Unlike Functioning Pituitary Neuroendocrine Tumor, which produces hormones that cause specific symptoms, non-functioning tumors do not produce hormones that cause specific symptoms.
However, non-functioning pituitary tumors can still cause problems by pressing on nearby structures, such as the optic nerves, which can lead to vision problems, or on the pituitary gland itself, which can disrupt hormone production. Depending on the size and location of the tumor, it may require surgical removal or radiation therapy to manage its effects.
The immune infiltration-associated differentially expressed genes (DEGs) were obtained based on high/low immune scores, which were calculated through the ESTIMATE algorithm. The abundance of immune cells was predicted using the ImmuCellAI database. WGCNA was used to construct a coexpression network of immune cell-related genes. Random forest analysis was used to select the candidate genes associated with invasion. The expression of key genes was verified in an external validation set using quantitative real-time polymerase chain reaction (qRT‒PCR).
The immune and invasion-related DEGs were obtained based on the first dataset of NF-PitNEts (n=112). The immune cell-associated modules in NF-PitNEts were calculated by WGCNA. Random forest analysis was performed on 81 common genes intersected by immune-related genes, invasion-related genes, and module genes. Then, 20 of these genes with the highest RF score were selected to construct the invasion and immune-associated classification model. We found that this model had high prediction accuracy for tumor invasion, which had the largest area under the receiver operating characteristic curve (AUC) value in the training dataset from the first dataset (n=78), the self-test dataset from the first dataset (n=34), and the independent test dataset (n=73) (AUC=0.732/0.653/0.619). Functional enrichment analysis revealed that 8 out of the 20 genes were enriched in multiple signaling pathways. Subsequently, the 8-gene (BMP6, CIB2, FABP5, HOMER2, MAML3, NIN, PRKG2 and SIDT2) classification model was constructed and showed good efficiency in the first dataset (AUC=0.671). In addition, the expression levels of these 8 genes were verified by qRT‒PCR.
They identified eight key genes associated with invasion and immunity in NF-PitNEts that may play a fundamental role in the invasive progression and may provide novel potential immunotherapy targets for NF-PitNEts 1).