Identification of anoikis related subtypes and construction of prognostic model in hepatocellular carcinoma.

Anoikis resistance, the acquired ability of tumor cells to resist detachment-induced cell death, has been linked to loss of cellular homeostasis, cancer growth, and metastasis. After cancer cells acquire this ability, they can spread to other tissues or organs in the body through the blood circulation system, promoting the distant metastasis of cancer. Therefore, studying the molecular mechanism of anti-anoikis is one of the effective methods for discovering the treatment of human malignant tumors.  This article aims to explore the expression of anoikis genes in hepatocellular carcinoma, identify and characterize the molecular subtypes based on anoikis genes, screen key features, construct a prognostic signature, and explore the treatment response of patients with different risks. This study utilized the TCGA tumor database to calculate differential expression between hepatocellular carcinoma and adjacent tissues using the limma package. A protein interaction network was constructed using the STRING database for gene GO enrichment analysis. Consensus clustering analysis was performed on anoikis apoptotic genes using TCGA tumor sample data as a training set to identify molecular subtypes. Principal component analysis was also performed, along with survival difference analysis on different groups. Additionally, immune cellular infiltration was analyzed using CIBERSORT, XCELL, SSGSEA, and ESTIMATE analysis tools. Finally, univariate cox screening was used to identify prognostic related genes based on differentially expressed genes between subtypes.Based on the analysis of prognostic genes, we utilized the LASSO cox algorithm to eliminate redundant genes and construct a prognostic model using characteristic genes. The prognostic performance was evaluated through training and verification sets, and patients were classified into high and low risk groups based on the median score of the model. Survival differences were then compared between these groups. Univariate and multivariate cox analyses were conducted to confirm that the signature genes were independent prognostic factors. Lastly, relevant molecular responses and potential drug treatment effects were predicted. Most anoikis genes were broadly dysregulated in the TCGA-LIHC cohort; molecular subtypes were identified using unsupervised clustering, and samples were divided into 2 subtypes with significant prognostic differences between subtypes difference; 13 key prognostic genes were finally screened and a risk scoring model was built. The prognostic model had a higher AUC and had a better predictive effect; drug efficacy prediction had a better curative effect in the low-risk group. In this study, a prognostic model of anoikis-related genes in liver hepatocellular carcinoma was constructed, and the model has good predictive performance.

Sphingomyelin Phodiesterase Acid-Like 3A Promotes Hepatocellular Carcinoma Growth Through the Enhancer of Rudimentary Homolog.

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, with unclear pathogenesis. Sphingomyelin phodiesterase acid-like 3A (SMPDL3A) affects cell differentiation and participates in immune regulation. However, its molecular biological function in HCC has not yet been elucidated. Methods: Data from 180 HCC patients were analyzed the relationship between the expression of SMPDL3A in liver cancer tissues and the prognosis of liver cancer patients. Crispr-Cas9 dual vector lentivirus was used to knock out SMPDL3A in HCC cell lines. The effects of SMPDL3A on cell viability were determined by CCK8 assay, clone formation experiment, cell cycle assay, cell scratch, TUNEL experiment and flow cytometry. Xenograft tumor assays in BALB/c nude mice confirmed that SMPDL3A promoted tumor growth and in vivo. Preliminary exploration of SMPDL3A interacting protein by mass spectrometry analysis and co-immunoprecipitation. Results: This study showed that the expression of SMPDL3A in HCC tissue differed from that in tumor-adjacent tissues. Moreover, the overall survival rate and tumor-free survival rate of patients with high-SMPDL3A expression were significantly lower than those with low-SMPDL3A expression. SMPDL3A expression was closely related to the level of protein induced by PIVKA-II, liver cirrhosis, tumor diameter, microvascular invasion, and Barcelona clinic liver cancer staging. Thus, SMPDL3A is an independent risk factor that affects the tumor-free survival rate and overall survival rate of HCC patients. In vitro study using Crispr-Cas9 genome editing technology revealed the knockout effect of SMPDL3A on cell proliferation, apoptosis, and migration. Cell counting kit-8 assay and clone formation experiment showed that sgSMPDL3A inhibited tumor cell proliferation and migration. Flow cytometry and TUNEL assay showed that sgSMPDL3A promoted apoptosis in tumors. Moreover, sgSMPDL3A inhibited tumor growth during subcutaneous tumor formation in nude mice. Immunohistochemistry of Ki67 and PNCA also indicated that sgSMPDL3A inhibited subcutaneous tumor proliferation in tumor-bearing nude mice. Further experiments showed that SMPDL3A interacts with the enhancer of rudimentary homolog (ERH). Conclusions: High-SMPDL3A expression was related to poor prognosis of patients with HCC. Knockout of SMPDL3A inhibited the proliferation and migration and accelerated the migration of HCC cells. SMPDL3A interacted with ERH to affect the tumorigenesis and progression of HCC.

CLEC1B is a Promising Prognostic Biomarker and Correlated with Immune Infiltration in Hepatocellular Carcinoma.

Purpose: C-type lectin domain family 1 member B (CLEC1B) is a protein-coding gene involved in various processes, such as platelet activation, tumor cell metastasis and separation of blood/lymphatic vessels. However, how CLEC1B plays its role in hepatocellular carcinoma (HCC) has not been well studied. The purpose of this study was to investigate the clinical significance and biological function of CLEC1B in HCC. Patients and Methods: Based on (The Cancer Genome Atlas) TCGA database, CLEC1B expression matrix and corresponding clinical information were extracted. ROC curves and Kaplan-Meier method were generated to evaluate the value of CLEC1B as a diagnostic and prognostic biomarker. Moreover, single-gene difference analysis constructed by DESeq2 method and then the related genes were used to predict CLEC1B-related signaling pathways. The ssGSEA algorithm was conducted for studies related to immune infiltration. CLEC1B protein expression was evaluated and immunohistochemistry in HCC tissues through tissue microarray. Finally, the relationship between CLEC1B expression and T cell infiltration was assessed according to tissue microarray. Results: The mRNA and protein levels of CLEC1B were significantly down-regulated in HCC compared to paired normal tissues, which were further verified in clinical tissue samples. ROC curves and Kaplan-Meier survival analysis suggested the significant diagnostic and clinical prognostic value of CLEC1B. Meanwhile, downregulation of CLEC1B was significantly associated with clinical parameters such as clinical tumor vascular invasion and distant metastasis. Moreover, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment (GSEA) analysis indicated that CLEC1B has significant association with immune function. Finally, immune infiltration analysis indicated that CLEC1B was significantly associated with immune cell subsets and affected the efficacy of immunotherapy in cancer patient. Conclusion: Collectively, our findings suggested that CLEC1B could be a promising prognostic biomarker in HCC and its expression was related to immune cell infiltration.