The Prognostic Significance and Potential Mechanism of Ferroptosis-Related Genes in Hepatocellular Carcinoma

PPM1G and its diagnostic, prognostic and therapeutic potential in HCC (Review)

Global statistics indicate that hepatocellular carcinoma (HCC) is the sixth most common cancer and the third leading cause of cancer‑related death. Protein phosphatase Mg2+/Mn2+ dependent 1G (PPM1G, also termed PP2Cγ) is one of the 17 members of the PPM family. The enzymatic activity of PPM1G is highly reliant on Mg2+ or Mn2+ and serves as a dephosphorylation regulator for numerous key proteins. PPM1G, functioning as a phosphatase, is involved in a number of significant biological processes such as the regulation of eukaryotic gene expression, DNA damage response, cell cycle and apoptosis, cell migration ability, cell survival and embryonic nervous system development. Additionally, PPM1G serves a role in regulating various signaling pathways. In recent years, further research has increasingly highlighted PPM1G as an oncogene in HCC. A high expression level of PPM1G is closely associated with the occurrence, progression and poor prognosis of HCC, offering notable diagnostic and therapeutic value for this patient population. In the present review, the regulatory role of PPM1G in diverse biological processes and signaling pathway activation in eukaryotes is evaluated. Furthermore, its potential application as a biomarker in the diagnosis and prognosis evaluation of HCC is assessed, and future prospects for HCC treatment strategies centered on PPM1G are discussed.

Unveiling a cuproptosis-related risk model and the role of FARSB in hepatocellular carcinoma

Background

Cuproptosis, a type of regulated cell death that was recently identified, has been linked to the development of a variety of diseases, among them being cancers. Nevertheless, the prognostic significance and therapeutic implications of the cuproptosis potential index in hepatocellular carcinoma (HCC) remain uncertain.

Methods

Single-sample gene set enrichment analysis (ssGSEA) and Weighted Gene Co-expression Network Analysis (WGCNA) methodology was conducted to ascertain the identification of modular genes that are closely linked to cuproptosis. In addition, the gene signature indicative of prognosis was formulated by employing univariate Cox regression analysis in conjunction with a random forest algorithm. The efficacy of this gene signature in predicting outcomes was confirmed through validation in both The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Furthermore, a study was undertaken to evaluate the association between the risk score and various clinical-pathological characteristics, explore the biological processes linked to the gene signature, and analyze tumor mutational burden and somatic mutations. Lastly, potential drugs targeting the identified gene signature were identified through screening.

Results

The results of our comprehensive analysis across multiple cancer types demonstrated a positive correlation between an elevated cuproptosis potential index (CPI) and an accelerated rate of tumor progression. Furthermore, employing the WGCNA technique, we successfully identified 640 genes associated with cuproptosis. Among these genes, we meticulously screened and validated a seven-gene signature (TCOF1, NOP58, TMEM69, FARSB, DHX37, SLC16A3, and CBX2) that exhibited substantial prognostic significance. Using the median risk score, the division of HCC patients into cohorts with high- and low-risk highlighted significant disparities in survival results, wherein the group with higher risk exhibited a less favorable overall survival. The risk score exhibited commendable predictive efficacy. Moreover, the in vitro knockdown of FARSB significantly hindered cell viability, induced G1 phase arrest, increased apoptosis, and impaired migration in HepG2 and Huh7 cells.

Conclusion

Our research has successfully identified a strong seven-gene signature linked to cuproptosis, which could be utilized for prognostic evaluation and risk stratification in patients with HCC. Furthermore, the discovered gene signature, coupled with the functional analysis of FARSB, presents promising prospects as potential targets for therapeutic interventions in HCC.

Identification of cuproptosis and ferroptosis-related subgroups and development of a signature for predicting prognosis and tumor microenvironment landscape in hepatocellular carcinoma

Background

Ferroptosis and cuproptosis play a crucial role in the progression and dissemination of hepatocellular carcinoma (HCC). The primary objective of this study was to develop a unique scoring system for predicting the prognosis and immunological landscape of HCC based on ferroptosis-related genes (FRGs) and cuproptosis-related genes (CRGs).

Methods

As the training cohort, we assembled a novel HCC cohort by merging gene expression data and clinical data from The Cancer Genome Atlas (TCGA) database, and Gene Expression Omnibus (GEO) database. The validation cohort consisted of 230 HCC cases taken from the International Cancer Genome Consortium (ICGC) database. Multiple genomic characteristics, such as tumor mutation burden (TMB), and copy number variations were analyzed concurrently. On the basis of the expression of CRGs and FRGs, patients were classified into cuproptosis and ferroptosis subtypes. Then, we constructed a risk model using least absolute shrinkage and selection operator (LASSO) analysis and Cox regression analysis based on ferroptosis and cuproptosis-related differentially expressed genes (DEGs). Patients were separated into two groups according to median risk score. We compared the immunophenotype, tumor microenvironment (TME), cancer stem cell index, and treatment sensitivity of two groups.

Results

Three subtypes of ferroptosis and two subtypes of cuproptosis were identified among the patients. A greater likelihood of survival (P<0.05) was expected for patients in FRGcluster B and CRGcluster B. After that, a confirmed risk signature for ferroptosis and cuproptosis was developed and tested. Patients in the low-risk group had significantly higher survival rates than those in the high-risk group, according to our study (P<0.001). There was also a strong correlation between the signature and other variables including immunophenoscore, TMB, cancer stem cell index, immunological checkpoint genes, and sensitivity to chemotherapeutics.

