LAPTM4B-YAP loop feedback amplification enhances the stemness of hepatocellular carcinoma

LAPTM4B enhances the stemness of CD133+ liver cancer stem-like cells via WNT/β-catenin signaling

Background & Aims

Lysosome-associated protein transmembrane 4β (LAPTM4B) is an oncogene implicated in the malignant progression of hepatocellular carcinoma (HCC). Previous research established a strong association between LAPTM4B and HCC stemness. However, specific mechanisms by which LAPTM4B regulates and maintains the stemness of liver cancer stem cells remain unclear. Therefore, we investigated the effects of LAPTM4B on the stemness regulation of cluster of differentiation 133 (CD133)+ liver cancer stem-like cells (CSLCs).

Methods

We used RNA interference and overexpression techniques in both in vitro and in vivo models. The involvement of LAPTM4B in wingless/integrated (WNT)/β-catenin signaling was examined through western blotting, immunofluorescence, and immunoprecipitation. The impact of LAPTM4B on β-catenin phosphorylation and ubiquitination was analyzed to elucidate its role in promoting stemness. Clinical relevance was evaluated in an in-house cohort of 105 specimens from patients with HCC through immunohistochemical and microarray analysis, enabling investigation of correlations with clinical outcomes.

Results

LAPTM4B promoted the self-renewal ability, chemoresistance, and tumorigenicity of CD133+ CSLCs. Mechanistically, aberrant LAPTM4B upregulation facilitated β-catenin nuclear translocation (nucleocytoplasmic separation assay, p <0.001) and inhibited its phosphorylation (p <0.01). In addition, LAPTM4B interacts with the deubiquitinating enzymes ubiquitin carboxyl-terminal hydrolase (USP)-1 and USP14, reducing β-catenin ubiquitination. Furthermore, patients with high LAPTM4B and β-catenin expression had markedly shorter 3-year overall survival rate (42.9% vs. 74.4%; hazard ratio, 5.174; 95% CI 2.280-11.741, p <0.001).

Conclusions

LAPTM4B promotes CD133+ CSLC stemness by activating WNT/β-catenin signaling by inhibiting β-catenin phosphorylation and ubiquitination degradation. The role of LAPTM4B in regulating WNT/β-catenin signaling suggests that LAPTM4B serves as a therapeutic target for impairing HCC stemness and progression.

Impact and implications

LAPTM4B contributes significantly to CD133+ CSLC stemness and inhibits β-catenin phosphorylation and ubiquitination degradation, activating WNT/β-catenin signaling. WNT inhibitors suppress LAPTM4B-induced CD133+ CSLC stemness. Thus, targeting the LAPTM4B-WNT/β-catenin axis could improve antitumor efficacy.

A comprehensive prognostic and immune analysis of LAPTM4B in pan-cancer and Philadelphia chromosome-positive acute lymphoblastic leukemia

Introduction

Lysosomal-associated protein transmembrane-4 beta (LAPTM4B) protein expression was increased in solid tumors, whereas few studies were performed in hematologic malignancies. We aimed to study the effect of the LAPTM4B gene in pan-cancer and Philadelphia chromosome-positive acute B cell lymphoblastic leukemia (Ph+ B-ALL).

Methods

The differential expression, diagnosis, prognosis, genetic and epigenetic alterations, tumor microenvironment, stemness, immune infiltration cells, function enrichment, single-cell analysis, and drug response across cancers were conducted based on multiple computational tools. Additionally, Ph+ B-ALL transgenic mouse model with Laptm4b knockout was used to analyze the function of LAPTM4B in vivo. BrdU incorporation method, flow cytometry, and Witte-lock Witte culture were used to evaluate the roles of LAPTM4B in vitro.

Results

We identified that LAPTM4B expression was increased in various cancers, with significant associations with clinical outcomes. LAPTM4B expression correlated with DNA and RNA methylation patterns and was associated with drug resistance. It also influenced the tumor immune microenvironment, with implications for immunotherapy response. In leukemia, LAPTM4B was expressed in stem cells and associated with specific subtypes. Knockout of LAPTM4B impeded B-ALL progression in mice and reduced cell proliferation and caused G0/G1 arrest in vitro.

