Midline 1 interacting protein 1 promotes cancer metastasis through FOS-like 1-mediated matrix metalloproteinase 9 signaling in HCC

Contribution of Matrix Metalloproteinase-2 and Matrix Metalloproteinase-9 to Upper Tract Urothelial Cancer Risk in Taiwan

Matrix metalloproteinase (MMP)-2 and -9, which degrade type IV collagen, are linked to cancer invasion and metastasis. Gene polymorphisms in MMP-2 and MMP-9 can influence their function, impacting cancer development and progression. This study analyzed the association between polymorphisms MMP-2 rs243865 (C-1306T), rs2285053 (C-735T), and MMP-9 rs3918242 (C-1562T) with serum concentrations of these enzymes in upper tract urothelial cancer (UTUC) patients. We conducted a case-control study with 218 UTUC patients and 580 healthy individuals in Taiwan. Genotyping was performed using PCR/RFLP on DNA from blood samples, and MMP-2 and MMP-9 serum levels and mRNA expressions in 30 UTUC patients were measured using ELISA and real-time PCR. Statistical analysis showed that MMP-2 rs2285053 and MMP-9 rs3918242 genotypes were differently distributed between UTUC patients and controls (p = 0.0199 and 0.0020). The MMP-2 rs2285053 TT genotype was associated with higher UTUC risk compared to the CC genotype (OR = 2.20, p = 0.0190). Similarly, MMP-9 rs3918242 CT and TT genotypes were linked to increased UTUC risk (OR = 1.51 and 2.92, p = 0.0272 and 0.0054). In UTUC patients, TT carriers of MMP-2 rs2285053 and MMP-9 rs3918242 showed higher mRNA and protein levels (p < 0.01). These findings suggest that MMP-2 rs2285053 and MMP-9 rs3918242 genotypes are significant markers for UTUC risk and metastasis in Taiwan.

Etiologically Significant microRNAs in Hepatitis B Virus-Induced Hepatocellular Carcinoma

Hepatitis B virus (HBV) infection has been causally linked to hepatocellular carcinoma (HCC) in more than 50% cases. MicroRNAs (miRNAs) play cross-cutting mechanistic roles in the complex interplay between viral pathogenesis, host survival, and clinical outcomes. The present study set out to identify etiologically significant human miRNAs associated with HBV infection in liver-related pathologies leading to HCC. In diverse tissue types, we assembled 573 miRNAs differentially expressed in HBV-associated liver pathologies, HBV infection, fibrosis, cirrhosis, acute on chronic liver failure, and HCC. Importantly, 43 human differentially expressed miRNAs (hDEmiRs) were regulated in serum/plasma and liver tissue of patients with HBV-positive conditions. However, only two hDEmiRs, hsa-miR-21-5p and hsa-miR-143-3p, were regulated across all disease conditions. To shortlist the functional miRNAs in HBV-induced HCC pathogenesis, a reverse bioinformatics analysis was performed using eight GEO datasets and the TCGA database containing the list of differentially regulated mRNAs in HCC. A comparative study using these data with the identified targets of hDEmiRs, a set of unidirectionally regulated hDEmiRs with the potential to modulate mRNAs in HCC, were found. Moreover, our study identified five miRNAs; hsa-miR-98-5p, hsa-miR-193b-3p, hsa-miR-142-5p, hsa-miR-522-5p, and hsa-miR-370-3p targeting PIGC, KNTC1, CSTF2, SLC41A2, and RAB17, respectively, in HCC. These hDEmiRs and their targets could be pivotal in HBV infection and subsequent liver pathologies modulating HCC clinical progression. HBV infection is the largest contributor to HCC, and the present study comprises the first of its kind compendium of hDEmiRs related to HBV-related pathologies.

Screening for immune-related biomarkers associated with myasthenia gravis and dilated cardiomyopathy based on bioinformatics analysis and machine learning

Background

We aim to investigate genes associated with myasthenia gravis (MG), specifically those potentially implicated in the pathogenesis of dilated cardiomyopathy (DCM). Additionally, we seek to identify potential biomarkers for diagnosing myasthenia gravis co-occurring with DCM.

