CRABP2 regulates infiltration of cancer-associated fibroblasts and immune response in melanoma

Finding biomarkers for immunotherapy is an urgent issue in cancer treatment.Cellular retinoic acid-binding protein 2(CRABP2)is a controversial factor in the occurrence and development of human tumors.However,there is limited research on the relationship between CRABP2 and immunotherapy response.This study found that negative correlations of CRABP2 and immune checkpoint markers(PD-1,PD-L1,and CTLA-4)were observed in breast invasive carcinoma(BRCA),skin cutaneous melanoma(SKCM),stomach adenocarcinoma(STAD)and testicular germ cell tumors(TGCT).In particular,in SKCM patients who were treated with PD-1 inhibitors,high levels of CRABP2 predicted poor prognosis.Additionally,CRABP2 expression was elevated in cancer-associated fibroblasts(CAFs)at the single-cell level.The expression of CRABP2 was positively correlated with markers of CAFs,such as MFAP5,PDPN,ITGA11,PDGFRα/βand THY1 in SKCM.To validate the tumor-promoting effect of CRABP2 in vivo,SKCM xenograft mice models with CRABP2 overexpression have been constructed.These models showed an increase in tumor weight and volume.Enrichment analysis indicated that CRABP2 may be involved in immunerelated pathways of SKCM,such as extracellular matrix(ECM)receptor interaction and epithelial-mesenchymal transition(EMT).The study suggests that CRABP2 may regulate immunotherapy in SKCM patients by influencing infiltration of CAFs.In conclusion,this study provides new insights into the role of CRABP2 in immunotherapy response.The findings suggest that CRABP2 may be a promising biomarker for PD-1 inhibitors in SKCM patients.Further research is needed to confirm these findings and to explore the clinical implications of CRABP2 in immunotherapy.

Applying artificial intelligence for cancer immunotherapy

Artificial intelligence(AI)is a general term that refers to the use of a machine to imitate intelligent behavior for performing complex tasks with minimal human intervention,such as machine learning;this technology is revolutionizing and reshaping medicine.AI has considerable potential to perfect health-care systems in areas such as diagnostics,risk analysis,health information administration,lifestyle supervision,and virtual health assistance.In terms of immunotherapy,AI has been applied to the prediction of immunotherapy responses based on immune signatures,medical imaging and histological analysis.These features could also be highly useful in the management of cancer immunotherapy given their ever-increasing performance in improving diagnostic accuracy,optimizing treatment planning,predicting outcomes of care and reducing human resource costs.In this review,we present the details of AI and the current progression and state of the art in employing AI for cancer immunotherapy.Furthermore,we discuss the challenges,opportunities and corresponding strategies in applying the technology for widespread clinical deployment.Finally,we summarize the impact of AI on cancer immunotherapy and provide our perspectives about underlying applications of AI in the future.