RMI2 is a prognostic biomarker and promotes tumor growth in hepatocellular carcinoma

Glabridin inhibits proliferation and migration in hepatocellular carcinoma by regulating multi-targets

ETHNOPHARMACOLOGICAL RELEVANCE

Glycyrrhiza uralensis Fisch. (GC) is widely utilized in traditional Chinese medicine (TCM) for its properties in Qi tonification, heat clearing, and detoxification. Within TCM theory, Qi is also implicated in tumor development. Numerous TCM formulas containing GC are used for their anti-tumor effects, and contemporary pharmacological research has demonstrated that ethyl acetate extracts (EAe) of GC, along with potential bioactive compounds like glabridin (Gla), possess anti-tumor properties. Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and a major challenge to global healthcare, with high incidence and poor prognosis. Nevertheless, the effects and mechanisms of action of Gla in inhibiting HCC have not been extensively studied.

AIM OF STUDY

This study aims to elucidate the effects and mechanisms of action of Gla against HCC by in vitro and in vivo experiments.

METHODS

The inhibitory effects of ethyl acetate extract (EAe) of GC and its bioactive compounds on HCC were studied using a drug-cell interaction system equipped with UPLC-MS/MS and high-throughput screening methods in vitro. RNA sequencing (RNA-seq) and bioinformatics technologies were employed to detect the differentially expressed genes (DEGs) and pathways in HepG2 cells. The findings were further validated using quantitative real-time PCR (qPCR) and Western blot (WB) assays. Additionally, an in vivo tumor-bearing mouse model established with H22 cells was utilized to examine alterations in tumor tissues via hematoxylin-eosin (HE) staining. Immunohistochemistry was used to assess the protein expression levels of hub targets within each group.

RESULTS

Both in vitro and in vivo experiments demonstrated the effects of EAe against HCC, identifying Gla was one of its main bioactive compounds. Integration of RNA-seq data with clinical databases revealed that Gla inhibited HCC by up-regulating the expression levels of DUSP5, ZFP36, KLF10, and NR4A1, while down-regulating RMI2 expression. These findings were further validated by Gene Expression Omnibus (GEO), qPCR, WB and immunohistochemistry assays.

CONCLUSIONS

Gla regulates the expression levels of DUSP5, ZFP36, KLF10, NR4A1, and RMI2 to against HCC, providing valuable insights for the application of Gla in HCC treatment.

RMI2 is a novel prognostic and predictive biomarker for breast cancer

BACKGROUND

RecQ-mediated genome instability 2 (RMI2) maintains genome stability by promoting DNA damage repair. It has been reported to accelerate the progression of several tumors. However, the functional mechanism of RMI2 in breast cancer remains unclear.

METHODS

Gene expression profiles were obtained from TCGA, GTEx, and GEO databases. The expression of RMI2 and its prognostic value in breast cancer was explored. In addition, we calculated pooled standardized mean deviation (SMD) and performed a summary receiver operating characteristic (sROC) curve analysis to further determine RMI2 expression status and diagnostic significance. The functions and related signaling pathways were investigated based on GO and KEGG analyses. The PPI network was constructed by combining the STRING database and Cytoscape software. Subsequently, in vitro assays were conducted to detect the effect of RMI2 on the proliferation and migration of breast cancer cells.

RESULTS

The expression of RMI2 was markedly upregulated in breast cancer tissues relative to that in normal tissues. Moreover, pooled SMD further confirmed the overexpression of RMI2 in breast cancer (SMD = 1.29, 95% confidence interval (CI): 1.18-1.41, p = 0.000). The sROC curve analysis result suggested that RMI2 had a relatively high diagnostic ability in breast cancer (AUC = 0.87, 95% CI: 0.84-0.90). High RMI2 expression was associated with poor prognosis. GO and KEGG analyses revealed that RMI2 was closely related to cell adhesion, various enzyme activities, and PI3K/AKT signaling pathway. PPI analysis showed that RMI2 had interactions with proteins involved in DNA damage repair. knockdown of RMI2 remarkably inhibited the proliferation and migration of breast cancer cells, while overexpression of RMI2 exerted the opposite effects. Furthermore, we identified that RMI2 accelerates the proliferation and migration of breast cancer cells via activation of the PI3K/AKT pathway.

CONCLUSION

The results suggest that RMI2 is a potential diagnostic and prognostic biomarker associated with cell proliferation and migration, and may be used as a novel therapeutic target for breast cancer in the future.

