System analysis based on the cancer-immunity cycle identifies ZNF207 as a novel immunotherapy target for hepatocellular carcinoma.

Background: Immune checkpoint inhibitors as monotherapies for advanced hepatocellular carcinoma (HCC) fail to achieve satisfying results, while combination therapies show greater efficacy. Therefore, identifying new combined targets for immune checkpoint inhibitors could be promising.

Methods: We combined the cancer-immunity cycle score with weighted gene coexpression network and system analyses to screen immunosuppressive targets in HCC.

Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning Study.

Currently, strategies to diagnose patients and predict neurological recovery in cervical spondylotic myelopathy (CSM) using MR images of the cervical spine are urgently required. In light of this, this study aimed at exploring potential preoperative brain biomarkers that can be used to diagnose and predict neurological recovery in CSM patients using functional connectivity (FC) analysis of a resting-state functional MRI (rs-fMRI) data. Two independent datasets, including total of 53 patients with CSM and 47 age- and sex-matched healthy controls (HCs), underwent the preoperative rs-fMRI procedure.

Hypoxia-induced myeloid derived growth factor promotes hepatocellular carcinoma progression through remodeling tumor microenvironment.

Exploring and studying the novel target of hepatocellular carcinoma (HCC) has been extremely important for its treatment. The principal objective of this project is to investigate whether myeloid derived growth factor (MYDGF) could accelerate the progression of HCC, and how it works. Cell proliferation, clonal formation, sphere formation and xenograft tumor experiments were used to prove the critical role of MYDGF in HCC progression.