Somatostatin, Cortistatin and Their Receptors Exert Antitumor Actions in Androgen-Independent Prostate Cancer Cells: Critical Role of Endogenous Cortistatin

Explainable cancer factors discovery: Shapley additive explanation for machine learning models demonstrates the best practices in the case of pancreatic cancer

Pancreatic cancer is one of digestive tract cancers with high mortality rate. Despite the wide range of available treatments and improvements in surgery, chemotherapy, and radiation therapy, the five-year prognosis for individuals diagnosed pancreatic cancer remains poor. There is still research to be done to see if immunotherapy may be used to treat pancreatic cancer. The goals of our research were to comprehend the tumor microenvironment of pancreatic cancer, found a useful biomarker to assess the prognosis of patients, and investigated its biological relevance. In this paper, machine learning methods such as random forest were fused with weighted gene co-expression networks for screening hub immune-related genes (hub-IRGs). LASSO regression model was used to further work. Thus, we got eight hub-IRGs. Based on hub-IRGs, we created a prognosis risk prediction model for PAAD that can stratify accurately and produce a prognostic risk score (IRG_Score) for each patient. In the raw data set and the validation data set, the five-year area under the curve (AUC) for this model was 0.9 and 0.7, respectively. And shapley additive explanation (SHAP) portrayed the importance of prognostic risk prediction influencing factors from a machine learning perspective to obtain the most influential certain gene (or clinical factor). The five most important factors were TRIM67, CORT, PSPN, SCAMP5, RFXAP, all of which are genes. In summary, the eight hub-IRGs had accurate risk prediction performance and biological significance, which was validated in other cancers. The result of SHAP helped to understand the molecular mechanism of pancreatic cancer.

Immunological role and prognostic value of somatostatin receptor family members in colon adenocarcinoma

Colon adenocarcinoma (COAD) is among the most prevalent cancers worldwide, ranking as the third most prevalent malignancy in incidence and mortality. The somatostatin receptor (SSTR) family comprises G-protein-coupled receptors (GPCRs), which couple to inhibitory G proteins (Gi and Go) upon binding to somatostatin (SST) analogs. GPCRs are involved in hormone release, neurotransmission, cell growth inhibition, and cancer suppression. However, their roles in COAD remain unclear. This study used bioinformatics to investigate the expression, prognosis, gene alterations, functional enrichment, and immunoregulatory effects of the SSTR family members in COAD. SSTR1-4 are differentially downregulated in COAD, and low SSTR2 expression indicates poor survival. Biological processes and gene expression enrichment of the SSTR family in COAD were further analyzed using the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology. A strong correlation was observed between SSTR expression and immune cell infiltration. We also quantified SSTR2 expression in 25 COAD samples and adjacent normal tissues using quantitative real-time polymerase chain reaction. We analyzed its correlation with the dendritic cell-integrin subunit alpha X marker gene. The biomarker exploration of the solid tumors portal was used to confirm the correlation between SSTR2 with immunomodulators and immunotherapy responses. Our results identify SSTR2 as a promising target for COAD immunotherapy. Our findings provide new insights into the biological functions of the SSTR family and their implications for the prognosis of COAD.

Aberrations of DNA Repair Pathways in Prostate Cancer-The State of the Art

Prostate cancer (PC) is the second most commonly diagnosed cancer in males worldwide and the fifth most common cause of cancer-related death in men [...].