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Background: Anoctamin 1 (ANO1) has been observed to be overexpressed in gastrointestinal and pulmonary epithelial cells, as well as in a number of cancers. Although Ano1 is involved in the prognosis of colorectal cancer (CRC), its mechanism of action in metastatic CRC has not been fully elucidated. Methods: The expression of Ano1 was assessed in samples obtained from The Cancer Genome Atlas (TCGA) database. Then, we used Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene set enrichment analysis (GSEA), Gene set variation analysis (GSVA), and Weighted Correlation Network Analysis (WGCNA) to determine the functions of Ano1. Additionally, random survival forest, Cox multivariate analysis, Kaplan Meier analysis, and ROC were used to determine the predictive value of Ano1 on clinical outcomes in CRC patients. Finally, HE staining, immunohistochemical (IHC) analysis and qRT-PCR were used to explore the expression of the Ano1 gene in CRC tissue. Results:The expression level of Ano1 in CRC was significantly elevated, and the prognosis was poor. The modules with a higher proportion of upregulated genes tended to be positively correlated with Ano1-high. KNG1, GNG4, F2, POSTN, THBS2, SPP1 and FGA were identified as hub proteins of the PPI network. The heatmap showed that the expression level of the Ano1-high group was significantly negatively correlated with immune infiltrate. The overexpression of the Ano1 gene in CRC tissue samples was also confirmed by HE staining, immunohistochemical (IHC) analysis and qRT-PCR. Conclusion: High expression of Ano1 is closely related to a poor prognosis in patients with colorectal cancer. Ano1 may participate in the metastasis and progression, as well as the immune regulation of CRC. In summary, Ano1 can act as a potential prognostic biomarker and a novel target for CRC therapy.

Background Hepatocellular carcinoma (HCC) is a primary malignant tumor of the liver, and pyroptosis has been identified as a novel cellular program that plays a role in numerous diseases including cancer. However, the functional role of pyroptosis in HCC remains unclear. The purpose of this study is to explore the relationship between the two found hub genes and provide targets for clinical treatment. Methods The Cancer Genome Atlas (TCGA) database was used to collect the gene data and clinically-related information of patients with HCC. After the differentially expressed genes (DEGs) were identified, they were intersected with the genes related to pyroptosis, and a risk prediction model was established to predict the overall survival (OS). Subsequently, drug sensitivity analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) was used to analyze the biological characteristics of the DEGs. Different immune cell infiltration and related pathways were analyzed, and hub genes were identified by protein-protein interaction (PPI). Finally, the expression of hub genes was verified by real-time quantitative PCR (qRT-PCR) and immunohistochemistry. Results We conducted a comprehensive bioinformatics analysis to investigate the molecular mechanisms of pyroptosis in hepatocellular carcinoma (HCC). A total of 8,958 differentially expressed genes were identified, and 37 differentially expressed genes were associated with pyroptosis through intersection. Moreover, we developed an OS model with excellent predictive ability and discovered the differences in biological function, drug sensitivity, and immune microenvironment between high-risk and low-risk groups. Through enrichment analysis, we found that the differentially expressed genes are related to various biological processes. Then, 10 hub genes were identified from protein-protein interaction networks. Finally, midkine (MDK) was screened from the 10 hub genes and further verified by PCR and immunohistochemistry, which revealed its high expression in HCC. Conclusion We have developed a reliable and consistent predictive model based on the identification of potential hub genes, which can be used to accurately forecast the prognosis of patients, thus providing direction for further clinical research and treatment.