Methods

We obtained two expression profiling datasets related to DCM and MG from the Gene Expression Omnibus (GEO). Subsequently, we conducted differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) on these datasets. The genes exhibiting differential expression common to both DCM and MG were employed for protein-protein interaction (PPI), Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Additionally, machine learning techniques were employed to identify potential biomarkers and develop a diagnostic nomogram for predicting MG-associated DCM. Subsequently, the machine learning results underwent validation using an external dataset. Finally, gene set enrichment analysis (GSEA) and machine algorithm analysis were conducted on pivotal model genes to further elucidate their potential mechanisms in MG-associated DCM.

Results

In our analysis of both DCM and MG datasets, we identified 2641 critical module genes and 11 differentially expressed genes shared between the two conditions. Enrichment analysis disclosed that these 11 genes primarily pertain to inflammation and immune regulation. Connectivity map (CMAP) analysis pinpointed SB-216763 as a potential drug for DCM treatment. The results from machine learning indicated the substantial diagnostic value of midline 1 interacting protein1 (MID1IP1) and PI3K-interacting protein 1 (PIK3IP1) in MG-associated DCM. These two hub genes were chosen as candidate biomarkers and employed to formulate a diagnostic nomogram with optimal diagnostic performance through machine learning. Simultaneously, single-gene GSEA results and immune cell infiltration analysis unveiled immune dysregulation in both DCM and MG, with MID1IP1 and PIK3IP1 showing significant associations with invasive immune cells.

Conclusion

We have elucidated the inflammatory and immune pathways associated with MG-related DCM and formulated a diagnostic nomogram for DCM utilizing MID1IP1/PIK3IP1. This contribution offers novel insights for prospective diagnostic approaches and therapeutic interventions in the context of MG coexisting with DCM.

Reciprocal interactions between malignant cells and macrophages enhance cancer stemness and M2 polarization in HBV-associated hepatocellular carcinoma

Background: The tumor microenvironment of cancers has emerged as a crucial component in regulating cancer stemness and plays a pivotal role in cell-cell communication. However, the specific mechanisms underlying these phenomena remain poorly understood. Methods: We performed the single-cell RNA sequencing (scRNA-seq) on nine HBV-associated hepatocellular carcinoma (HCC) patients. The heterogeneity of the malignant cells in pathway functions, transcription factors (TFs) regulation, overall survival, stemness, as well as ligand-receptor-based intercellular communication with macrophages were characterized. The aggressive and stemness feature for the target tumor subclone was validated by the conduction of in vitro assays including sphere formation, proliferation, Annexin V apoptosis, flow cytometry, siRNA library screening assays, and multiple in vivo preclinical mouse models including mouse hepatoma cell and human HCC cell xenograft models with subcutaneous or orthotopic injection. Results: Our analysis yielded a comprehensive atlas of 31,664 cells, revealing a diverse array of malignant cell subpopulations. Notably, we identified a stemness-related subclone of HCC cells with concurrent upregulation of CD24, CD47, and ICAM1 expression that correlated with poorer overall survival. Functional characterization both in vitro and in vivo validated S100A11 as one of the top downstream mediators for tumor initiation and stemness maintenance of this subclone. Further investigation of cell-cell communication within the tumor microenvironment revealed a propensity for bi-directional crosstalk between this stemness-related subclone and tumor-associated macrophages (TAMs). Co-culture study showed that this interaction resulted in the maintenance of the expression of cancer stem cell markers and driving M2-like TAM polarization towards a pro-tumorigenic niche. We also consolidated an inverse relationship between the proportions of TAMs and tumor-infiltrating T cells. Conclusions: Our study highlighted the critical role of stemness-related cancer cell populations in driving an immunosuppressive tumor microenvironment and identified the S100A11 gene as a key mediator for stemness maintenance in HCC. Moreover, our study provides support that the maintenance of cancer stemness is more attributed to M2 polarization than the recruitment of the TAMs.