Conclusions

Through this comprehensive research, we identified a unique risk signature associated with HCC patients' treatment status and prognosis. Our findings highlight FRGs' and CRGs' significance in clinical practice and imply ferroptosis and cuproptosis may be therapeutic targets for HCC patients.

Ferroptosis and cuproptosis prognostic signature for prediction of prognosis, immunotherapy and drug sensitivity in hepatocellular carcinoma: development and validation based on TCGA and ICGC databases

Background

Hepatocellular carcinoma (HCC) is a common malignancy. Ferroptosis and cuproptosis promote HCC spread and proliferation. While fewer studies have combined ferroptosis and cuproptosis to construct prognostic signature of HCC. This work attempts to establish a novel scoring system for predicting HCC prognosis, immunotherapy, and medication sensitivity based on ferroptosis-related genes (FRGs) and cuproptosis-related genes (CRGs).

Methods

FerrDb and previous literature were used to identify FRGs. CRGs came from original research. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases included the HCC transcriptional profile and clinical information [survival time, survival status, age, gender, Tumor Node Metastasis (TNM) stage, etc.]. Correlation, Cox, and least absolute shrinkage and selection operator (LASSO) regression analyses were used to narrow down prognostic genes and develop an HCC risk model. Using "caret", R separated TCGA-HCC samples into a training risk set and an internal test risk set. As external validation, we used ICGC samples. We employed Kaplan-Meier analysis and receiver operating characteristic (ROC) curve to evaluate the model's clinical efficacy. CIBERSORT and TIMER measured immunocytic infiltration in high- and low-risk populations.

Results

TXNRD1 [hazard ratio (HR) =1.477, P<0.001], FTL (HR =1.373, P=0.001), GPX4 (HR =1.650, P=0.004), PRDX1 (HR =1.576, P=0.002), VDAC2 (HR =1.728, P=0.008), OTUB1 (HR =1.826, P=0.002), NRAS (HR =1.596, P=0.005), SLC38A1 (HR =1.290, P=0.002), and SLC1A5 (HR =1.306, P<0.001) were distinguished to build predictive model. In both the model cohort (P<0.001) and the validation cohort (P<0.05), low-risk patients had superior overall survival (OS). The areas under the curve (AUCs) of the ROC curves in the training cohort (1-, 3-, and 5-year AUCs: 0.751, 0.727, and 0.743), internal validation cohort (1-, 3-, and 5-year AUCs: 0.826, 0.624, and 0.589), and ICGC cohort (1-, 3-, and 5-year AUCs: 0.699, 0.702, and 0.568) were calculated. Infiltration of immune cells and immunological checkpoints were also connected with our signature. Treatments with BI.2536, Epothilone.B, Gemcitabine, Mitomycin.C, Obatoclax. Mesylate, and Sunitinib may profit high-risk patients.

Conclusions

We analyzed FRGs and CRGs profiles in HCC and established a unique risk model for treatment and prognosis. Our data highlight FRGs and CRGs in clinical practice and suggest ferroptosis and cuproptosis may be therapeutic targets for HCC patients. To validate the model's clinical efficacy, more HCC cases and prospective clinical assessments are needed.

Analysis of the potential association between ferroptosis and immune in hepatocellular carcinoma and their relationship with prognosis

Background

The development of targeted therapy and immunotherapy has enriched the treatment of hepatocellular carcinoma (HCC), however, have had poor or no reponse, or even no response. Previous research suggested that ferroptosis and tumor immune microenvironment (TIME) may have a fundamental impact on efficacy during HCC immunotherapy and targeted therapy. Therefore, there is a clinical need to develop a signature that categorizes HCC patients in order to make more accurate clinical decisions.

Methods

Clinical data and gene expression data of HCC patients were obtained from The Cancer Genome Atlas (TCGA) portal and International Cancer Genome Consortium (ICGC) portal. To identify ferroptosis-related immune-related genes (ferroptosis-related IRGs), Pearson correlation analysis was conducted. The ferroptosis-related IRGs prognostic signature (FIPS) was constructed using Univariate Cox and LASSO Cox algorithms. The predictive effectiveness of FIPS was evaluated using Receiver Operating Characteristic (ROC) curves and survivorship curve. The correlation ship between FIPS and TIME was evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA) and CIBERSORT. The relationship between FIPS and immunotherapy responsiveness was evaluated using immunophenoscore. The expression level of 10 ferroptosis-related IRGs in normal liver tissues and HCC tissues was compared using immunohistochemistry. Finally, we established a nomogram (based on FIPS, TNM stage, and age) for clinical application.

Results

The FIPS was established with ten ferroptosis-related IRGs. The high-FIPS subgroup showed a poor clinical prognosis and an obviously higher proportion of HCC patients with advanced TNM stage, high WHO grade and high alpha fetoprotein(AFP) value. Analysis of TIME indicated that patients in the high-FIPS subgroup may be in immunosuppressed state. Meanwhile, we found that ferroptosis may be inhibited in the high-FIPS subgroup and this subgroup may be impervious to immunotherapy and sorafenib.

Conclusion

We constructed a novel potential prognostic signature for HCC patients that predicts overall survival, ferroptosis and immune status, sorafenib sensitivity, and immunotherapy responsiveness.