A prognostic model based on the COL1A1-network in gastric cancer

Gastric cancer (GC) is one of the most common malignancies worldwide with a poor prognosis due to the lack of early detection and effective treatments. As a biomarker, collagen type I alpha 1 (COL1A1) is often dysregulated in some cancer types. However, the expression profile of COL1A1 and functional mechanism in GC is still unclear.

To screen for the different expression genes of GC vs. adjacent tissues, an RNA-seq dataset containing 30 clinical samples and multi-omics datasets of 478 samples were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, respectively. Then the functional enrichment analysis and survival analysis of dysregulated genes were performed. Furthermore, through constructing the protein-protein interactive network, the function mode of COL1A1 was studied. Finally, a prognostic model was built by least absolute shrinkage and selection operator (LASSO) Cox algorithm to assess the clinical value of COL1A1-network.

Firstly, a total of 89 different expression genes (58 down-regulated and 31 up-regulated) that appeared simultaneously in both GEO and TCGA datasets were detected and enriched in some functions regarding the extracellular matrix. However, only 12 genes were significantly correlative with overall survival of GC patients. Among them, ASPN, COL1A1, COL12A1, FNDC1, INHBA and MMP12 could form a network that might activate the epithelial-mesenchymal transition (EMT) pathway. Meanwhile, a prognostic model containing ASPN and INHBA was able to divide GC patients into 2 groups with different risks and predict 5-years survival accurately (AUC = 0.732, 95% CI (0.619, 0.845)).

COL1A1 is up-regulated in GC and may result in a poor prognosis with a higher mRNA level. Moreover, the COL1A1-network may promote malignant metastasis via EMT pathway activation and act as a prognostic marker.