The identification of genetic biomarkers that play a crucial role in cancer survival is challenging, but requires attention. The present study aimed to identify common biomarkers that affect the survival of patients with cancer. For this purpose, The Cancer Genome Atlas datasets of liver, lung, cervical and pancreatic cancers were used to identify differentially expressed genes (DEGs) that have an impact on cancer survival. The STRING database and ComplexHeatmap package were used to develop a protein‑protein interaction network and heatmaps of the DEGs, respectively. The inhibitors of DEGs were identified using CMap software. Molecular docking and molecular dynamics simulations were performed using PyRx and NAMD software. Of note, two common genes [human NDC80 kinetochore complex component (NDC80) and human disks large‑associated protein 5] in liver and lung cancers and five common genes [human minichromosome maintenance complex component (MCM)2, origin recognition complex subunit 1, cell division cycle 45, MCM3 and human DNA topoisomerase II alpha] in cervical and liver cancers were found to significantly affect overall survival. Several inhibitors of these seven common genes were identified and it was found that cytochalasin (NDC80 inhibitor) demonstrated the highest binding affinity. The results of molecular dynamics suggested that cytochalasin B can be used as a therapeutic drug in cancers with a high expression of NDC80. In addition, the present study identified unique biomarkers present in specific types of cancer and these data were validated using the c‑BioPortal database. The c‑BioPortal data analysis demonstrated that the expression of the cytochrome P450 family 2 subfamily C member 9 and acyl‑CoA dehydrogenase short chain genes, together with the American Joint Committee on Cancer Pathological Staging, can be used as prognostic markers of liver cancer recurrence (AUC value=0.71). No such data were obtained for other cancers. On the whole the present study highlights that the selection of DEGs as potential treatment targets should be based on their effects on cancer survival along with tumor stages.

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