Autonomic Nervous System Development-Related Signature as Novel Predictive Biomarkers for Immunotherapy in Pan-Cancers

Cunen Wu^1,2,3Weiwei Xue^1,2Yuwen Zhuang¹Dayue Darrel Dayue^3,4,5Zhou Zhou^2,3,6Xiaoxiao Wang⁷Zhenfeng Wu⁸Jin-Yong Zhou⁹Xiangkun Huan⁸Ruiping Wang^1*Haibo Cheng^1,2,3*

• ¹Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
• ²First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Liaoning Province, China
• ³Collaborative Innovation Center of Traditional Chinese Medicine in Prevention and Treatment of Tumor, Nanjing, Liaoning Province, China
• ⁴The Academy of Phenomics of TCM, and School of Integrated Medicine, Nanjing University of Chinese Medicine, Nanjing, China
• ⁵Department of Pharmacology, School of Medicine, University of Nevada, Reno, Nevada, United States
• ⁶Oncology Department of Integrated Chinese and Western Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, China
• ⁷Department of GCP Research Center, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
• ⁸Department of Surgical Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
• ⁹Department of Key Laboratory, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China

Background: Immunotherapy has revolutionized the anticancer algorithm. However, new predictive and prognostic biomarkers for selecting more patients with objective and durable responses and improving the accuracy of prognosis are urgently needed to overcome the limitation of its clinical application.The predictive model of immunotherapy was established by profiling 34 scRNA-Seq data sets of pan-cancers and 8 RNA-Seq data sets of immune checkpoint inhibitor (ICI) , and employing 7 machine learning (ML) methods. The paramount significance of vital genes involved in various cancerous and immune characteristics were identified using multiple advanced algorithms. The differentially expressed vital genes were validated by RT-PCR and immumohistochemical (IHC) staining analyses of clinical samples.The analysis of the differentially expressed genes of the scRNA-Seq data sets and their autonomic nervous system development (ANSD) scores revealed 20 genes as a novel gene set of ANSD-related differential signature (ANSDR.Sig). The ANSDR.Sig-based pan-cancer predicting model for prognostic ICI outcomes was established and the one constructed by random forest algorithm was further proven to be the most powerful. The screening of the most important feature genes related to ANSD in the ICI RNA-Seq data sets through 5 ML algorithms identified 18 genes as a gene set of the Hub-ANSDR.Sig. The regulatory network disclosed diversified molecular interactions in Hub-ANSDR.Sig with TFs and miRNAs. Strong correlation between Hub-ANSDR.Sig and immune cell infiltration, MSI, OS in different cancer types, as well as the role of Hub-ANSDR.Sig in dysfunction, TMB, MSI, mutation frequency, immune infiltration, cell chat and developmental trajectories in GC were testified respectively. Aiming for better clinical significance, we discovered differentially expressed genes in GC compared to normal samples, which was also found in immunotherapy sensitive GC tissues in comparison with insensitive ones.Our results provided a novel insight into immunotherapy efficacy forecasting from ANSD related signature to improve clinical strategies and expand potential indications, intending to bring about more accurate prediction models and therapeutic interventions to help more patients to benefit from immunotherapy.