Dataset Information

A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma.

ABSTRACT:

Background

Cumulative evidences have been implicated cancer stem cells in the tumor environment of hepatocellular carcinoma (HCC) cells, whereas the biological functions and prognostic significance of stemness related genes (SRGs) in HCC is still unclear.

Methods

Molecular subtypes were identified by cumulative distribution function (CDF) clustering on 207 prognostic SRGs. The overall survival (OS) predictive gene signature was developed, internally and externally validated based on HCC datasets including The Cancer Genome Atlas (TCGA), GEO and ICGC datasets. Hub genes were identified in molecular subtypes by protein-protein interaction (PPI) network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analyses were performed to assess prognostic genes and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve and nomogram were used to assess the performance of the gene signature.

Results

We identified four molecular subtypes, among which the C2 subtype showed the highest SRGs expression levels and proportions of immune cells, whereas the worst OS; the C1 subtype showed the lowest SRGs expression levels and was associated with most favorable OS. Next, we identified 11 prognostic genes (CDX2, PON1, ADH4, RBP2, LCAT, GAL, LPA, CYP19A1, GAST, SST and UGT1A8) and then constructed a prognostic 11-gene module and validated its robustness in all three datasets. Moreover, by univariate and multivariate Cox regression, we confirmed the independent prognostic ability of the 11-gene module for patients with HCC. In addition, calibration analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the 11-gene signature.

Conclusions

Findings in the present study shed new light on the role of stemness related genes within HCC, and the established 11-SRG signature can be utilized as a novel prognostic marker for survival prognostication in patients with HCC.

SUBMITTER: Hong L

PROVIDER: S-EPMC8214273 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundActivation of the Hedgehog signaling pathway is linked to the initiation and development of human hepatocellular carcinoma (HCC). However, its impact on clinical outcomes and the HCC microenvironment remains unclear.MethodsWe performed comprehensive analyses of Hedgehog pathway genes in a large cohort of HCC patients. Specifically, we utilized univariate Cox regression analysis to identify Hedgehog genes linked to overall survival, and the LASSO algorithm was used to construct a Hedgehog-related gene pattern. We subsequently examined the correlation between the Hedgehog pattern and the HCC microenvironment employing the CIBERSORT and ssGSEA algorithms. Furthermore, Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and the anti-PD-L1 treatment dataset (IMvigor210) are used to evaluate the clinical response of the Hedgehog pattern in predicting immune checkpoint inhibitors.ResultsWe found that the Hedgehog activation score (HHAS), a prognostic score based on 11 Hedgehog genes, was significantly associated with HCC patient survival. Patients exhibiting high HHAS experienced markedly reduced survival rates compared to those with low HHAS, and HHAS emerged as an independent prognostic factor for HCC. Functional enrichment analysis unveiled the association of the HHAS phenotype with functions related to the immune system, and further investigation demonstrated that HCC patients exhibiting low HHAS displayed elevated levels of anti-tumor immune activation in CD8+ T cells, while high HHAS were linked to immune escape phenotypes and increased infiltration of immune suppressive cells. In addition, in the Immune Checkpoint Inhibitor (ICI) cohort of IMvigor210, patients with higher HHAS had worse ICI treatment outcomes and shortened survival time, indicating that the HHAS is a useful indicator for predicting patient response to immunotherapy.ConclusionsIn summary, our study offers valuable insights for advancing research on Hedgehog and its impact on tumor immunity, which provides an opportunity to optimize prognosis and immune therapy for HCC.

Project description:Hepatocellular carcinoma (HCC) is the main subtype of primary liver cancer with high malignancy and poor prognosis. Metabolic reprogramming is a hallmark of cancer and has great importance on the tumor microenvironment (TME). As an abundant metabolite, lactate plays a crucial role in cancer progression and the immunosuppressive TME. Nonetheless, the potential roles of lactate in HCC remain unclear. In this study, we downloaded transcriptomic data of HCC patients with corresponding clinical information from the TCGA and ICGC portals. The TCGA-HCC dataset used as the training cohort, while the ICGC-LIRI-JP dataset was served as an external validation cohort. Cox regression analysis and the LASSO regression model were combined to construct the lactate metabolism-related gene signature (LMRGS). Then, we assessed the clinical significance of LMRGS in HCC. Besides, enriched molecular functions, tumor mutation burden (TMB), infiltrating immune cells, and immune checkpoint were comprehensively analyzed in different LMRGS subgroups. In total, 66 differentially expressed lactate metabolism-related genes (LMRGs) were screened. The functions of LMRGs were mainly enriched in mitochondrial activity and metabolic processes. The LMRGS comprised of six key LMRGs (FKTN, PDSS1, PET117, PUS1, RARS1, and RNASEH1) had significant clinical value for independently predicting the prognosis of HCC patients. The overall survival and median survival of patients in the LMRGS-high group were significantly shorter than in the LMRGS-low group. In addition, there were differences in TMB between the two LMRGS subgroups. The probability of genetic mutations was higher in the LMRGS-high group. Most importantly, the LMRGS reflected the TME characteristics. In the LMRGS-high group, the immune microenvironment presented a suppressed state, accompanied by more inhibitory immune cell infiltration, including follicular helper T cells and regulatory T cells. Additionally, the expression of inhibitory checkpoint molecules was much higher in the LMRGS-high group. Our study suggested that the LMRGS was a robust biomarker to predict the clinical outcomes and evaluate the TME of patients with HCC.

