expression of LDHA and CHAC2 was associated with an unfavorable prognosis but that high ADPGK, GOT2, MTHFS, and FTCD expression was associated with a favorable prognosis. Patients with higher metabolic scores had poor prognoses (p value = 2.19e-11, hazard ratio = 3.767, 95% CI = 2.555-5.555). By associating the score level with clinical features and genomic alterations, it was found that NAT had the lowest metabolic score and HCC with tumor stage III/IV the highest. qRT‒PCR results for HCC patients also revealed that tumor samples had higher score levels than NAT. Regarding genetic alterations, patients with higher metabolic scores had more TP53 gene mutations than those with lower metabolic scores (p value = 8.383e-05). Validation of this metabolic score model was performed using another two independent HCC cohorts from the Gene Expression Omnibus (GEO) repository and other TCGA datasets and achieved good performance, suggesting that this model may be used as a reliable tool for predicting the prognosis of HCC patients.

Project description:Globally, hepatocellular carcinoma (HCC) is the sixth most common cancer. In this study, the correlation between mitophagy and HCC prognosis was evaluated using data from The Cancer Genome Atlas (TCGA). Clinical and transcriptomic data of HCC patients were downloaded from TCGA dataset, and mitophagy-related gene (MRG) datasets were obtained from the Molecular Signature Database. Then, a consensus clustering analysis was performed to classify the patients into two clusters. Furthermore, tumor prognosis, clinicopathological features, functional analysis, immune infiltration, immune checkpoint (IC)-related gene expression level, tumor stem cells, ferroptosis status, and N6-methyladenosine analysis were compared between the two clusters. Finally, a mitophagy-related signature was developed. Two clusters (C1 and C2) were identified using the consensus clustering analysis based on the MRG signature. Patients with the C1 subtype exhibited upregulated pathways with better liver function, downregulated cancer-related pathways, lower cancer stem cell scores, lower Tumor Immune Dysfunction and Exclusion scores (TIDE), different ferroptosis status, and better prognosis compared with the patients with the C2 subtype. The C2 subtype was characterized by the increased grade of HCC, as well as the increased number of immune-related pathways and m6A-related genes. Higher immune scores were also observed for the C2 subtype. A signature containing four MRGs (PGAM5, SQSTM1, ATG9A, and GABARAPL1) which can accurately predict the prognosis of HCC patients was then identified. This four-gene signature exhibited a predictive effect in five other cancer types, namely glioma, uveal melanoma, acute myeloid leukemia, adrenocortical carcinoma, and mesothelioma. The mitophagy-associated subtypes of HCC were closely related to the immune microenvironment, immune checkpoint-related gene expression, cancer stem cells, ferroptosis status, m6A, prognosis, and HCC progression. The established MRG signature could predict prognosis in patients with HCC.

Dataset Information

A precise prognostic signature in CTNNB1-mutant hepatocellular carcinoma: Prognosis prediction and precision treatment exploration.

Background

Methods

Results

Conclusion

Publications

A precise prognostic signature in CTNNB1-mutant hepatocellular carcinoma: Prognosis prediction and precision treatment exploration.

Similar Datasets

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