CTNNB1 Knockout Cell Lysate

The Wingless-type (Wnt) signaling pathway plays an important role in the development and progression of cancer. This study aimed to evaluate the relationship between single nucleotide polymorphisms (SNPs) in the Wnt pathway and the risk of bone metastasis in patients with non-small cell lung cancer (NSCLC). We collected 500 blood samples from patients with NSCLC and genotyped eight SNPs from four core genes (WNT2, AXIN1, CTNNB1 and APC) present within the WNT pathway. Moreover, we assessed the potential relationship of these genes with bone metastasis development. Our results showed that the AC/AA genotype of CTNNB1: rs1880481 was associated with a decreased risk of bone metastasis. Polymorphisms with an HR of < 1 had a cumulative protective impact on the risk of bone metastasis. Furthermore, patients with the AC/AA genotype of CTNNB1: rs1880481 was associated with Karnofsky performance status score, squamous cell carcinoma antigen and Ki-67 proliferation index. Lastly, patients with the AC/AA genotype of CTNNB1: rs1880481 had significantly longer median progression free survival time than those with the CC genotype. In conclusion, SNPs within the Wnt signaling pathway are associated with a decreased risk of bone metastasis, and may be valuable biomarkers for bone metastasis in patients with NSCLC.

We aimed to develop an HCC classification model based on the integrated gene expression and methylation data of methylation-driven genes. Genome, methylome, transcriptome, proteomics and clinical data of 369 HCC patients from The Cancer Genome Atlas Network were retrieved and analyzed. Consensus clustering of the integrated gene expression and methylation data from methylation-driven genes identified 4 HCC subclasses with significant prognosis difference. HS1 was well differentiated with a favorable prognosis. HS2 had high serum a-fetoprotein level that was correlated with its poor outcome. High percentage of CTNNB1 mutations corresponded with its activation in WNT signaling pathway. HS3 was well differentiated with low serum alpha-fetoprotein level and enriched in metabolism signatures, but was barely involved in immune signatures. HS3 also had high percentage of CTNNB1 mutations and therefore enriched in WNT activation signature. HS4 was poorly differentiated with the worst prognosis and enriched in immune-related signatures, but was barely involved in metabolism signatures. Subsequently, a prediction model was developed. The prediction model had high sensitivity and specificity in distributing potential HCC samples into groups identical with the training cohort. In conclusion, this work sheds light on HCC patient prognostication and prediction of response to targeted therapy.