Identification of Genes Associated with the Metastasis of Pheochromocytoma/Paraganglioma Based on Weighted Gene Coexpression Network Analysis

TOP2A Expression in Pheochromocytoma and Abdominal Paraganglioma: a Marker of Poor Clinical Outcome?

Pheochromocytoma and abdominal paraganglioma (PPGL) are rare neuroendocrine tumors originating from chromaffin cells. Even though only 10-15% of the tumors metastasize, all PPGLs are considered potentially malignant. Topoisomerase 2A (TOP2A) is a protein involved in cell proliferation and has been found to be over-expressed in metastatic PPGL. To provide support whether TOP2A could serve as a prognostic marker, 88 PPGLs (of which 8 metastatic/relapsing) and 10 normal adrenal gland samples were assessed for TOP2A mRNA expression using quantitative real-time PCR (qRT-PCR) and TOP2A immunohistochemistry. Comparisons to clinical parameters connected to metastatic behavior were made, and The Cancer Genome Atlas was used for validation of the results. A significant association between high TOP2A mRNA expression in primary PPGL and subsequent metastatic events (p = 0.008) was found, as well as to specific histological features and clinical parameters connected to metastatic behavior and mutations in SDHB. TOP2A immunoreactivity was calculated as an index of positive nuclei divided by the total amount of nuclei, and this index associated with TOP2A mRNA levels (p = 0.023) as well as the Ki-67 labeling index (p = 0.001). To conclude, TOP2A is a potential prognostic marker as it is frequently elevated in PPGL displaying subsequent metastatic disease, and future studies in larger cohorts are warranted to determine if a TOP2A index as assessed by immunohistochemistry could be a marker of poor outcome. Additionally, elevated levels of TOP2A could indicate a potential actionable event, and future studies with topoisomerase inhibitors would be of interest.

Identification of vital prognostic genes related to tumor microenvironment in pheochromocytoma and paraganglioma based on weighted gene co-expression network analysis

Pheochromocytoma and paraganglioma (PCPG) is a rare neuroendocrine tumor. This study aims to identify vital prognostic genes which were associated with PCPG tumor microenvironment (TME). We downloaded transcriptome data of PCPG from TCGA database and calculated the immune scores and stromal scores by using the ESTIMATE algorithm. DEGs related to TMB were then identified. We conducted WGCNA to further extract the TME-related modules. GO, KEGG pathway analysis, and PPI network were performed. Survival analysis was conducted to identify the hub genes associated with the prognosis of PCPG. A total of 150 PCPG samples were included in this study. We obtained 1507 and 2067 DEGs based on immune scores and stromal scores, respectively. WGCNA analysis identified the red module and brown module were correlated with immune sores while the turquoise module and red module were significantly associated with stromal scores. Functional enrichments analysis revealed that 307 TME-related genes were correlated with the inflammation or immune response. Survival analysis showed that three TME-relate genes (ADGRE1, CCL18, and LILRA6) were associated with PCPG prognosis. These three hub genes including ADGRE1, CCL18, and LILRA6 might be involved in the progression of PCPG and could serve as potential biomarkers and novel therapeutic targets.

The Potential of Five Immune-Related Prognostic Genes to Predict Survival and Response to Immune Checkpoint Inhibitors for Soft Tissue Sarcomas Based on Multi-Omic Study

Low response rates to immunotherapy have been reported in soft tissue sarcoma (STS). There are few predictive biomarkers of response, and the tumor immune microenvironment associated with progression and prognosis remains unclear in STS. Gene expression data from the Cancer Genome Atlas were used to identify the immune-related prognostic genes (IRPGs) and construct the immune gene-related prognostic model (IGRPM). The tumor immune microenvironment was characterized to reveal differences between patients with different prognoses. Furthermore, somatic mutation data and DNA methylation data were analyzed to understand the underlying mechanism leading to different prognoses. The IGRPM was constructed using five IRPGs (IFIH1, CTSG, STC2, SECTM1, and BIRC5). Two groups (high- and low-risk patients) were identified based on the risk score. Low-risk patients with higher overall survival time had higher immune scores, more immune cell infiltration (e.g., CD8 T cell and activated natural killer cells), higher expression of immune-stimulating molecules, higher stimulating cytokines and corresponding receptors, higher innate immunity molecules, and stronger antigen-presenting capacity. However, inhibition of immunity was observed in low-risk patients owing to the higher expression of immune checkpoint molecules and inhibiting cytokines. High-risk patients had high tumor mutation burden, which did not significantly influence survival. Gene set enrichment analysis further revealed that pathways of cell cycle and cancers were activated in high-risk patients. DNA methylation analysis indicated that relative high methylation was associated with better overall survival. Finally, the age, mitotic counts, and risk scores were independent prognostic factors for STS. Five IRPGs performed well in risk stratification of patients and are candidate biomarkers for predicting response to immunotherapy. Differences observed through the multi-omic study of patients with different prognoses may reveal the underlying mechanism of the development and progression of STS, and thereby improve treatment.