A new machine learning method for cancer mutation analysis

It is complicated to identify cancer-causing mutations. The recurrence of a mutation in patients remains one of the most reliable features of mutation driver status. However, some mutations are more likely to happen than others for various reasons. Different sequencing analysis has revealed that cancer driver genes operate across complex pathways and networks, with mutations often arising in a mutually exclusive pattern. Genes with low-frequency mutations are understudied as cancer-related genes, especially in the context of networks. Here we propose a machine learning method to study the functionality of mutually exclusive genes in the networks derived from mutation associations, gene-gene interactions, and graph clustering. These networks have indicated critical biological components in the essential pathways, especially those mutated at low frequency. Studying the network and not just the impact of a single gene significantly increases the statistical power of clinical analysis. The proposed method identified important driver genes with different frequencies. We studied the function and the associated pathways in which the candidate driver genes participate. By introducing lower-frequency genes, we recognized less studied cancer-related pathways. We also proposed a novel clustering method to specify driver modules. We evaluated each driver module with different criteria, including the terms of biological processes and the number of simultaneous mutations in each cancer. Materials and implementations are available at: https://github.com/MahnazHabibi/MutationAnalysis.

RecQ mediated genome instability 2 (RMI2): a potential prognostic and immunological biomarker for pan-cancers

Background: RecQ mediated genome instability 2 (RMI2) is an essential component of the BLM-TopoIIIa-RMI1-RMI2 (BTR) complex. However, the mysterious veil of the potential immunological relationship of RMI2 in tumorigenesis and development has not been revealed.

Methods: We conducted the differential expression (DE) analysis of the RMI2 in pan-cancer using data onto Oncomine, TIMER, and GEPIA databases. Afterward, survival analysis and clinical-stage correlation analysis were performed via the TCGA database. Subsequently, we used R software to further explore the relationship between the expression level of RMI2 and tumor mutation burden (TMB), microsatellite instability (MSI), tumor microenvironment (TME), tumor immune-infiltrated cells (TILs), immune checkpoints (ICP), mismatch repairs (MMRs) -related genes, m6A-related genes, DNA methylation-related genes. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional networks were also performed for annotation via gene set enrichment analysis (GSEA).

Results: The RMI2 expressed remarkably high in most cancer types compared to cancer adjacent normal tissues (P < 0.05). High expression of RMI2 was linked to unfavorable prognosis and advanced stage of disease, especially in LIHC and PAAD. RMI2 expression was related to TMB in 16 cancer types and MSI in 8 cancer types. Furthermore, it is significant positive correlations between RMI2 and stromal and immune cells, ICP-related genes, MMRs-related genes, m6A-related genes, and DNA methylation-related genes. Finally, GSEA analysis revealed that RMI2 was engaged in a variety of signaling pathways in pan-cancers.

Conclusions: RMI2 may serve as a potential biological target and probably assume a crucial part in tumorigenesis and progression.

DNA Damage Response Genes in Osteosarcoma

BACKGROUND

Improving the osteosarcoma (OS) patients' survival has long been a challenge, even though the disease's treatment is on the verge of progress. DNA damage response (DDR) has traditionally been associated with carcinogenesis, tumor growth, and genomic instability. No study has used DDR genes as a signature to identify the prognosis of OS. The goal of this work was to find an effective possible DDR gene biomarker for predicting OS prognosis, which may be useful in clinical diagnosis and therapy.

METHODS

To assess gene methylation, univariate and multivariate cox regression analyses were performed on data from OS patients. The data were retrieved from public databases, including the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and the Gene Expression Omnibus (GEO).

RESULTS

The DDR gene signature was chosen, which included seven genes (NHEJ1, RMI2, SWI5, ERCC2, CLK2, POLG, and MLH1). In the TARGET dataset, patients were categorized into two groups: high-risk and low-risk. Patients with a high-risk score revealed a shorter OS rate (hazard ratio (HR): 3.15, 95% confidence interval (CI): 1.38-4.34, P < 0.001) in comparison with the patients with a low-risk score in the TARGET as a training group. The validation of the prognostic signature accuracy was carried out in relapse and validation cohorts (TARGET, n = 75; GSE21257, n = 53). The signature was found to be an independent predictive factor for OS in multivariate cox regression analysis, and a nomogram model was developed to predict an individual's risk of OS. DDR gene signature involved in Fanconi anemia pathway, nonhomologous end-joining pathway, mismatch repair, and nucleotide excision repair pathway.

CONCLUSIONS

Our study suggests that the identified novel DDR genes could be a powerful prognostic tool for prognosis evaluation and a valuable tool in predicting the risk factors in OS patients.