Discussion

Our study elucidated the role LAPTM4B that promoted the development and progression in Ph+ B-ALL. Furthermore, LAPTM4B played a diagnostic, prognostic, and immunological factor.

Prognostic Risk Models Using Epithelial Cells Identify β-Sitosterol as a Potential Therapeutic Target Against Esophageal Squamous Cell Carcinoma

Background

Esophageal squamous cell carcinoma (ESCC) is an aggressive and fatal malignancy that leads to epithelial cancer. The association between epithelial cell heterogeneity, prognosis, and immune response in this cancer remains uncertain. This study aimed to investigate epithelial cell heterogeneity in ESCC and develop a predictive risk model using the identified cell types.

Methods

Single-cell RNA sequencing (scRNA-seq) and differential ESCC gene data were accessed from the Gene Expression Omnibus. Functional enrichment analysis, inferCNV, cell development trajectories, and intercellular communication were analyzed following epithelial cell characterization. Differentially expressed ESCC (n = 773) and epithelial cell marker genes (n = 3407) were intersected to obtain core genes, and epithelial cell-related prognostic genes were identified. LASSO regression analysis was used to construct a prognostic model. The external dataset GSE53624 was used to further validate the stability of the model. Drug sensitivity predictions, and immune cell infiltration were analyzed. Molecular docking clarified the possible therapeutic role of β-sitosterol in ESCC. Finally, wound healing assay, cell colony, and transwell assay were constructed to detect the effects of the core gene PDLIM2 on ESCC cell proliferation, invasion, and migration.

Results

Eight cell clusters were identified, and epithelial cells were categorized into tumor and paratumor groups. The tumor group possessed more chromosomal variants than the paratumor group. Epithelial cells were associated with multiple cell types and significantly correlated with the Wnt, transforming growth factor, and epidermal growth factor signaling pathways. From 231 intersected genes, five core genes were screened for use in the risk model: CTSL, LAPTM4B, MYO10, NCF2, and PDLIM2. These genes may contribute to the cancerous transformation of normal esophageal epithelial cells and thereby act as biomarkers and potential therapeutic targets in patients with ESCC. β-Sitosterol furthermore displayed excellent docking potential with these genes. Meanwhile, further experiments demonstrated that the gene PDLIM2 plays a major role in the progression of oesophageal squamous carcinoma.

Conclusion

We successfully developed a risk model for the prognosis of ESCC based on epithelial cells that addresses the response of ESCC to immunotherapy and offers novel cancer treatment options.

Identifying the programmed cell death index of hepatocellular carcinoma for prognosis and therapy response improvement by machine learning: a bioinformatics analysis and experimental validation

Background

Despite advancements in hepatocellular carcinoma (HCC) treatments, the prognosis for patients remains suboptimal. Cumulative evidence suggests that programmed cell death (PCD) exerts crucial functions in HCC. PCD-related genes are potential predictors for prognosis and therapeutic responses.

Methods

A systematic analysis of 14 PCD modes was conducted to determine the correlation between PCD and HCC. A novel machine learning-based integrative framework was utilized to construct the PCD Index (PCDI) for prognosis and therapeutic response prediction. A comprehensive analysis of PCDI genes was performed, leveraging data including single-cell sequencing and proteomics. GBA was selected, and its functions were investigated in HCC cell lines by in vitro experiments.

Results

Two PCD clusters with different clinical and biological characteristics were identified in HCC. With the computational framework, the PCDI was constructed, demonstrating superior prognostic predictive efficacy and surpassing previously published prognostic models. An efficient clinical nomogram based on PCDI and clinicopathological factors was then developed. PCDI was intimately associated with immunological attributes, and PCDI could efficaciously predict immunotherapy response. Additionally, the PCDI could predict the chemotherapy sensitivity of HCC patients. A multilevel panorama of PCDI genes confirmed its stability and credibility. Finally, the knockdown of GBA could suppress both the proliferative and invasive capacities of HCC cells.

Conclusion

This study systematically elucidated the association between PCD and HCC. A robust PCDI was constructed for prognosis and therapy response prediction, which would facilitate clinical management and personalized therapy for HCC.