The membrane‐associated RING‐CH (MARCH) family, a member of the E3 ubiquitin ligases, has been confirmed by a growing number of studies to be associated with immune function and has been highlighted as a potential immunotherapy target. In our research, hepatocellular carcinoma (HCC) patients were divided into C1 and C2 MARCH ligase-related patterns by the non-negative matrix factorization (NMF) algorithm. Multiple analyses revealed that the MARCH ligase-related cluster was related to prognosis, clinicopathological characteristics, and the tumor immune microenvironment (TIME). Next, the signature (risk score) of the MARCH prognosis was constructed, including eight genes associated with the MARCH ligase (CYP2C9, G6PD, SLC1A5, SPP1, ANXA10, CDC20, PON1, and FTCD). The risk score showed accuracy and stability. We found that the correlations between risk score and TIME, tumor mutation burden (TMB), prognosis, and clinicopathological characteristics were significant. Additionally, the risk score also had important guiding significance for HCC treatment, including chemotherapy, immunotherapy, and transarterial chemoembolization (TACE).

Background and aimsAs a result of increasing numbers of studies most recently, mitophagy plays a vital function in the genesis of cancer. However, research on the predictive potential and clinical importance of mitophagy-related genes (MRGs) in hepatocellular carcinoma (HCC) is currently lacking. This study aimed to uncover and analyze the mitophagy-related diagnostic biomarkers in HCC using machine learning (ML), as well as to investigate its biological role, immune infiltration, and clinical significance.MethodsIn our research, by using Least absolute shrinkage and selection operator (LASSO) regression and support vector machine- (SVM-) recursive feature elimination (RFE) algorithm, six mitophagy genes (ATG12, CSNK2B, MTERF3, TOMM20, TOMM22, and TOMM40) were identified from twenty-nine mitophagy genes, next, the algorithm of non-negative matrix factorization (NMF) was used to separate the HCC patients into cluster A and B based on the six mitophagy genes. And there was evidence from multi-analysis that cluster A and B were associated with tumor immune microenvironment (TIME), clinicopathological features, and prognosis. After then, based on the DEGs (differentially expressed genes) between cluster A and cluster B, the prognostic model (riskScore) of mitophagy was constructed, including ten mitophagy-related genes (G6PD, KIF20A, SLC1A5, TPX2, ANXA10, TRNP1, ADH4, CYP2C9, CFHR3, and SPP1). ResultsThis study uncovered and analyzed the mitophagy-related diagnostic biomarkers in HCC using machine learning (ML), as well as to investigate its biological role, immune infiltration, and clinical significance. Based on the mitophagy-related diagnostic biomarkers, we constructed a prognostic model(riskScore). Furthermore, we discovered that the riskScore was associated with somatic mutation, TIME, chemotherapy efficacy, TACE and immunotherapy effectiveness in HCC patients.ConclusionMitophagy may play an important role in the development of HCC, and further research on this issue is necessary. Furthermore, the riskScore performed well as a standalone prognostic marker in terms of accuracy and stability. It can provide some guidance for the diagnosis and treatment of HCC patients.

Background: The tripartite motif (TRIM) family are important members of the Gene-finger-containing E3 ubiquitin-conjugating enzyme and are involved in the progression of hepatocellular carcinoma (HCC).Previous studies have largely focused on gene expression and molecular pathways, while the underlying role of the TRIM family in the tumor immune microenvironment (TIME) remains poorly understood. Methods:We systematically explored the correlations of prominent TRIM genes with immune checkpoints and immune infiltrates in 231 HCC samples [International Cancer Genome Consortium (ICGC) cohort (n=231); The Cancer Genome Atlas (TCGA) cohort (n=370)]. A prognostic risk model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis in the ICGC cohort. Kaplan-Meier curves based on the overall survival (OS) were used to assess differences in survival between clusters. We utilized gene set variation analysis (GSVA) to characterize the differences in biological functions. Based on univariate and multivariate Cox progression analysis, we developed a risk score signature and verified its reliability and validity. The Tumor Immune Single-cell Hub (TISCH) single-cell database was employed to evaluate the correlation of TRIM genes with the tumor microenvironment.Results: Cluster 1 was preferentially associated with a favorable prognosis (P<0.001). The amino acid, fatty acid, and drug metabolism pathways were significantly enriched in cluster 2. A prognosis risk score project was established and evaluated based on the 9 independent prognostic genes (all P<0.05). The immune score and stromal scores of patients with low-risk scores were greater than those of patients with high-risk scores (all P<0.001). However, patients with a high-risk score exhibited lower responses to immune checkpoint inhibitors (ICIs), sorafenib, and transarterial chemoembolization (TACE) treatment (all P<0.05).Consistently, TRIM genes showed the same influence in the external TCGA cohort. TRIM gene-based signatures were implicated in TIME and their copy-number alterations dynamically impacted the abundance of tumor-infiltrating immune cells.Conclusions: Our findings revealed that MID1, TRIM5, TRIM22, TRIM28, TRIM 31, TRIM37, TRIM38, TRIM47, and TRIM74 could serve as efficient prognostic biomarkers and therapeutic targets in HCC. The identified TRIM gene-based signatures could serve as important TIME mediators in HCC, potentially increasing immune treatment efficacy.