In vivo CRISPR screen identifies LTN1 as a novel tumor suppressor ubiquitinating insulin-like growth factor 2 mRNA-binding protein 1 in hepatocellular carcinoma.

BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) is a frequent and aggressive kind of cancer. Although E3 ligases play important roles in HCC development, several E3 ligases remain unknown. APPROACH AND RESULTS: Through in vivo CRISPR knockout (KO) screens targeting related E3 ligase genes in HCC nude mice models, we discovered LTN1 as a novel tumor suppressor in HCC. Co-IP paired with 2D-LC-MS/MS and subsequent western blotting in HCC cells were used to identify the interactome of LTN1. Compared to matched normal tissues, the expression of LTN1 was decreased in human HCC tissues (ANT) (157/209). Clinically, patients with HCC who expressed low levels of LTN1 had a poor prognosis. Forced expression of LTN1 decreased cell growth in vitro and in vivo, whereas knockdown of LTN1 increased cell growth. Mechanistically, elevated LTN1 expression inhibited HCC cell growth by ubiquitinating and destabilizing the IGF2BP1 protein, which inhibited the c-Myc and IGF-1R signaling pathways. There was a negative correlation between the LTN1 protein expression and the IGF2BP1 protein expression in HCC tissues (R2=0.2799, P=0.0165). CONCLUSIONS: LTN1 may be a crucial tumor suppressor for determining the prognosis and a possible therapeutic target since it inhibits the proliferation of HCC cells by ubiquitinating IGF2BP1.

Identification of the mitophagy-related diagnostic biomarkers in hepatocellular carcinoma based on machine learning algorithm and construction of prognostic model.

Background and aims: As 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. Methods: In 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). Results: This 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. Conclusion: Mitophagy 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.

Comprehensive analysis of the clinical significance, immune infiltration, and biological role of MARCH ligases in HCC.

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).

MiR-30e inhibits tumor growth and chemoresistance via targeting IRS1 in Breast Cancer.

MicroRNA-30e (miR-30e) is downregulated in various tumor types. However, its mechanism in inhibiting tumor growth of breast cancer remains to be elucidated. In this study, we found that miR-30e was significantly downregulated in tumor tissues of breast cancer (BC) patients and cell lines, and overexpression of miR-30e inhibited cell proliferation, migration and invasion. To understand the potential mechanism of miR-30e in inhibiting tumor growth, we showed that miR-30e blocked the activation of AKT and ERK1/2 pathways, and the expression of HIF-1α and VEGF via directly targeting IRS1. Moreover, miR-30e regulates cell proliferation, migration, invasion and increases chemosensitivity of MDA-MB-231 cells to paclitaxel by inhibiting its target IRS1. MiR-30e also inhibited tumor growth and suppressed expression of IRS1, AKT, ERK1/2 and HIF-1α in mouse xenograft tumors. To test the clinical relevance of these results, we used 40 pairs of BC tissues and adjacent normal tissues, analyzed the levels of miR-30e and IRS1 expression in these tissues, and found that miR-30e levels were significantly inversely correlated with IRS1 levels in these BC tissues, suggesting the important implication of our findings in translational application for BC diagnostics and treatment in the future.