BACKGROUND

As a common cancer, liver cancer imposes an unacceptable burden on patients, but its underlying molecular mechanisms are still not fully understood. Therefore, there is an urgent need to potential biomarkers and diagnostic models for liver cancer.

METHODS

In this study, transcriptome and single-cell datasets related to liver cancer were downloaded from the UCSC Xena database and the Mendeley database, and differential analysis and weighted gene co-expression network analysis were used to find differentially expressed genes related to liver cancer. We used multiple machine algorithms to find hub genes related to liver cancer, and constructed new artificial neural network models based on their transcriptome expression patterns to assist in the diagnosis of liver cancer. Subsequently, we conducted survival analysis and immune infiltration analysis to explore the correlation between hub genes and immune cells, and used single-cell data to verify hub genes related to liver cancer.

RESULTS

This study identified MARCO, KCNN2, NTS, TERT and SFRP4 as central genes associated with liver cancer, and constructed a new artificial neural network model for molecular diagnosis of liver cancer. The diagnostic performance of the training cohort and the validation cohort was good, with the areas under the ROC curves of 1.000 and 0.986, respectively. Immune infiltration analysis determined that these central genes were closely associated with different types of immune cells. The results of immunohistochemistry and the results at the single cell level were consistent with those at the transcriptome level, and also showed obvious differences between different cell types in liver cancer and healthy states.

CONCLUSION

This study identified MARCO, KCNN2, NTS, TERT, and SFRP4 from multiple dimensions and highlighted their key roles in the diagnosis and treatment of liver cancer from multiple dimensions, providing promising biomarkers for the diagnosis of liver cancer.