BACKGROUND

Pancreatic cancer (PC) is a highly malignant tumor featured with high intra-tumoral heterogeneity and poor prognosis. Cell-in-cell (CIC) structures have been reported in multiple cancers, and their presence is associated with disease progression. Nonetheless, the prognostic values and biological functions of CIC-related genes in PC remain poorly understood.

METHODS

The sequencing data, as well as corresponding clinicopathological information of PC were collected from public databases. Random forest screening, least absolute shrinkage, and selection operator (LASSO) regression and multivariate Cox regression analysis were performed to construct a prognostic model. The effectiveness and robustness of the model were evaluated using receiver operating characteristic (ROC) curves, survival analysis and establishing the nomogram model. Functional enrichment analyses were conducted to annotate the biological functions. The immune infiltration levels were evaluated by ESTIMATE and CIBERSORT algorithms. The expression of KRT7 (Keratin 7) was validated by quantitative real-time PCR (qRT-PCR), western blotting and immunohistochemistry (IHC) staining. The CIC formation, cell clusters, cell proliferation, migration and invasion assays were applied to investigate the effects of silencing the expression of KRT7.

RESULTS

A prognostic model based on four CIC-related genes was constructed to stratify the patients into the low- and high-risk subgroups. The high-risk group had a poorer prognosis, higher tumor mutation burden and lower immune cell infiltration than the low-risk group. Functional enrichment analyses showed that numerous terms and pathways associated with invasion and metastasis were enriched in the high-risk group. KRT7, as the most paramount risk gene in the prognostic model, was significantly associated with a worse prognosis of PC in TCGA dataset and our own cohort. High expression of KRT7 might be responsible for the immunosuppression in the PC microenvironment. KRT7 knockdown was significantly suppressed the abilities of CIC formation, cell cluster, cell proliferation, migration, and invasion in PC cell lines.

CONCLUSIONS

Our prognostic model based on four CIC-related genes has a significant potential in predicting the prognosis and immune microenvironment of PC, which indicates that targeting CIC processes could be a therapeutic option with great interests. Further studies are needed to reveal the underlying molecular mechanisms and biological implications of CIC phenomenon and related genes in PC progression.