Project description:Background: Non-apoptotic programmed cell death, including autophagy, ferroptosis, and pyroptosis, newly discovered in recent years, plays an important role in hepatocellular carcinoma (HCC). So, this study attempted to explore the relationship between non-apoptotic programmed cell death-related genes and the molecular characteristics, tumor microenvironment, and prognosis in HCC patients. Methods: The transcriptomic and clinical data of HCC samples were downloaded from various public datasets, followed by acquiring non-apoptotic programmed cell death-related genes from the database. A gene signature model was then constructed using univariate and multivariate Cox regression analyses and validated in other cohorts as well as our institution sequencing data. Kaplan-Meier survival curves and time-dependent receiver operating characteristic curves were generated to evaluate the model's predictive capability. Furthermore, the relationships among the gene signature, TP53 mutation, stemness, immune status, and responsiveness of transarterial chemoembolization (TACE) were analyzed. Results: The gene signature model was constructed based on five autophagy-, three ferroptosis-, and two pyroptosis-related differentially expressed genes. The model accurately predicted that patients classified as low risk would have better overall survival than high-risk patients, which was robustly consistent with data from other cohorts as well as our institution sequencing data. The comprehensive results indicated that a high-risk index was correlated with a high TP53 mutation rate, high cancer cell stemness, high infiltration of immunosuppressive cells and low immunophenoscore, and low TACE responsiveness of HCC patients. Conclusion: Collectively, the established non-apoptotic programmed cell death-related gene signature was shown to accurately predict prognosis, associated with the TP53 mutation and liver cancer cell stemness, reflect the tumor immune microenvironment, and predict TACE responsiveness in HCC patients.

Project description:BackgroundHepatocellular carcinoma (HCC) is one of the world's most prevalent and lethal cancers. Notably, the microenvironment of tumor starvation is closely related to cancer malignancy. Our study constructed a signature of starvation-related genes to predict the prognosis of liver cancer patients.MethodsThe mRNA expression matrix and corresponding clinical information of HCC patients were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GSEA) was used to distinguish different genes in the hunger metabolism gene in liver cancer and adjacent tissues. Gene Set Enrichment Analysis (GSEA) was used to identify biological differences between high- and low-risk samples. Univariate and multivariate analyses were used to construct prognostic models for hunger-related genes. Kaplan-Meier (KM) and receiver-operating characteristic (ROC) were used to assess the model accuracy. The model and relevant clinical information were used to construct a nomogram, protein expression was detected by western blot (WB), and transwell assay was used to evaluate the invasive and metastatic ability of cells.ResultsFirst, we used univariate analysis to identify 35 prognostic genes, which were further demonstrated to be associated with starvation metabolism through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We then used multivariate analysis to build a model with nine genes. Finally, we divided the sample into low- and high-risk groups according to the median of the risk score. KM can be used to conclude that the prognosis of high- and low-risk samples is significantly different, and the prognosis of high-risk samples is worse. The prognostic accuracy of the 9-mRNA signature was also tested in the validation data set. GSEA was used to identify typical pathways and biological processes related to 9-mRNA, cell cycle, hypoxia, p53 pathway, and PI3K/AKT/mTOR pathway, as well as biological processes related to the model. As evidenced by WB, EIF2S1 expression was increased after starvation. Overall, EIF2S1 plays an important role in the invasion and metastasis of liver cancer.ConclusionsThe 9-mRNA model can serve as an accurate signature to predict the prognosis of liver cancer patients. However, its mechanism of action warrants further investigation.